1,841 results
Search Results
2. Artificial Intelligence Enabled IOT System for Football Identification in a Football Match
- Author
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Singh, Mahesh Kumar, Singh, Akhilesh Kumar, Singh, Pushpendra, Kalpana, Rishi, Om Prkash, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Garg, Deepak, editor, Narayana, V. A., editor, Suganthan, P. N., editor, Anguera, Jaume, editor, Koppula, Vijaya Kumar, editor, and Gupta, Suneet Kumar, editor
- Published
- 2023
- Full Text
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3. COVID-19 Signs Detection in Chest Radiographs Using Convolutional Neural Networks
- Author
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Armoa, Guido Sebastián, Lencina, Nuria Isabel Vega, Eckert, Karina Beatriz, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, and Pesado, Patricia, editor
- Published
- 2023
- Full Text
- View/download PDF
4. Single-camera system for measuring paper deformations based on image analysis
- Author
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Paweł Pełczyński, Włodzimierz Szewczyk, and Maria Bieńkowska
- Subjects
digital image processing ,characteristic point tracking ,paper physical properties ,deformation analysis ,poisson’s ratio ,Technology - Abstract
The article presents a new technique for measuring paper deformation in unidirectional tensile tests, based on recording and analysis of a series of specimen images. The proposed technique differs from the DIC-based deformation measurement in that the cross-correlation of image data has been replaced with linear filtering. For this purpose, a regular grid of markers is printed on the sample. Filtering the image creates local maxima in the places where markers occur. The developed algorithm finds their location with sub-pixel accuracy. Printing a grid of markers on tested paper and use of reference objects visible in the same image as the paper sample, freed from the need to mechanically connect the camera and the universal testing machine and from the necessity to electronically synchronize their work. The obtained deformation distributions and Poisson’s ratios are in accordance with the literature data which confirms the correctness of the developed measurement technique.
- Published
- 2021
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5. Automated Vehicle Number Recognition Scheme Using Neural Networks
- Author
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Anish, T. P., Prathap, P. M. Joe, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Singh, Mayank, editor, Tyagi, Vipin, editor, Gupta, P. K., editor, Flusser, Jan, editor, and Ören, Tuncer, editor
- Published
- 2022
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6. Automatic Extraction of Heat Maps and Goal Instances of a Basketball Game Using Video Processing
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Eberle, Gerónimo, Bourlot, Jimena, Martínez, César, Albornoz, Enrique M., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Pesado, Patricia, editor, and Gil, Gustavo, editor
- Published
- 2022
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7. Digital colorimetric analysis for estimation of iron in water with smartphone-assisted microfluidic paper-based analytical devices.
- Author
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Balasubramanian, Sivasamy, Udayabhanu, Aditya, Kumar, Ponnusamy Senthil, Muthamilselvi, Ponnuchamy, Eswari, Chidhambaram, Vasantavada, Aalekhya, Kanetkar, Shreyas, and Kapoor, Ashish
- Subjects
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IRON , *SMARTPHONES , *DIGITAL images , *DIGITAL image processing , *IMAGE processing , *COLORIMETRIC analysis , *COLORIMETRY - Abstract
Microfluidic paper-based analytical devices are emerging as promising options for on-the-spot detection of chemical contaminants in water. The coupling of these devices with digital imaging technology has attracted immense interest in developing portable sensor applications. However, the potential of digital techniques still remains to be fully explored. In this work, we integrate digital imaging with microfluidic paper-based analytical devices to develop a portable colorimetric assay for iron. The experimental conditions are optimised using image analysis and the effects of imaging equipment and colorimetric analysis methods on the readout of devices are studied using numerical, graphical and statistical techniques. The experimental results are obtained for the estimation of iron contamination in water samples using a colorimetric assay based on iron (III)-thiocyanate reaction. The digital image acquisition approach using smartphone as imaging equipment is adopted for on-the-spot colorimetric data collection. Image processing techniques are employed to estimate the iron concentration in water from digital images. Greyscale colour intensity obtained from a digital image is identified as a readout of the analytical device and employed for quantitative assessment. The limit of detection for iron is estimated to be 0.26 ppm. The microfluidic approach offers good recovery and repeatability with relative standard deviation of 1.4%. The maximum percentage difference for intensities between the image analysis methods is 4.94% and that between the imaging equipment is 9.96%. The gradients of regression lines for pairwise equipment and method outputs with zero intercept range from 0.9931 to 1.0162 with high values of coefficients of determination. The correlation coefficient values for various paired comparisons (>0.99) suggest strong agreement. Statistical test results suggest no significant differences between methods and between imaging equipment (significance level 0.05). The findings suggest the efficacy of digital imaging and colorimetric analysis for paper-based analytical devices. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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8. Fabrication of a paper-based facile and low-cost microfluidic device and digital imaging technique for point-of-need monitoring of hypochlorite.
- Author
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Debnath, Snehasish, Ghosh, Riya, Pragti, Mukhopadhyay, Suman, Baskaran, Kamesh Viswanathan, and Chatterjee, Pabitra B.
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MICROFLUIDIC devices , *DIGITAL technology , *DIGITAL image processing , *DIGITAL cameras , *IMAGING systems , *FLUORESCENCE spectroscopy , *ULTRAVIOLET-visible spectroscopy - Abstract
Lab-on-a-paper-based devices are promising alternatives to the existing arduous techniques for point-of-need monitoring. The present work reports an instant and facile method to produce a microfluidic paper-based analytical device (μPAD). The fabricated μPAD has been used to detect hypochlorite (OCl−) by incorporating newly synthesized chromo-fluorogenic ratiometric probes 1 and 2 into the sample reception zone. The probes showed high selectivity and fast response (<10 s) toward OCl− with an excellent linear relationship in the concentration range of 0–100 μM. The concentration-dependent fluorometric change driven by the reaction of 1@μPAD with OCl− has been monitored using gel-doc imaging systems, which is unprecedented. Digitizing the intensity of the colour solution with different mathematical models of colour has developed a straightforward method for monitoring OCl− without any interference from its competitors. 1@μPAD can detect OCl− at ∼10 times lower than the WHO recommended limit. The detection limit of 1@μPAD via a digital camera-based fluorescence technique was found to be better over digital camera-based cuvette assays. Therefore, 1@μPAD has been successfully utilized to monitor OCl− in actual environmental water samples with portability, ease of use, and sensitivity. The analytical RSD was found to be ≤3% based on fluorimetric detection using 1@μPAD. The chemodosimetric reaction between OCl− and the probe was evidenced by UV-vis and fluorescence spectroscopy, 1H NMR, and ESI-MS. The rapid response time, biocompatibility, low cytotoxicity, 100% aqueous solubility, ratiometric feature, and exclusive OCl− selectivity over other competitive ROS/RNS successfully lead to the application of the probes for bioimaging of exogenous as well as endogenous OCl− in normal cells (HEK293) and cancerous cells (HeLa). [ABSTRACT FROM AUTHOR]
- Published
- 2023
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9. Features of Indian paper currency and its validation.
- Author
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Sarkar, Sonia and Pal, Arun Kiran
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DIGITAL image processing , *HARD currencies , *LIKERT scale , *FUZZY logic - Abstract
In this paper the different salient features of the old and new version of Indian paper currency notes have been studied and compared. The list of security features of Indian currencies have been mentioned and discussed. Various kinds of conventional methods that are used to detect authenticity and to recognize a note has been explained. It has been observed that each technique has its own objective and significance. There are many conventional tools that can be used to recognize different features of a note. To classify different techniques, the detection method comprises of two parts i.e., the first line detection method and the second line detection method. Physical dimensions of a paper note along with its thickness have pointed out a new parameter which is the diagonal of a genuine currency note that has been calculated to obtain a proposed standardization and each value differs from different denominations. The classification of different Indian banknotes giving emphasis on its characteristics, both salient and security features, dimensions and detection methods has been mentioned and briefly explained. For validation of the paper currencies two methods have been discussed emphasizing on fuzzy logic framework by taking into consideration a 3-point likert scale and another validation methodology using digital image processing. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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10. 基于碳点分子印迹纸芯片法测定 金银花绿原酸含量.
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乐 薇, 孔京华, 刘诗诗, and 段凤琪
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CHLOROGENIC acid ,HYDROPHILIC surfaces ,IMPRINTED polymers ,FILTER paper ,DETECTION limit ,MONOMERS ,DIGITAL image processing - Abstract
Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2022
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11. Canadian Association of Radiologists Prostate MRI White Paper.
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Chang, Silvia D., Reinhold, Caroline, Kirkpatrick, Iain D. C., Clarke, Sharon E., Schieda, Nicola, Hurrell, Casey, Cool, Derek W., Tunis, Adam S., Alabousi, Abdullah, Diederichs, Brendan J., and Haider, Masoom A.
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DIGITAL image processing , *MEN'S health , *REPORT writing , *BIOPSY , *MAGNETIC resonance imaging , *RADIOLOGIC technology , *QUALITY assurance , *PROSTATE tumors - Abstract
Prostate cancer is the most common malignancy and the third most common cause of death in Canadian men. In light of evolving diagnostic pathways for prostate cancer and the increased use of MRI, which now includes its use in men prior to biopsy, the Canadian Association of Radiologists established a Prostate MRI Working Group to produce a white paper to provide recommendations on establishing and maintaining a Prostate MRI Programme in the context of the Canadian healthcare system. The recommendations, which are based on available scientific evidence and/or expert consensus, are intended to maintain quality in image acquisition, interpretation, reporting and targeted biopsy to ensure optimal patient care. The paper covers technique, reporting, quality assurance and targeted biopsy considerations and includes appendices detailing suggested reporting templates, quality assessment tools and sample image acquisition protocols relevant to the Canadian healthcare context. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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12. Fault detection system for paper cup machine based on real-time image processing.
- Author
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Aydın, Alaaddin and Güney, Selda
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- *
PROGRAMMABLE controllers , *OBJECT recognition (Computer vision) , *SERVOMECHANISMS , *ARTIFICIAL intelligence , *DEEP learning , *DIGITAL image processing , *PRODUCT image , *IMAGE processing - Abstract
In the production of paper cups in industrial factories, it is tried to print high quality cups with less waste loss with the help of sensors and heating resistances mounted on the paper cup machine. In this study, a system that detects faulty products based on image processing and removes it by controlling the machine with servo motors, asynchronous motors and programmable logic controller (PLC) is designed. For fault product detection, classification has been performed using real-time Haarcascade algorithm and You Only Look Once (YOLO) algorithm which is a deep learning methods, and real-time object detection has been carried out using the OpenCv library. With this study, an effective faulty product detection and removing hardware system was realized by adapting artificial intelligence algorithms to a machine used in industry. Based on the results, a whole system can be applied to systems that involve removing a faulty product from a band in any production, packaging etc. facility is proposed. A hardware consisting of servo motors, asynchronous motors and PLC was designed to separate faulty cups from the existing paper cup production machine in this study. Then, a data set composed of 1068 images was created with images taken from the camera for faulty and faultless paper cups. Using this dataset, the effect of different deep learning methods on performance in the real-time system has been examined and successful results have been obtained. The optimal outcome was achieved, yielding a real-time application accuracy rate of 90.8% through the utilization of the Yolov5x architecture. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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13. 基于X射线断层扫描技术的 涂布纸张三维结构表征.
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王 益, 刘 寅, and 沈文浩
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DIGITAL image processing ,POROSITY ,DRAG (Hydrodynamics) ,ROOT-mean-squares ,SURFACE morphology - Abstract
Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2023
- Full Text
- View/download PDF
14. Investigation of the effect of laser technology on the color of polyester fabrics
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Atav, Rıza and Çolakoğlu, Özge
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- 2024
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15. Rapid and cost-effective detection of perchlorate in water using paper-based analytical devices.
- Author
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Kumar, Praveen, Kapoor, Ashish, and Raghunathan, MuthuKumar
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PERCHLORATE removal (Water purification) ,WATER use ,DIGITAL images ,IMAGE processing ,DIGITAL image processing ,MICROFLUIDIC devices - Abstract
Perchlorate, a hazardous pollutant, is mainly found in untreated wastewater from urban and industrial sites and unregulated surface and groundwater sources. Effective monitoring of perchlorate in water is essential to mitigate its potential harmful effects. Microfluidic systems are evolving as promising technologies for detecting chemical contaminants in water due to their ability to enable rapid analysis with minimal consumption of reagents and samples. The integration of paper-based microfluidic devices with digital imaging has garnered enormous attention from the perspective of developing portable analytical techniques. Nevertheless, there is a need for further exploration to fully realize the potential of these systems. This study aimed to develop and evaluate the performance of a microfluidic paper-based device for measuring perchlorate levels in water samples. Smartphone-based digital imaging was integrated with microfluidic paper-based analytical device to establish a reliable colorimetric method for detecting perchlorate contamination. The results demonstrated successful quantitative estimation of perchlorate levels in water samples using a colorimetric assay based on the methylene blue-perchlorate reaction. Real-time, on-site colorimetric data were collected using a digital smartphone, and image processing methods were used to detect the occurrence of perchlorate in water samples from digital images. The developed approach yielded a broad linear response ranging from 4 to 12 µg/L (R2 = 0.97) for perchlorate detection, with a limit of detection of 3.41 µg/L and a limit of quantification of 10.34 µg/L. The findings underscore the effectiveness of colorimetric analysis and digital imaging for paper-based analytical devices. The limitations of this method include the capability to detect only a single analyte and the requirement for additional steps in image processing to obtain analytical results. Future developments should focus on designing devices for simultaneous detection of multiple contaminants and exploring automated methods of image analysis. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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16. Automatic Segmentation with Deep Learning in Radiotherapy.
- Author
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Isaksson, Lars Johannes, Summers, Paul, Mastroleo, Federico, Marvaso, Giulia, Corrao, Giulia, Vincini, Maria Giulia, Zaffaroni, Mattia, Ceci, Francesco, Petralia, Giuseppe, Orecchia, Roberto, and Jereczek-Fossa, Barbara Alicja
- Subjects
DIGITAL image processing ,DEEP learning ,NATURAL language processing ,ARTIFICIAL intelligence ,AUTOMATION ,RADIOTHERAPY ,ARTIFICIAL neural networks ,ONCOLOGY - Abstract
Simple Summary: Automatic segmentation of organs and other regions of interest is a promising approach for reducing the workload of doctors in radiotherapeutic planning, but it can be hard for doctors and researchers to keep up with current developments. This review evaluates 807 papers and reveals trends, commonalities, and gaps in the existing corpus. A set of recommendations for conducting effective segmentation studies is also provided. This review provides a formal overview of current automatic segmentation studies that use deep learning in radiotherapy. It covers 807 published papers and includes multiple cancer sites, image types (CT/MRI/PET), and segmentation methods. We collect key statistics about the papers to uncover commonalities, trends, and methods, and identify areas where more research might be needed. Moreover, we analyzed the corpus by posing explicit questions aimed at providing high-quality and actionable insights, including: "What should researchers think about when starting a segmentation study?", "How can research practices in medical image segmentation be improved?", "What is missing from the current corpus?", and more. This allowed us to provide practical guidelines on how to conduct a good segmentation study in today's competitive environment that will be useful for future research within the field, regardless of the specific radiotherapeutic subfield. To aid in our analysis, we used the large language model ChatGPT to condense information. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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17. Automatic Examination Paper Scores Calculation and Grades Analysis Based on OpenCV
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Zhang, Xin-Yu, Sun, Zhan-Li, Liu, Mengya, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yu, Shiqi, editor, Zhang, Zhaoxiang, editor, Yuen, Pong C., editor, Han, Junwei, editor, Tan, Tieniu, editor, Guo, Yike, editor, Lai, Jianhuang, editor, and Zhang, Jianguo, editor
- Published
- 2022
- Full Text
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18. Building Crack Detection Based on Digital Image Processing Technology and Multiscale Feature Analysis Automatic Detection Algorithm.
- Author
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Liu, Chenguang
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DIGITAL image processing ,SMART structures ,ENGINEERING personnel ,ELECTRONIC paper ,ALGORITHMS ,CRACKING of concrete - Abstract
At present, the monitoring of concrete cracks is still mainly carried out by engineering personnel using simple mechanical monitoring instruments. The human inspection will undoubtedly be interfered by the individual's psychological, physical, and external conditions, and there may also be unobjective emotions, so it is impossible to ensure that the quality of the detection is up to standard and accurate. This paper combines digital image processing technology and multiscale feature analysis automatic detection algorithm to construct an intelligent building structure crack detection system. Moreover, this paper proposes an enrichment scheme for the unknown partially entangled states of building microparticles and utilizes the entanglement exchange process based on the Raman interaction of two building microparticles. The experimental results show that the automatic detection method of building cracks based on digital image processing technology and multiscale feature analysis has a good effect. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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19. Research and Application of Color of Qiuci Murals Based on Intelligent Digital Image Processing Technology.
- Author
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Kong, Qianqian and Qiao, Yong
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DIGITAL image processing ,DIGITAL technology ,COLOR image processing ,MURAL art ,ART conservation & restoration ,ELECTRONIC paper ,COMPUTER art - Abstract
In order to improve the color research effect of Qiuci murals, this paper combines digital technology, applies intelligent digital image processing technology to the color research of Qiuci murals, and builds an intelligent system. Moreover, this paper analyzes the color mural pixel digitization of murals from the perspective of digitization and conducts research from a microscopic perspective. In addition, in this paper, the expansion operation is performed for particularly small cracks, and the extraction area of the crack is correspondingly increased, which reduces the bad influence on the subsequent repair due to incomplete crack extraction. Finally, this paper constructs an intelligent mural analysis system based on the improved algorithm. The experimental research results show that the color research system of Qiuci murals based on intelligent digital image processing technology proposed in this paper has good effects and can play an important role in the digital protection and digital restoration of art. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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20. Interactive Multimedia Reporting Technical Considerations: HIMSS-SIIM Collaborative White Paper.
- Author
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Berkowitz, Seth J., Kwan, David, Cornish, Toby C., Silver, Elliot L., Thullner, Karen S., Aisen, Alex, Bui, Marilyn M., Clark, Shawn D., Clunie, David A., Eid, Monief, Hartman, Douglas J., Ho, Kinson, Leontiev, Andrei, Luviano, Damien M., O'Toole, Peter E., Parwani, Anil V., Pereira, Nielsen S., Rotemberg, Veronica, Vining, David J., and Gaskin, Cree M.
- Subjects
PUBLIC health laws ,CONSENSUS (Social sciences) ,DIGITAL image processing ,MEDICAL radiology ,MULTIMEDIA systems ,MEDICAL information storage & retrieval systems ,MANAGEMENT of medical records ,DOCUMENTATION ,COMMUNICATION ,MEDICAL informatics ,ELECTRONIC health records ,DIGITAL diagnostic imaging - Abstract
Despite technological advances in the analysis of digital images for medical consultations, many health information systems lack the ability to correlate textual descriptions of image findings linked to the actual images. Images and reports often reside in separate silos in the medical record throughout the process of image viewing, report authoring, and report consumption. Forward-thinking centers and early adopters have created interactive reports with multimedia elements and embedded hyperlinks in reports that connect the narrative text with the related source images and measurements. Most of these solutions rely on proprietary single-vendor systems for viewing and reporting in the absence of any encompassing industry standards to facilitate interoperability with the electronic health record (EHR) and other systems. International standards have enabled the digitization of image acquisition, storage, viewing, and structured reporting. These provide the foundation to discuss enhanced reporting. Lessons learned in the digital transformation of radiology and pathology can serve as a basis for interactive multimedia reporting (IMR) across image-centric medical specialties. This paper describes the standard-based infrastructure and communications to fulfill recently defined clinical requirements through a consensus from an international workgroup of multidisciplinary medical specialists, informaticists, and industry participants. These efforts have led toward the development of an Integrating the Healthcare Enterprise (IHE) profile that will serve as a foundation for interoperable interactive multimedia reporting. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
21. NETWORKED IMAGE.
- Author
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BINSCHTOK, VIKTORIA
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IMAGE processing ,PHOTOGRAPHY ,SOCIAL media ,DIGITAL image processing - Published
- 2022
22. SINCE YOU WERE BORN.
- Author
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ROTH, EVAN
- Subjects
DIGITAL image processing ,SELF-portraits ,IMAGE processing ,PHOTOGRAPHY ,SOCIAL media - Published
- 2022
23. Despeckling by sparse sampling on Bernoulli process in single-shot digital holography.
- Author
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Leng, Junmin, Li, Honglian, and Li, Fubing
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HOLOGRAPHY ,DIGITAL holographic microscopy ,SAMPLING (Process) ,SPECKLE interference ,THREE-dimensional imaging ,ELECTRONIC paper ,IMAGE reconstruction - Abstract
Digital holography is a promising imaging technology. However, there is speckle noise in the reconstructed image of a digital hologram. Speckle degrades the quality of the reconstructed image. Suppression of speckle noise is a challenging problem in digital holography. A novel method is proposed to reduce speckle by a single-shot digital hologram in this paper. In the proposed method, a single-shot digital hologram is obtained by a conventional experiment setup without additional requirements. At the same time, different binary masks are designed and generated on Bernoulli process in a computer. Then the single-shot digital hologram is sampled by these binary masks to generate multiple holographic patterns. Eventually, these holographic patterns are reconstructed to suppress speckle noise in the reconstruction image. Simulation and experiment are made to verify the proposed method. Results show that it is effective and feasible for the proposed method to reduce speckle in the digital holography. The proposed method can be applied into various fields such as three-dimensional imaging, holographic diagnosis, and art display. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
24. Supraspinatus extraction from MRI based on attention-dense spatial pyramid UNet network.
- Author
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Wang, Peng, Liu, Yang, and Zhou, Zhong
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SUPRASPINATUS muscles ,DEEP learning ,DIGITAL image processing ,ROTATOR cuff injuries ,COMPUTER-aided design ,MAGNETIC resonance imaging ,RETROSPECTIVE studies ,DESCRIPTIVE statistics ,RESEARCH funding ,DECISION making in clinical medicine ,COMPUTER-assisted image analysis (Medicine) ,SHOULDER - Abstract
Background: With potential of deep learning in musculoskeletal image interpretation being explored, this paper focuses on the common site of rotator cuff tears, the supraspinatus. It aims to propose and validate a deep learning model to automatically extract the supraspinatus, verifying its superiority through comparison with several classical image segmentation models. Method: Imaging data were retrospectively collected from 60 patients who underwent inpatient treatment for rotator cuff tears at a hospital between March 2021 and May 2023. A dataset of the supraspinatus from MRI was constructed after collecting, filtering, and manually annotating at the pixel level. This paper proposes a novel A-DAsppUnet network that can automatically extract the supraspinatus after training and optimization. The analysis of model performance is based on three evaluation metrics: precision, intersection over union, and Dice coefficient. Results: The experimental results demonstrate that the precision, intersection over union, and Dice coefficients of the proposed model are 99.20%, 83.38%, and 90.94%, respectively. Furthermore, the proposed model exhibited significant advantages over the compared models. Conclusion: The designed model in this paper accurately extracts the supraspinatus from MRI, and the extraction results are complete and continuous with clear boundaries. The feasibility of using deep learning methods for musculoskeletal extraction and assisting in clinical decision-making was verified. This research holds practical significance and application value in the field of utilizing artificial intelligence for assisting medical decision-making. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
25. Frequency roughness analysis in image processing and game design.
- Author
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Li, Jiaqi
- Subjects
IMAGE processing ,IMAGE analysis ,PATTERN recognition systems ,COMPUTER vision ,IMAGE intensifiers ,DIGITAL image processing ,CONCEPT mapping - Abstract
With the continuous progress of science and technology, image processing techniques have been used increasingly in recent years. Image processing plays an indispensable role in the fields of computer vision, artificial intelligence, pattern recognition, and related fields. Improvements in basic algorithms and the development of new algorithms have resulted in considerable innovation and progress. This paper is devoted to finding new game applications in a branch of image processing. It introduces an analysis model proposed by the author and discusses the relationship between roughness in the frequency domain and visual image interpretation. By using the concept of roughness, we separated the image features into meaningful information and residual information and analysed the image in the frequency domain. The results were compared with those of traditional image processing methods. The starting point is the visual identification of a feature based on human interpretation. The image information was separated into meaningful features and the residual component to reduce the redundancy of the model. This allowed for a sparse representation of the feature information in the image. By analysing the meaningful features and residual components of an image separately, we established a relationship between the results and the original images. Parameters such as texture, morphology, and the degree of blurring were considered and we developed a parameter called "frequency roughness". The algorithm incorporates the concepts of frequency and roughness and the roughness is determined in the frequency domain. The frequency roughness algorithm successfully separated the rough features in the frequency domain and calculated the residual value in an image. This model provided more accurate image processing results than comparable methods. This paper includes an analysis and game applications of the proposed model for de-blurring, image enhancement, recognition, and other image processing tasks. Some game applications were successful, whereas others require further investigation. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
26. Service reliability evaluation of highway tunnel based on digital image processing.
- Author
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Dai, Chunquan, Zhou, Zhaochen, Zhang, Huidi, Jiang, Kun, Li, Haisheng, and Yu, Haiyang
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TUNNELS ,DIGITAL image processing ,ROAD construction ,TUNNEL design & construction ,TUNNEL lining ,FRACTAL dimensions - Abstract
China is gradually transitioning from the "tunnel construction exploration era" to the "tunnel high-quality construction and operation era", and the maintenance demand of highway tunnels has increased sharply. Therefore, there is an urgent need for an evaluation method to evaluate the service reliability of highway tunnels, so as to provide reference for tunnel maintenance personnel to carry out maintenance work. Taking highway tunnels as the research object, this paper extracts three parameters, including length, maximum width and fractal dimension, from the binary image of highway tunnel lining cracks. The standard for dividing the length of the highway tunnel section is 500m as the tunnel section, and a section disease sample space including multiple highway tunnels is constructed. The EM clustering algorithm was used to determine the number of graded grades of disease, and the relative Euclidean distance was used as the evaluation index to divide the safety grade of the tunnel into five grades: normal, degraded, inferior, deteriorated and hazardous. The partial least squares method is used to establish the lining service reliability evaluation formula and verify the residual of each sample point in the sample space. The smaller the average value of the residual, the better fitting effect of the established evaluation formula. The service reliability evaluation method proposed in this paper is applied to engineering practice and compared with the expert scoring method and the national standard method, which proves that the evaluation method in this paper has the advantages of strong visibility, simple evaluation method, and is conducive to engineering practice. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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27. A COMPARATIVE EXPLORATION OF ACTIVATION FUNCTIONS FOR IMAGE CLASSIFICATION IN CONVOLUTIONAL NEURAL NETWORKS.
- Author
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MAKHDOOM, FAIZA and RAHMAN, JAMSHAID UL
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ARTIFICIAL neural networks ,DEEP learning ,ARTIFICIAL intelligence ,MACHINE learning ,DIGITAL image processing ,COMPUTER vision - Abstract
Activation functions play a crucial role in enabling neural networks to carry out tasks with increased flexibility by introducing non-linearity. The selection of appropriate activation functions becomes even more crucial, especially in the context of deeper networks where the objective is to learn more intricate patterns. Among various deep learning tools, Convolutional Neural Networks (CNNs) stand out for their exceptional ability to learn complex visual patterns. In practice, ReLu is commonly employed in convolutional layers of CNNs, yet other activation functions like Swish can demonstrate superior training performance while maintaining good testing accuracy on different datasets. This paper presents an optimally refined strategy for deep learning-based image classification tasks by incorporating CNNs with advanced activation functions and an adjustable setting of layers. A thorough analysis has been conducted to support the effectiveness of various activation functions when coupled with the favorable softmax loss, rendering them suitable for ensuring a stable training process. The results obtained on the CIFAR-10 dataset demonstrate the favorability and stability of the adopted strategy throughout the training process. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
28. Comparison and design of energy-efficient approximate multiplier schemes for image processing by CNTFET.
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Tavakkoli, Elmira, Shokri, Shayan, and Aminian, Mahdi
- Subjects
IMAGE processing ,DIGITAL signal processing ,FIELD-effect transistors ,CARBON nanotubes ,COMPUTER systems ,DIGITAL image processing - Abstract
Approximate computing is an emerging paradigm that can tolerate some loss of accuracy to improve the energy consumption and design complexity. Multiplication is one of the important computational components in various digital signal processing (DSP) applications. Improvement of multipliers, as the building blocks of an approximate computing systems, has received significant attention. Compressors are key elements in an approximate multiplier to have an optimal design with high performance. This paper proposes an approximate/exact 4–2 compressor and an exact 5–2 compressor in carbon nanotube field-effect transistors (CNTFET) technology for utilisation in an approximate multiplier. Four efficient designs for utilising the proposed compressors are introduced and analysed for a Wallace multiplier. All of the proposed circuits are analysed in 32 nm CNTFET Stanford technology and are simulated by HSPICE. In addition, as an image processing application of approximate multipliers, this paper compares the quality metrics of the image multiplication for the proposed designs and previously presented approximated multipliers. For example, the proposed schemes provide excellent results in structural similarity index up to 99%, and peak signal-to-noise ratio more than 45 dB. Also, it uses figure of metric (FOM) that includes the accuracy and quality metrics of the approximate multipliers. The simulation results demonstrate the efficiency of proposed designs. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
29. Efficient partial product reduction for image processing application using approximate 4:2 compressor.
- Author
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Kattekola, Naresh, Jawale, Amol, Nath, Pallab Kumar, and Majumdar, Shubhankar
- Subjects
PRODUCT image ,IMAGE processing ,SIGNAL-to-noise ratio ,COMPRESSORS ,DIGITAL image processing - Abstract
Purpose: This paper aims to improve the performance of approximate multiplier in terms of peak signal to noise ratio (PSNR) and quality of the image. Design/methodology/approach: The paper proposes an approximate circuit for 4:2 compressor, which shows a significant amount of improvement in performance metrics than that of the existing designs. This paper also reports a hybrid architecture for the Dadda multiplier, which incorporates proposed 4:2 compressor circuit as a basic building block. Findings: Hybrid Dadda multiplier architecture is used in a median filter for image de-noising application and achieved 20% more PSNR than that of the best available designs. Originality/value: The proposed 4:2 compressor improves the error metrics of a Hybrid Dadda multiplier. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
30. A novel design of N-bit approximate comparator for image processing applications.
- Author
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Kattekola, Naresh and Majumdar, Shubhankar
- Subjects
IMAGE processing ,COMPARATOR circuits ,DIGITAL image processing - Abstract
Purpose: This paper aims to implement a novel design of approximate comparator which can be suitable for image processing applications. Design/methodology/approach: Here, the N-bit approximate comparator is implemented by taking reference of N as 2-, 4- and 8-bit. The design analyses the fractional change in error to bit in several bit formats. The final implementation of approximate comparator design application compares the structural similarity index, colour test and extraction of an image to the results. Findings: The novel approximate comparator was designed using 2-, 4- and 8-bit to explore N-bit comparator expressions. The implementation, computations, evaluation of errors, applications and the design constraints were executed using Python and Synopsys, respectively. The computations, evaluation of errors, applications and the design constraints were executed using Python and Synopsys, respectively. Originality/value: This paper presents the N-bit accurate and approximate comparator which is novel over the existing design of comparators. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
31. Rectification for Stitched Images with Deformable Meshes and Residual Networks.
- Author
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Fan, Yingbo, Mao, Shanjun, Li, Mei, Wu, Zheng, Kang, Jitong, and Li, Ben
- Subjects
MESH networks ,IMAGE enhancement (Imaging systems) ,DIGITAL image processing - Abstract
Image stitching is an important method for digital image processing, which is often prone to the problem of the irregularity of stitched images after stitching. And the traditional image cropping or complementation methods usually lead to a large number of information loss. Therefore, this paper proposes an image rectification method based on deformable mesh and residual network. The method aims to minimize the information loss at the edges of the spliced image and the information loss inside the image. Specifically, the method can select the most suitable mesh shape for residual network regression according to different images. Its loss function includes global loss and local loss, aiming to minimize the loss of image information within the grid and global target. The method in this paper not only greatly reduces the information loss caused by irregular shapes after image stitching, but also adapts to different images with various rigid structures. Meanwhile, its validation on the DIR-D dataset shows that the method outperforms the state-of-the-art methods in image rectification. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
32. A REVIEW PAPER: DIGITAL IMAGE FILTERING PROCESSING.
- Author
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Al-Taie, Rana Riad K., Saleh, Basma Jumaa, and Abu-Alsaad, Hiba A.
- Subjects
DIGITAL image processing ,RANDOM noise theory ,COLOR image processing ,NOISE control ,NONLINEAR analysis - Abstract
Nowadays, visual information is increasingly sent in the form of digital images, making the identification of noisy data a common challenge in many research and application domains. Many noise reduction techniques have been developed in the contemporary era to remove noise while preserving image information. The task of eliminating noise from an original image remains a difficult one for researchers. The primary focus of this study is on picture denoising and filtering. For performing a comparative examination of existing denoising techniques, such as linear and nonlinear filters. salt, pepper noise, Gaussian noise, and speckle noise are examples of different noise models. PSNR of color images provides a quantity measure of comparison. [ABSTRACT FROM AUTHOR]
- Published
- 2021
33. Robust DWT-SVD watermarking algorithm with additional digital signature security for color images.
- Author
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Bhatt, Kaveri, Singh, Kaptan, and Saxena, Amit
- Subjects
DIGITAL watermarking ,COLOR space ,WATERMARKS ,DIGITAL image processing ,DIGITAL signatures ,SECURITY management - Abstract
Watermarking is a technique for verifying and securing digital imaging data. Watermarking of a colour image is a time-consuming and complicated process. A range of transformation-based approaches are used to improve watermark robustness and invisibility. Hence, this paper utilizes the DWT-SVD strategy to introduce a preferable hybrid transformation technique. For upgrading the robustness, paper proposed a hybrid DWT-SVD based transform domain rule including the extra digital signature security. For color reproduction the YCbCr color space model is used at the pre-processing level. Qualitative performance of suggested algorithm is compared with the existing results for validation of work. The quantitative performance is evaluated under the presence of various attacks. Qualitative analysis is based on structural similarity (SSIM), NC, MSE and PSNR justifying efficiency of proposed algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
34. Improved digital image interpolation technique based on multiplicative calculus and Lagrange interpolation.
- Author
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Othman, Gheyath Mustafa, Yurtkan, Kamil, and Özyapıcı, Ali
- Abstract
Digital imaging is used in variety of applications. Together with the improvements in artificial intelligence and its sub-fields, improving computer vision methods to address inter- and multi-disciplinary problems is possible. Especially in medical applications, there are significant improvements related to imaging in the last decades. Digital image interpolation is a key operation in digital image processing where there are no sufficient samples during the acquisition process. Using the available samples in hand, digital interpolation techniques are predicting the missing samples. The paper addresses the problem of digital image interpolation and proposes a novel algorithm using multiplicative calculus. The main contribution of the paper is the application of multiplicative Lagrange interpolation to accomplish image interpolation task. The proposed method is tested on several datasets, and the results are comparable to the state-of-the-art methods. The paper presents encouraging results to the literature, and the proposed method is open for further improvements. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
35. A novel material fabrication to detect breast cancer using deep novel classification mechanism.
- Author
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Venkatesh, J., Titus, Anita, Janarthanan, R., Anitha, C., ChirraKesavaReddy, and Ranjith, S.
- Subjects
BREAST cancer ,COMPUTER-assisted image analysis (Medicine) ,DIGITAL image processing ,CONVOLUTIONAL neural networks ,IMAGE processing ,LOGICAL prediction - Abstract
Now-a-days, Breast Cancer is a serious cause in world and it raises a death ratio in drastic manner. Most of the women are suffered due to this kind of breast cancer disease, in which several literatures are available regarding this breast cancer identification, but all are strucked over certain level. Lots of researchers in industry providing different digital image processing schemes to identify the Breast Cancer in clear manner to save lives of women but all are strucked with real world prediction scenarios. For Breast Cancer identification mammography is a well-known technique, in which it screens the breast cancer cells in clear manner. However, based on the workload and manual expertise failures, a numerous failures and wrong classification procedures reported as well. So, that a novel computer assisted digital image processing technique and smart tool is required to provide intelligent prediction logics to identify the breast cancer on earlier conditions instead of detecting that in complex stages. In this paper, a novel Deep Learning enabled Image Processing tool is designed, called as Digital Image Processing Scheme enabled SmartKit (DIPSK) to identify the breast cancer disease effectively on earlier stages without any human intervention. The prop osed DIPSB enables the system to acquire the input scanning image and process that with DIP techniques with respect to pre-processing feature extraction and classification. The detailed features are cross-validated with the trained samples, in which the Deep Learning procedure called Improved Convolutional Neural Network (iCNN) is utilized by the SmartKitto provide classification process in accurate manner. The final resulting of this SmartKitis more efficient as compare to the traditional image processing schemes and provides accurate prediction on earlier stages. The resulting section provides the proper proof for the classification accuracy and prediction logics. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
36. Machine-based identification system via optical character recognition.
- Author
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Shahin, Mohammad, Chen, F. Frank, and Hosseinzadeh, Ali
- Subjects
OPTICAL character recognition ,SYSTEM identification ,DIGITAL image processing ,LEAN management ,INFORMATION technology - Abstract
In the past, information technology was frequently considered a waste from Lean manufacturing perspective. Though the business landscape evolves and competition from low-cost nations grows, new models must be created that provides a competitive edge by combining the Lean paradigm with Industry 4.0 technical advancements. This paper aims to contribute to this field by assessing the supporting function of a Machine-based Identification system (MBID) via Optical Character Recognition (OCR) in Lean manufacturing paradigm. The objective of this paper is to also explore the use of MBID to enable a competitive manufacturing process in a Lean 4.0 environment. Furthermore, a MBID via OCR model is proposed to extract the printed identification number of packages from images captured by a fixed camera in an industrial environment. The method considers different digital image processing techniques to deal with the significant lighting and printing variation observed, followed by a segmentation process that extracts and aligns the characters. The proposed system utilized an approach to treating lighting variations in images, covering low contrast, distorted, darker, and brighter images. Experiments were carried out on a data set consisting of 200 images and achieved an overall detection accuracy of 95% with a very low Character Error Rate (CER) value of 0.0041, clearly supporting the validity and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
37. TARGET IMAGE PROCESSING BASED ON SUPER-RESOLUTION RECONSTRUCTION AND MACHINE LEARNING ALGORITHM.
- Author
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CHUNMAO LIU
- Subjects
IMAGE processing ,COMPUTER-assisted image analysis (Medicine) ,IMAGE reconstruction ,MAGNETIC resonance imaging ,HIGH resolution imaging ,IMAGE reconstruction algorithms ,MACHINE learning ,DIGITAL image processing - Abstract
This article proposes a target image processing method based on super-resolution reconstruction and machine learning algorithms, which solves the low-resolution problem in medical images during imaging. This method uses nonlocal autoregressive learning based on a medical image super-resolution reconstruction method. The autoregressive model is introduced into the sparse representation-based medical image super-resolution reconstruction model by utilizing medical image data inherent nonlocal similarity characteristics. At the same time, a clustering algorithm is used to obtain a classification dictionary, improving experimental efficiency. The experimental results show that ten randomly selected CT/MR images are used as test images, and each image's peak signal-to-noise ratio and structural similarity values are calculated separately. Compared with other methods, the method proposed in this paper is significantly better and can achieve ideal results, with the highest value being 31.49. This method demonstrates the feasibility of using super-resolution reconstruction and machine learning algorithms in medical image resolution. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
38. An improved corner dealiasing and recognition algorithm for 2D Wadell roundness computation.
- Author
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Chen, Jianhuang, Zhang, Zhongjian, Lin, Daming, Li, Lihui, and Xu, Wenjie
- Abstract
This paper optimizes the 2D Wadell roundness calculation of particles based on digital image processing methods. An algorithm for grouping corner key points is proposed to distinguish each independent corner. Additionally, the cyclic midpoint filtering method is introduced for corner dealiasing, aiming to mitigate aliasing issues effectively. The relationships between the number of corner pixels (m), the central angle of the corner (α) and the parameter of the dealiasing degree (n) are established. The Krumbein chart and a sandstone thin section image were used as examples to calculate the 2D Wadell roundness. A set of regular shapes is calculated, and the error of this method is discussed. When α ≥ 30°, the maximum error of Wadell roundness for regular shapes is 5.21%; when 12° ≤ α < 30°, the maximum error increases. By applying interpolation to increase the corner pixels to the minimum number (m
0 ) within the allowable range of error, based on the α-m0 relational expression obtained in this study, the error of the corner circle can be minimized. The results indicate that as the value of m increases, the optimal range interval for n also widens. Additionally, a higher value of α leads to a lower dependence on m. The study's results can be applied to dealiasing and shape analysis of complex closed contours. [ABSTRACT FROM AUTHOR]- Published
- 2024
- Full Text
- View/download PDF
39. Admission and Registrar Office (ARO) Mobile Document Scanner with Archiving System.
- Author
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Mallares, Dinalyn A., Alegata, Wayne Custer G., Madrigal, Dennis V., and Bual, Joel M.
- Subjects
ARCHIVES ,DIGITAL image processing ,DIGITIZATION of archival materials ,SCANNING systems ,SCHOOL admission - Abstract
Archive document scanning refers to the procedure of converting physical paper records into digital formats, such as images or text files, and establishing a systematic workflow to preserve and access these archived files electronically over an extended period [1]. With the growing acceptance of mobility and digitalization worldwide, institutions such as libraries, government offices, museums, and universities are increasingly adopting document scanning as means to safeguard and preserve historical documents and records. This approach is particularly valuable in preventing the natural degradation and aging of physical documents, as highlighted by Scan2Archive [2]. The purpose of the Admission and Registrar Office (ARO) Mobile Document Scanner with Archiving System is to support the registrar's office in managing student requests and admissions as well as handling the documents that are presented during the enrollment. The office of the registrar will experience a transformation in document handling with the development of a document archiving system, which will promote operational excellence, security, and transparency. With the help of this system, the office's procedures for managing vital records--such as student transcripts, enrollment requirements, and other important paperwork--will be completely transformed. The basis for the development of the system is transforming paper-based records into a digitally accessible format. In evaluating the effectiveness of the application and the system's operation it specified the performance requirements for the software evaluation. The star topology was used as part of the communication interfaces. The thirty (30) respondents tabulated the data in each of the different features of the system are accuracy, user-friendliness, reliability, operability, learning reinforcement, interactivity, compatibility, and the cross-platform environment was rated as "excellent". The application was made to help the whole process become much more convenient to the admin and staff of the university. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
40. Orchard monitoring based on unmanned aerial vehicles and image processing by artificial neural networks: a systematic review.
- Author
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Popescu, Dan, Ichim, Loretta, and Stoican, Florin
- Subjects
ARTIFICIAL neural networks ,DRONE aircraft ,IMAGE processing ,ORCHARDS ,FRUIT quality ,TREE diseases & pests ,DIGITAL image processing - Abstract
Orchard monitoring is a vital direction of scientific research and practical application for increasing fruit production in ecological conditions. Recently, due to the development of technology and the decrease in equipment cost, the use of unmanned aerial vehicles and artificial intelligence algorithms for image acquisition and processing has achieved tremendous progress in orchards monitoring. This paper highlights the new research trends in orchard monitoring, emphasizing neural networks, unmanned aerial vehicles (UAVs), and various concrete applications. For this purpose, papers on complex topics obtained by combining keywords from the field addressed were selected and analyzed. In particular, the review considered papers on the interval 2017-2022 on the use of neural networks (as an important exponent of artificial intelligence in image processing and understanding) and UAVs in orchard monitoring and production evaluation applications. Due to their complexity, the characteristics of UAV trajectories and flights in the orchard area were highlighted. The structure and implementations of the latest neural network systems used in such applications, the databases, the software, and the obtained performances are systematically analyzed. To recommend some suggestions for researchers and end users, the use of the new concepts and their implementations were surveyed in concrete applications, such as a) identification and segmentation of orchards, trees, and crowns; b) detection of tree diseases, harmful insects, and pests; c) evaluation of fruit production, and d) evaluation of development conditions. To show the necessity of this review, in the end, a comparison is made with review articles with a related theme. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
41. Development of Static and Dynamic Colorimetric Analysis Techniques Using Image Sensors and Novel Image Processing Software for Chemical, Biological and Medical Applications.
- Author
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Yoo, Woo Sik, Kim, Jung Gon, Kang, Kitaek, and Yoo, Yeongsik
- Subjects
IMAGE processing software ,COLORIMETRIC analysis ,IMAGE sensors ,CHEMICAL processes ,DIGITAL image processing ,VIDEO excerpts ,COLOR of meat - Abstract
Colorimetric sensing techniques for point(s), linear and areal array(s) were developed using image sensors and novel image processing software for chemical, biological and medical applications. Monitoring and recording of colorimetric information on one or more specimens can be carried out by specially designed image processing software. The colorimetric information on real-time monitoring and recorded images or video clips can be analyzed for point(s), line(s) and area(s) of interest for manual and automatic data collection. Ex situ and in situ colorimetric data can be used as signals for process control, process optimization, safety and security alarms, and inputs for machine learning, including artificial intelligence. As an analytical example, video clips of chromatographic experiments using different colored inks on filter papers dipped in water and randomly blinking light-emitting-diode-based decorative lights were used. The colorimetric information on points, lines and areas, with different sizes from the video clips, were extracted and analyzed as a function of time. The video analysis results were both visualized as time-lapse images and RGB (red, green, blue) color/intensity graphs as a function of time. As a demonstration of the developed colorimetric analysis technique, the colorimetric information was expressed as static and time-series combinations of RGB intensity, HSV (hue, saturation and value) and CIE L*a*b* values. Both static and dynamic colorimetric analysis of photographs and/or video files from image sensors were successfully demonstrated using a novel image processing software. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
42. Research on multiple jellyfish classification and detection based on deep learning.
- Author
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Han, Ying, Chang, Qiuyue, Ding, Shuaimin, Gao, Meijing, Zhang, Bozhi, and Li, Shiyu
- Subjects
DEEP learning ,JELLYFISHES ,CONVOLUTIONAL neural networks ,DIGITAL image processing ,MARINE biologists ,MARINE ecology - Abstract
In recent years, there have been frequent jellyfish outbreaks in many offshore areas worldwide, which have severely affected marine fishery production, coastal tourism, coastal industrial cooling water systems, and marine ecology. Achieving the monitoring of jellyfish plays a vital role in solving the problems mentioned above. However, the research on jellyfish is still in the primary stage. Jellyfish detection technology based on deep learning is gradually being applied to jellyfish detection due to its high efficiency and accuracy, but it is not systematic enough and can identify few jellyfish species. So this paper studies a jellyfish detection algorithm based on deep learning. Based on convolution neural network theory and digital image processing technology, 10 species of jellyfish and fish are detected. Because the quality of underwater images affects the detection accuracy, to further improve the accuracy of the detection algorithm, this paper studies the underwater image processing algorithm. Experimental results show that the image quality is better after applying the three algorithms of dark channel prior algorithm, quadratic combining gray world and perfect reflection algorithm, and contrast-limited adaptive histogram equalization algorithm, which is more conducive to detection. Then, deep learning theory is applied to classify jellyfish. By comparing the AlexNet and GoogLeNet backbone networks' classification results, the accuracy of the jellyfish classification task based on the GoogLeNet backbone network is 96.21%, which is better than AlexNet. Finally, the Faster R-CNN algorithm is used to detect jellyfish, and its detection performance is analyzed based on the two backbone networks mentioned above. The results show that the Faster R-CNN algorithm based on GoogLeNet has a higher detection accuracy in the jellyfish detection task, with an average detection accuracy of 74.96%. In addition, we set up a new jellyfish data set, which includes 25,344 images. The images were divided into 11 species, including 10 species of jellyfish and one fish species. The paper's research lays a theoretical and technical foundation for the subsequent construction of a real-time monitoring system for underwater jellyfish optical imaging, plays an important role in the development of jellyfish monitoring technology, and provides valuable information for marine biologists. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
43. Three-Dimensional (3D) Visualization under Extremely Low Light Conditions Using Kalman Filter.
- Author
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Kim, Hyun-Woo, Cho, Myungjin, and Lee, Min-Chul
- Subjects
DATA visualization ,RANDOM numbers ,IMAGE reconstruction ,THREE-dimensional imaging ,SIGNAL-to-noise ratio - Abstract
In recent years, research on three-dimensional (3D) reconstruction under low illumination environment has been reported. Photon-counting integral imaging is one of the techniques for visualizing 3D images under low light conditions. However, conventional photon-counting integral imaging has the problem that results are random because Poisson random numbers are temporally and spatially independent. Therefore, in this paper, we apply a technique called Kalman filter to photon-counting integral imaging, which corrects data groups with errors, to improve the visual quality of results. The purpose of this paper is to reduce randomness and improve the accuracy of visualization for results by incorporating the Kalman filter into 3D reconstruction images under extremely low light conditions. Since the proposed method has better structure similarity (SSIM), peak signal-to-noise ratio (PSNR) and cross-correlation values than the conventional method, it can be said that the visualization of low illuminated images can be accurate. In addition, the proposed method is expected to accelerate the development of autonomous driving technology and security camera technology. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
44. Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.
- Author
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Pitarch, Carla, Ungan, Gulnur, Julià-Sapé, Margarida, and Vellido, Alfredo
- Subjects
DEEP learning ,DIGITAL image processing ,RESEARCH evaluation ,MAGNETIC resonance imaging ,MACHINE learning ,ARTIFICIAL intelligence ,BRAIN tumors ,QUALITY control ,RESEARCH funding ,DATA analysis ,NEURORADIOLOGY ,ONCOLOGY ,DIFFUSION of innovations - Abstract
Simple Summary: Within the rapidly evolving landscape of Machine Learning in the medical field, this paper focuses on the forefront advancements in neuro-oncological radiology. More specifically, it aims to provide the reader with an in-depth exploration of the latest advancements in employing Deep Learning methodologies for the classification of brain tumor radiological images. This review meticulously scrutinizes papers published from 2018 to 2023, unveiling ongoing topics of research while underscoring the main remaining challenges and potential avenues for future research identified by those studies. Beyond the review itself, the paper also underscores the importance of placing the image data modelling provided by Deep Learning techniques within the framework of analytical pipeline research. This means that data quality control and pre-processing should be correctly coupled with modelling itself, in a way that emphasizes the importance of responsible data utilization, as well as the critical need for transparency in data disclosure to ensure trustworthiness and reproducibility of findings. Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the analysis of medical images. This paper reviews in detail some of the most recent advances in the use of Deep Learning in this field, from the broader topic of the development of Machine-Learning-based analytical pipelines to specific instantiations of the use of Deep Learning in neuro-oncology; the latter including its use in the groundbreaking field of ultra-low field magnetic resonance imaging. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
45. Automated detection of leukemia in blood microscopic images using image processing techniques and unique features: Cell count and area ratio.
- Author
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Al-Ghraibah, Amani and Al-Ayyad, Muhammad
- Subjects
DIGITAL image processing ,LEUCOCYTES ,ERYTHROCYTES ,IMAGE processing ,CANCER diagnosis - Abstract
Leukemia is a type of cancer that affects the body's blood-forming tissue, where the bone marrow produces an excessive amount of abnormal white blood cells (WBCs) that do not function properly. The diagnosis of leukemia is typically done by a trained expert who visually observes unique features and determines the type of cancer. However, digital image processing techniques have been improving in the healthcare system, particularly in diagnosing different types of diseases and helping doctors make treatment decisions. This paper presents a system for detecting leukemia in blood microscopic images and classifying them as normal or abnormal (with leukemia) automatically. Two main techniques were used: counting the number of WBCs around red blood cells (RBCs) and measuring the average area of WBCs around a bounding box around each cell. The classification accuracy was calculated at 91.7 and 88.8% for the two techniques, respectively. These techniques can be used as features in machine learning applications, and the system presented is faster and more efficient than traditional diagnostic processes used in hospitals. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
46. Digital continuity of rotations in the 2D regular grids.
- Author
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Saadetoğlu, Müge, Nagy, Benedek, and Avkan, Aydın
- Abstract
A digitized rigid motion is called digitally continuous if two neighbor pixels still stay neighbors after the motion. This concept plays important role when people or computers (artificial intelligence, machine vision) need to recognize the object shown in the image. In this paper, digital rotations of a pixel with its closest neighbors are of our interest. We compare the neighborhood motion map results among the three regular grids, when the center of rotation is the midpoint of a main pixel, a grid point (corner of a pixel) or an edge midpoint. The first measure about the quality of digital rotations is based on bijectivity, e.g., measuring how many of the cases produce bijective and how many produce not bijective neighborhood motion maps (Avkan et. al, 2022). Now, a second measure is investigated, the quality of bijective digital rotations is measured by the digital continuity of the resulted image: we measure how many of the cases are bijective and also digitally continuous. We show that rotations on the triangular grid prove to be digitally continuous at many more real angles and also as a special case, many more integer angles compared to the square grid or to the hexagonal grid with respect to the three different rotation centers. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
47. An In-Depth Analysis of Steganography Techniques: From Classical Edge Detection to Adaptive Approaches.
- Author
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Abdullah, Noor Gh. and Hasso, Shahd A.
- Subjects
CRYPTOGRAPHY ,DIGITAL image processing ,IMAGE encryption ,DATA security ,DATA encryption - Abstract
This paper delves into the realm of steganography, focusing on the evolution of techniques employed to conceal data within digital images. Beginning with an exploration of the motivations behind information security and the role of cryptography, the paper introduces adaptive steganography as a discreet means of incorporating private data into a cover medium. The discussion then shifts to the three key factors in image steganography - resilience, capacity, and imperceptibility - forming the foundation of effective data hiding. The spatial and frequency domain methods are compared, with specific emphasis on the classical Least Significant Bit insertion and more advanced adaptive steganography techniques. The paper further introduces edge-based steganography, highlighting the advantage of manipulating edge areas for increased imperceptibility. Moving beyond definitions and types, the paper provides a comprehensive analysis of notable research works in the field, elucidating their objectives, methodologies, results, strengths, and limitations. The conclusion reflects on the dynamic landscape of steganography, acknowledging both achievements and areas for improvement. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
48. 无监督学习步态识别综述.
- Author
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陈福仕, 沈 尧, 周池春, 丁 锰, 李居昊, 赵东越, 雷永升, and 潘亦伦
- Abstract
Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2024
- Full Text
- View/download PDF
49. The Performance and Clinical Applicability of HER2 Digital Image Analysis in Breast Cancer: A Systematic Review.
- Author
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Dunenova, Gauhar, Kalmataeva, Zhanna, Kaidarova, Dilyara, Dauletbaev, Nurlan, Semenova, Yuliya, Mansurova, Madina, Grjibovski, Andrej, Kassymbekova, Fatima, Sarsembayev, Aidos, Semenov, Daniil, and Glushkova, Natalya
- Subjects
BREAST tumors ,DIGITAL diagnostic imaging ,DESCRIPTIVE statistics ,SYSTEMATIC reviews ,MEDLINE ,DIGITAL image processing ,ONLINE information services ,ALGORITHMS - Abstract
Simple Summary: HER2-positive breast cancer occurs in 15–30% of cases and has a poor prognosis. Digital image analysis of HER2 is promising, but its implementation in real clinical practice remains unclear. This systematic review evaluates the effectiveness of digital image analysis algorithms for HER2 in breast cancer and their performance, with a focus on testing them in real-world clinical settings. The authors aim to assess the applicability of these algorithms in practical clinical scenarios. By analyzing 25 papers from the period 2013–2024 and emphasizing mostly deep learning approaches, the review underscores the importance of standardized evaluation criteria, study designs tailored for clinical applications, and clinical validation. While direct evidence of clinical application was not found, the findings aim to guide future research and the implementation of digital image analysis in breast cancer diagnosis within clinical settings, potentially impacting the research community by advancing algorithmic applications in real clinical practice. This systematic review aims to address the research gap in the performance of computational algorithms for the digital image analysis of HER2 images in clinical settings. While numerous studies have explored various aspects of these algorithms, there is a lack of comprehensive evaluation regarding their effectiveness in real-world clinical applications. We conducted a search of the Web of Science and PubMed databases for studies published from 31 December 2013 to 30 June 2024, focusing on performance effectiveness and components such as dataset size, diversity and source, ground truth, annotation, and validation methods. The study was registered with PROSPERO (CRD42024525404). Key questions guiding this review include the following: How effective are current computational algorithms at detecting HER2 status in digital images? What are the common validation methods and dataset characteristics used in these studies? Is there standardization of algorithm evaluations of clinical applications that can improve the clinical utility and reliability of computational tools for HER2 detection in digital image analysis? We identified 6833 publications, with 25 meeting the inclusion criteria. The accuracy rate with clinical datasets varied from 84.19% to 97.9%. The highest accuracy was achieved on the publicly available Warwick dataset at 98.8% in synthesized datasets. Only 12% of studies used separate datasets for external validation; 64% of studies used a combination of accuracy, precision, recall, and F1 as a set of performance measures. Despite the high accuracy rates reported in these studies, there is a notable absence of direct evidence supporting their clinical application. To facilitate the integration of these technologies into clinical practice, there is an urgent need to address real-world challenges and overreliance on internal validation. Standardizing study designs on real clinical datasets can enhance the reliability and clinical applicability of computational algorithms in improving the detection of HER2 cancer. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
50. Identification of plants from the convolutional neural network.
- Author
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Tiendrebeogo, Amed
- Subjects
CONVOLUTIONAL neural networks ,DEEP learning ,PLANT identification ,SPINE ,DIGITAL image processing - Abstract
This paper studies a deep learning-based plant detection algorithm. Based on convolution neural network theory and digital image processing technology, 9 species of planes are detected. In order to further improve the accuracy of the detection algorithm, the paper proposes to improve the quality of the images. The experimental results show that the image quality is better after applying the following three algorithms the black channel prioritization algorithm, the quadratic combination algorithm of gray world and perfect reflection, and the contrast-limited adaptive histogram equalization algorithm, which is more conducive to the detection. Then, deep learning theory is applied to classify the plants. Comparing the classification results of AlexNet and GoogLeNet backbone networks, the accuracy of the jellyfish classification task based on GoogLeNet backbone network is 96.21%, which is better than AlexNet. Finally, the Faster R-CNN algorithm is used to detect plants and its detection performance is analyzed based on the two backbone networks mentioned above. The results show that the Faster R-CNN algorithm based on GoogLeNet has better detection accuracy in the jellyfish detection task, with an average detection accuracy of 74.96%. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
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