507 results
Search Results
2. Relationships between Ink Jet Print Quality and Paper Formation and Roughness
- Author
-
International Paper Physics Conference (2007 : Gold Coast, Qld.), Loffler, Steven M, Dusting, Vanessa J, and Vanderhoek, Nafty J
- Published
- 2007
3. Framework development of fabric drape evaluation: Paper weighing method transformed to digital image analysis technique.
- Author
-
Kamadi, Nur Rasyidah, Ghani, Suzaini Abdul, Yahya, Mohamad Faizul, Tulos, Najua, Yusof, Nur Ain, and Yusof, Nor Juliana Mohd
- Subjects
DIGITAL images ,IMAGE analysis ,DRAPERIES ,TEXTILES ,PAPER arts ,VALUATION of real property - Abstract
The drape is a fabric's ability to deform when suspended under its weight in specified conditions of known size using a Drape Meter. The Drape Coefficient % (DC%) is calculated by taking the weight of paper cut out based on the shadow of the fabric. A fabric property is very important in relating to the behavior of drapes and these factors were determined by earlier researchers in developing a tool to measure drape. The Cusiks's Drape Meter was the first equipment that used the principle of shadow in reflecting the drape behavior and later on calculating the DC%. The principle introduced by Cusick has been used by a lot of researchers to determine DC% more accurately using computer captured images. In recent years, a lot of research has moved forward using software to calculate the DC% by incorporating the values of fabric properties to create an image for the drape. A lot of work on comparing the values of DC% using conventional and simulated/virtual methods was published. The usage of particular software can predict the DC% before the actual production of fabrics. Prediction can be made based on physical properties needed on particular fabrics to be produced and thus saving a lot of time and giving more choices in fabrics selection. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
4. A New Instrument for Measurement of the Out-of-plane Dimensional Stability of Paperboard
- Author
-
International Paper Physics Conference (2007 : Gold Coast, Qld.), Lucisano, Marco FC, Nilsson, Mikael, and Cochard, Julien
- Published
- 2007
5. Measurement of Curl Using Image Analysis
- Author
-
International Paper Physics Conference (2007 : Gold Coast, Qld.), Reich, Michael, Conn, Andrew, Faltas, Rafik, Liu, Fuping, and Branson, Tristen
- Published
- 2007
6. A New Method for the Measurement of Longitudinal Fibre Flexibility
- Author
-
International Paper Physics Conference (2007 : Gold Coast, Qld.), Navaranjan, Namasivayam, Richardson, John D, Dickson, Alan R, Blaikie, Richard J, and Prabhu, Ashok N
- Published
- 2007
7. Measurement of Wet Fibre Flexibility of Mechanical Pulp Fibres by Confocal Laser Scanning Microscopy
- Author
-
International Paper Physics Conference (2007 : Gold Coast, Qld.), Yan, Dongbo, Li, Kecheng, and Zhou, Yajun
- Published
- 2007
8. Image classification based on sentiment polarity using machine learning approaches.
- Author
-
Sharma, Divya, Sharma, Shilpa, Raja, Linesh, Bhagirath, Swami Nisha, and Bhatnagar, Vaibhav
- Subjects
IMAGE recognition (Computer vision) ,MACHINE learning ,RANDOM forest algorithms ,IMAGE analysis ,CLASSIFICATION algorithms ,SECURE Sockets Layer (Computer network protocol) ,AIRBORNE lasers ,TEXT recognition - Abstract
Sentiment, emotions, feelings, showing or giving judgments by face gestures, etc. can be communicated by text, speech or images. Analyzing image Polarity via images is now a burgeoning research field. An image can easily be interpreted by any human being and acts as a bridge to relate an image to the human's thoughts. The paper proposed a model for analyzing the sentiments or emotions of an image. We have used a Data-Mining Orange Tool for executing the experiment for classification and sentiment analysis of an image. The main challenge is to acquire the polarity of the unlabeled data using optimal machine learning techniques. To overcome this challenge for getting the polarity and sentiment of an unlabeled data the proposed model used various noteworthy machine learning models like Neural Network, Naïve Bayes and random forest for classifying and analyzing the image sentiment and also provide a comparison based on certain parameters like Area Under Curve, Precision, Recall, classification Accuracy, etc. The tool provides a complete package for performing image analysis using the widgets available with orange tool and a data visualization tool to get a visible representation of the output. The proposed model shows that Random Forest performance the best from the three compared algorithms with classification Accuracy of 0.747, recall & precision as 0.747 & 0.752 and the accuracy based on confusion matrix is 74.72%. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
9. Application of artificial intelligence in computer vision algorithms.
- Author
-
Wu, Kexin
- Subjects
OBJECT tracking (Computer vision) ,ARTIFICIAL intelligence ,COMPUTER vision ,IMAGE analysis ,COMPUTER algorithms ,IMAGE recognition (Computer vision) - Abstract
Image recognition plays a very important role in the application of computer vision, but there is a problem that object tracking is not ideal. The previous recognition method could not solve the problem of object tracking in the field of computer vision, and the recognition was inaccurate. Therefore, this paper proposes an artificial intelligence technology for object tracking image recognition and analysis. they used to process the image information, and the indicators are divided according to the requirements of artificial intelligence processing to reduce them Interference factors in image recognition. Then, the theory of human intelligence processes the image artificial intelligence, forms an artificial intelligence processing scheme, and recognizes the results of the image Conduct a comprehensive analysis. The revised results show that the calculation process in this paper is relatively good, and it is suitable for practical theoretical analysis, which has high significance and function, and can be used for later comprehensive judgment. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
10. The design of an efficient bioinspired CNN model for automated malaria detection in blood smear images.
- Author
-
Choudhary, Ashutosh Kumar, Nausheen, Iram, Khan, Nariman, Singh, Brijendra Krishna, and Purbey, Suniti
- Subjects
CONVOLUTIONAL neural networks ,MALARIA ,COMPUTER-aided diagnosis ,PROCESS capability ,IMAGE analysis - Abstract
The use of a bioinspired Convolutional Neural Network (CNN) model in this paper's unique automated method for malaria diagnosis in blood smear images. Extensive development in computer-aided diagnosis systems has been driven by the urgent need to fight malaria, a disease that poses a serious threat to life. However, the precision, accuracy, and recall rates of current approaches are constrained, which reduces their usefulness in real-world settings. The suggested approach places a strong emphasis on incorporating bioinspired ideas into the CNN model design to overcome these limitations. The bioinspired CNN model outperforms conventional CNN architectures in terms of greater specificity and expanded capabilities by taking inspiration from nature-inspired algorithms. This concept provides a considerable improvement over current methodologies by enabling more precise and reliable malaria detection process. The strategy that is being discussed combines machine learning strategies with extensive image processing capabilities. Through the use of several approaches, this integration makes it possible to provide a comprehensive solution to the problem of malaria detection. In comparison to conventional methods, experimental results show the proposed method to be effective, with precision rates of 3.9%, accuracy rates of 2.9%, and a surprising 3.5% increase in recall levels. Thus, this paper demonstrates the need for a novel strategy to malaria detection in light of the shortcomings of current techniques. By combining a bio-inspired CNN model with image processing and machine learning methods, it is possible to greatly improve the precision, accuracy, and recall rates for automated malaria identification in blood smear images. The results of this work show significant promise for developing the discipline of medical image analysis and supporting continuing global efforts to eradicate malaria for real-time scenarios. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
11. Assessment of structural and mechanical quality of nitrided layers on structural steels with the use of precision metallographic spherical polished microsection in the HRC indentation mark.
- Author
-
Betiuk, Marek, Domanowski, Piotr, Mirońska, Aleksandra, and Goluch, Weronika
- Subjects
MATERIALS testing ,SURFACE cracks ,IRON ,IMAGE analysis ,STRUCTURAL steel ,NITRIDING - Abstract
The paper presents a series of material tests for the assessment of structural and mechanical quality of nitrided layers obtained on steels: 40HM, 38HMJ. The assessment of the structural quality of the layers was made in the comparisons of metallographic images of cross-sections and spherical sections. The thickness of the iron nitride layer and the internal nitriding zone were assessed. In the structural description, metallographic analyses and measurements of profile hardness were used with the criterion HV 0.5 g=r+50, g=400 HV0.5. The mechanical quality of the WW system was assessed on the basis of the analysis of SEM images of the nature of cracks or their absence around the ones formed within the HRC indentation. The paper proposes an improvement of this method by introducing the imaging of cracks on the surface of a spherical microsection located in the HRC indentation space. The prairie spherical microsection was obtained using a modern proprietary Recalo2 research station. The paper presents the technical characteristics of the Recalo2 stand and indicates further directions for its technical improvement. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
12. OPTIMIZATION STRATEGIES FOR HIGH-LEVEL SYNTHESIS OF CONVOLUTIONAL NEURAL NETWORKS HARDWARE ACCELERATORS.
- Author
-
Egiazarian, Victor and Bykovskii, Sergei
- Subjects
CONVOLUTIONAL neural networks ,IMAGE processing ,IMAGE analysis ,HARDWARE ,PROGRAMMING languages - Abstract
There are many problems related to image processing and analysis that could be solved using convolutional neural networks (CNN). It's easy to implement CNN using one of thousand high-level programming languages. Such CNN will not be fast and energy efficient enough to be used in real-time systems. The good way to solve this problem is to use special hardware accelerators (neuroprocessors). The paper shows that it is possible to reduce the calculation time of the network using neuroprocessors. The implementation of such hardware is quite challenging process that requires specialized knowledge. That's why we need a tool for automated hardware synthesis. The basis of this tool are various optimizations that will allow us to transfer the network to a hardware platform. The optimization mechanisms in existing tools are either very poor or nonexistent. We propose several optimizations on different stages of developing process of target hardware. In the paper the authors describe a CNN model for handwritten digits recognition and show how to reduce the number of neural network parameters without significant accuracy losses. The authors managed to reduce the number of parameters from 644 120 to 31 530 with accuracy loss just about 0.43%, making the CNN suitable for synthesis on dedicated hardware platform. The authors also examined the dependence of the target platform resources on the method of computing the neural network output (sequentially / pipelined / parallel). It was showed that it is possible to decrease computation time in 7 times using fully parallel computations, bit it required in 4-6 times more resources than using sequential calculations. Using the above, as well as many other optimizations, it allows to create a tool for automated synthesis of high-quality hardware accelerators for CNN. The paper also presents the concept of such tool. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
13. MEASURING AND EVALUATION OF THE ECHOGENICITY GRADE OF SUBSTANTIA NIGRA IN MRI SEQUENCES VS B-MODE ULTRASOUND IMAGING USING THE SAME ALGORITHM: PILOT COMPARISON STUDY.
- Author
-
Blahuta, Jiri, Soukup, Tomas, Lavrincik, Jan, Pavlik, Lukas, and Kozel, Jiri
- Subjects
SUBSTANTIA nigra ,ULTRASONIC imaging ,MAGNETIC resonance imaging ,DIAGNOSTIC ultrasonic imaging ,IMAGE analysis ,FETAL ultrasonic imaging - Abstract
Diagnostic ultrasound (US) and magnetic resonance imaging (MRI) are important medical imaging methods in modern radiology. Our research is focused on imaging brain structures in neurology. In this paper we present differences of digital image analysis of the substantia nigra (SN) between US and MRI using the same algorithm. In the past, we developed an application for analyzing substantia nigra echogenicity in BMODE US images. Our developed application is based on a principle of binary thresholding in Region of Interest (ROI) to evaluate echogenicity grade. Increased echogenicity of SN is one of important markers for Parkinson's Disease (PD) progress. The goal of this paper is to analyze if the same principle used for US B-MODE imaging is also applicable for different MR sequences to find out SN changes. From the achieved results detectable SN changes using MRI are possible at least as a complementary examination to US imaging. We need to prove if echogenicity index (called Echo-Index) is well reproducible value between two different MR sequences; SWI and T2-TSE; how to distinguish between pathological SN and normal anatomy. In the first pilot analysis, it seems that the principle of Echo-Index measurement could be a starting point to create a new large clinical study in this field. Totally 23 MR images from two different sequences (T1 and T2) were analyzed in this pilot study. However, it seems that Echo-Index cannot distinguish normal and diseased SN. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
14. Thermal imaging analysis of temperature distribution on the human skin during strength training exercises.
- Author
-
Zubrzycki, Jarosław, Staniszewski, Michał, Pavlík, Zbyšek, Matysiak, Magdalena, and Łagód, Grzegorz
- Subjects
THERMOGRAPHY ,IMAGE analysis ,THERMAL imaging cameras ,EXERCISE therapy ,TEMPERATURE distribution ,STRENGTH training - Abstract
Thermal imaging cameras are used in many fields. The paper analyses the possibility of using thermal imaging in medicine. It has been proven that the infrared camera can be used in sports medicine. The aspects of the measurement apparatus and the features which should be paid attention to during the tests were also quoted in the paper. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
15. Design and implementation of efficient automatic attendance record system based on facial recognition technique.
- Author
-
Rathore, Kuldeep Singh, Pandey, Abhishek, Gupta, Arya, Srivastava, Divyanshu, Agrawal, Kartik, and Srivastava, Saurabh
- Subjects
HUMAN facial recognition software ,FACE perception ,ATTENDANCE ,IMAGE analysis - Abstract
This paper describes a modern and innovative method for attendance monitoring using an open CV. It is an open-source library and is widely used whenever we talk about facial recognition, video analysis, and image analysis. The traditional method of attendance taking is a hectic and time-consuming task that also requires a lot of physical effort. In this paper face detection, face recognition, and printing the accurate data in form of the CSV file is presented. Face detection helped to determine the position and location of the face; face recognition helped to mark the nominee's attendance and the third step is storing the data received in form of a CSV file such that the fear of attendance is delicacy has been solved. The proposed solution to the traditional attendance problem not only eradicates the burden on human resource but also provide one of the fastest and more efficient methods to take the attendance. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
16. CONDITIONAL DIFFUSION MODEL FOR GENERATING BIOLOGIC DATA.
- Author
-
Sidorenko, Denis and Shalyto, Anatoly
- Subjects
SELF-organizing maps ,INFORMATION retrieval ,DATA analysis ,IMAGE analysis ,PREDICTION models - Abstract
Diffusion models have shown remarkable success in generating high-quality data across various domains. However, applications in the biomedical field often require conditioning the generative process on additional information to obtain relevant and controlled outputs. This paper presents a conditional diffusion model tailored for multimodal data fusion, with a focus on integrating categorical features and continuous variables to generate biologically plausible patterns of gene expression and methylation. The model architecture builds upon the U-Net with self-attention mechanisms and employs techniques to effectively incorporate categorical conditions via learnable embeddings and continuous conditions through transformation networks. To represent the gene data as images for the diffusion model, self-organizing maps are used to construct a unified coordinate system based on expression or methylation profiles. Experimental results on the GTEx and CNCB datasets demonstrate the model's promising performance in tasks such as tissue classification and age prediction from generated methylation patterns. However, there is room for improvement in handling continuous conditions for generating more accurate expression patterns. The conditional diffusion approach shows strong potential for generating biologically relevant data conditioned on multiple factors, with key areas for future work including enhanced continuous condition modeling and capturing intricate details in the generated patterns. [ABSTRACT FROM AUTHOR]
- Published
- 2024
17. A DEFORMABLE 3D ZEBRAFISH MODEL FOR BRIGHT-FIELD MICROSCOPY IMAGE SIMULATION.
- Author
-
Jámbor, Richárd and Tanács, Attila
- Subjects
LOGPERCH ,THREE-dimensional imaging ,MICROSCOPY ,BIOLOGICAL research ,IMAGE analysis - Abstract
Due to their rapid reproduction, development, and similar genetic structure to humans, zebrafish are popular animals in various biological research. Their small size and body transparency make them convenient to examine under a bright-field light microscope. However, these provide 2-dimensional projected images, which makes 3D measurements difficult. Usual approaches try to reconstruct 3D structures from series of projected images, that is not always an option. In our paper we propose an approach to model the images acquisition process including the 3D parametric deformable model of the zebrafish and the light conditions in the microscopy environment. Our goal is to simulate images that look similar to the real ones using only a few parameters. [ABSTRACT FROM AUTHOR]
- Published
- 2024
18. Personalized travel recommendation system : Hybrid model based on ratings and image analysis.
- Author
-
Bailke, Preeti, Gurav, Rohit, Suryawanshi, Sakshi, Narkhede, Parth, Hukare, Sejal, and Patil, Sankalp
- Subjects
RECOMMENDER systems ,MACHINE learning ,IMAGE analysis ,TOURIST attractions ,WIRELESS geolocation systems - Abstract
Choosing a tourist place for vacation is very important, as a user spends a lot of money and efforts to choose a location wisely. To alleviate this effort, a travel recommendation system is introduced. However, the current recommendation system falls short of providing a perfect tourist location because there are different types of users, each with their own preferences. So here, a user needs a personalized recommendation system. This paper focuses mainly on creating a machine-learning system that is a travel recommender system. It recommends a particular tourist place in two different ways. The first is based on ratings given by other users, and the second is based on previous images of places that users have previously visited. This machine learning model can be further linked to a website using Flask to make it easier for people to use it and search for their favorite destination. This system recommends a tourist attraction based on two factors: first, the ratings of each location given by other users, and second, the images users prefer. This paper mainly focuses on using SVD, LDA, and OpenCV. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
19. Modeling of Color Probability Map Using Artificial Neural Network.
- Author
-
Hee-Yong Kang, Ngoc Hanh Vu Thi, and Dong-Chul Park
- Subjects
ARTIFICIAL neural networks ,PROBABILITY theory ,TRAFFIC signs & signals ,IMAGE analysis ,DATA analysis - Abstract
A modeling method for color probability map using an artificial neural network architecture is examined in this paper. An optimal architecture of the artificial neural network is determined after evaluating various structures extensively. For evaluating the performance of a specific structure of the Multi-Layered Perceptron type Neural Network (MLPNN), a measurement for evaluating the quality of a Color Probability Map (CPM) image is proposed in this paper. An optimal architecture of MLPNN for CPM is found based on the quality measurement. The resulting MLPNN model trained with CPM data is applied to traffic sign datasets. The results show that the architecture of MLPNN obtained in this experiment can detect traffic signs very efficiently when compared with the look-up table method with improved accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
20. THE POLITICS-IMAGE INTERSECTION: PAOLO GIOVIO AND THE TEXTUAL TRACEABILITY OF SYMBOLIC MEANING IN IMAGES.
- Author
-
Kun Xu
- Subjects
RENAISSANCE art ,HISTORIOGRAPHY ,RELIGIOUS idols ,IMAGE analysis - Abstract
During the Renaissance, symbolic art flourished, and emblems were an important sort of emblem during this time period. Dialogo dell' Imprese Militari et Amorose, Paolo Giovio (1483-1552)'s work on emblems, was written during the development of the chronicle of his book Historiarum (1550-1552). It is an important aspect of the establishment of Giovio's system of historiographical thought and aims to establish a model for the display of iconic pictures. The Dialogo is of tremendous relevance in cross-field studies due to the fact that it is an important work in the fields of both image studies and history studies. This paper will argue that Giovio's writing does not stop at the symbolic description and interpretation of images, but rather borrows the functional features of the emblematic vehicle to symbolize the characters, and will attempt to further discuss the interpretive dimension of iconography through the use of Dialogo. The purpose of this paper is to link the perspective of image interpretation with the perspective of historiographical analysis. This argument will be presented through Dialogo. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
21. Analysis review of deep learning for lumbar spine image based on computed tomography and magnetic resonance imaging.
- Author
-
Khamiss, Nasser N. and Al-Kubaisi, Ali
- Subjects
LUMBAR vertebrae ,MAGNETIC resonance imaging ,DEEP learning ,COMPUTED tomography ,COMPUTER-assisted image analysis (Medicine) ,IMAGE analysis - Abstract
Recently, deep learning algorithms have become one of the most popular methods and forms of algorithms used in the medical imaging analysis process. Deep learning tools provide accuracy and speed in the process of diagnosing and classifying lumbar spine problems. Deep learning tools deal with many types of medical images, including computed tomography (CT), X-rays, and magnetic resonance imaging (MRI). MRI is the most common method for diagnosing diseases of the lumbar spine. This paper aims to provide a general overview of how deep learning can be used to analyze lumbar spine images. It focuses on papers, results, and methods used by researchers in recent years. The presented works indicate that deep learning can be highly relied upon in the process of analyzing medical images of the lumbar spine and identifying the correct diagnosis. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
22. A survey on various image analysis techniques.
- Author
-
Pegada, Naresh Kumar, Vetrithangam, Fathima, Azra, and Arunadevi
- Subjects
DIGITAL images ,IMAGE processing ,IMAGE analysis ,CLASSIFICATION algorithms - Abstract
The image processing area has become increasingly important in real-time applications in the current world. Such image processing techniques assist in carrying out processes on digitized images in order to deliver superior results. There are a variety of algorithms for data classification, some of which are rule-based and others which are learning-based. Various image analysis techniques and related challenges in the medical field are investigated in this research. This paper presents effective strategies for overcoming the limitations of image analysis approaches, as well as a brief discussion of image pre-processing before focusing on Image Classification and Segmentation. Our research can help readers learn more about many aspects of medical image analysis. This paper will review the most relevant studies on this topic to date and will describe existing image analysis methods for Data Preprocessing, Image Classification, and Image Segmentation. Image analysis will help readers understand how to increase the performance of their models and expand constrained datasets to take use of the possibilities of other data sets. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
23. Segmentation of ultrasound fetal image using spatial fuzzy C-Mean clustering method.
- Author
-
Pregitha, R. Eveline, Kumar, R. S. Vinod, and Selvakumar, C. Ebbie
- Subjects
FETAL imaging ,ULTRASONIC imaging ,SPECKLE interference ,FETAL ultrasonic imaging ,IMAGE analysis ,IMAGE segmentation - Abstract
Image segmentation is a vital and crucial part of image analysis and medical systems. This is the utmost challenging task as it decides the efficiency of the outcome of the image analysis. Ultrasound images play a significant role among other medical images. The automatic segmentation of these images becomes a factual challenge due to the speckle noise and the artifacts. The selection of the segmentation approach depends on the quality of the segmentation and the scale of information required. In this paper, the fetal ultrasound image is segmented using the Spatial Fuzzy C-Mean clustering method. The feature vectors are developed for each pixel of the fetal images used as inputs for the clustering method. The clustering methods segment the fetal image based on spatial information. An anisotropic Diffusion filter is used for image enhancement before the image segmentation. Experimental results indicate that the Spatial Fuzzy C-Means clustering method can be applied with promising results. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
24. Sustainability of research-based ecosystem.
- Author
-
Ivanova, Antonina, Momcheva, Galina, Zhekova, Ralitsa, Tankova, Eleonora, and Pavlov, Stoyan
- Subjects
SUSTAINABILITY ,ECOSYSTEMS ,SOCIAL network analysis ,IMAGE analysis - Abstract
The aim of the paper is to study sustainability of the research-based entrepreneurial ecosystem BioMed-Varna. The BioMed-Varna is focused on interdisciplinary research in Biomedical Image Analysis, Computational Life Sciences, and Neuroscience. Its main activities include multidisciplinary scientific research, educational projects, development of STEM practices and events/cases for educational institutions, promotion of innovation and entrepreneurial culture, and support for its members' scientific advancement. This paper investigates the development and performs a sustainability appraisal of the research-based entrepreneurial ecosystem using real-world data. The analysis applied by Social network analysis (SNA), reveals the participants' roles, activities and relationships. Using Key Point Indicators (KPIs) and network-based models, this research can support decisions about necessary changes in the ecosystem. Results can be implemented for running evaluation as well as for predictive sustainability assessment. The methodology can be transferred for further research on other regional ecosystems. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
25. A Survey on History, Present and Perspectives of Document Image Analysis Systems.
- Author
-
STĂNICĂ, Iulia-Cristina, BOIANGIU, Costin-Anton, VLĂSCEANU, Giorgiana Violeta, PRODAN, Marcel, AVATAVULUI, Cristian, DEACONESCU, Răzvan-Adrian, and TĂUT, Codrin
- Subjects
DOCUMENT imaging systems ,PIXELS ,INTERNET access ,IMAGE analysis ,SCIENTISTS ,ORDER picking systems - Abstract
We live in the century of technology, where the enormous evolution of data and science has recently favored a strong interest in processing, transmitting, and storing information. If, in the past, only a human mind could extract meaningful information from image data, after decades of dedicated research, scientists have managed to build complex systems that can identify different areas, tables, and texts from scanned documents, all the obtained information being easily accessed and passed by one to another. Books, newspapers, maps, letters, drawings - all types of documents can be scanned and processed in order to become available in a digital format. In the digital world, the storage space is very small compared to physical documents, so these applications will replace millions of old paper volumes with a single memory disk and will be accessible at the same time for anyone using just Internet access and without having a risk of deterioration. Other problems, such as ecological issues, accessibility and flexibility constraints can be solved by the use of document image analysis systems. This article presents the methods and techniques used to process on-paper documents and convert them to electronic ones, starting from pixel level and getting to the level of the entire document. The main purpose of Document Image Analysis Systems is to recognize texts and graphical interpretations from images, extract, format and present their contained information accordingly to the people’s needs. We will also try to provide solid ground for practitioners that implement systems from this category to enhance the unsupervised processing features in order to make physical documents easily available to the masses. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
26. A review on image segregation and analysis within a broad image.
- Author
-
Anand, Prakash, Srivastava, Priyanka, and Ranjan, Piyush
- Subjects
IMAGE analysis ,COMPUTER engineering ,IMAGE processing - Abstract
Object localization, computer vision, video reconnaissance frameworks, and other applications all depend on image processing techniques, which are a basic component of today's computer technologies. Image segregation is a crucial step in image handling. The most popular method for breaking up images into several little pieces called fragments is termed image segregation. Image handling makes to work on the image portrayal to break down the images. A vast array of algorithms are created for segmenting images based on the unique characteristics of each pixel. In this paper various algorithms of Segregation can be evaluated, examined lastly drill down the examination for every one of the algorithms. This correlation study is helpful for expanding precision and execution of Segregation strategies in different image handling areas. The objective of the paper is to study on examination inside a wide image and study on Image segregation. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
27. Extracting Keywords from Images Using Deep Learning for the Visually Challenged.
- Author
-
Jaboob, Said, Chauhan, Munes Singh, Dhanasekaran, Balaji, and Natarajan, Senthil Kumar
- Subjects
ASSISTIVE technology ,DEEP learning ,COGNITIVE ability ,IMAGE analysis ,RECURRENT neural networks - Abstract
Assistive technologies can in many ways facilitate the normal day-to-day lives of the disabled. As part of the ongoing research on assistive technologies at UTAS, Oman, that deals with augmenting and finding multimodal aspects of applications for the disabled, this paper aspires to investigate the role of deep learning in the field of image interpretation. Images are one of the most important mediums of conveying information among humans. Visually impaired persons especially with low cognitive abilities face insurmountable difficulties in understanding cues through images. This challenge is met by filtering words from image captions to facilitate understanding of the key notion conveyed by an image. This work utilizes the image captioning technique using deep learning frameworks such as convolution neural networks (CNN) and recurrent neural networks (RNN) to generate captions. These captions are fed to Rake, an NLP library that identifies keywords in the caption. The entire process is automated and uses transfer learning techniques for caption generation from images. This process is then further integrated with our main project, Finger Movement Multimodal Assistive System (FMAS) thereby incorporating text cues for interpreting images for the visually impaired. [ABSTRACT FROM AUTHOR]
- Published
- 2023
28. Analysis of image filtering based on different types of rank filters.
- Author
-
Turk, Ahmad Al, Najdawi, Saif, Otair, Mohammad, and Ratrout, Serein Al
- Subjects
IMAGE analysis ,DIGITAL images ,PIXELS ,IMAGE denoising ,NOISE - Abstract
Digital images are usually suffering from noises that affect the appearance of the digital image, and this affected appearance could result in disrupting the details, edges, components, etc., and to remove these noises, we have to use one or more filtering techniques or methods. Noise is an unwanted signal that affects digital images which make the digital image has differences compared to the original image or even the real scene. Filtration is a technique used to do modifications to an image to make it more similar to the original image or scene. In this paper, we will use three methods maximum, median, and minimum filtering which are types of rank filtering. Rank filtering is a non-linear technique that uses a kernel that is a window with an odd size and has a minimum size of three, passes through all the pixels in the image and does ascend ordering, and then chooses a pixel within the order kernel to replace the center value of the kernel and reflect it to the direct equivalent position inside the filtered image. For choosing a value from the ordered kernel, a lot of rank filtering techniques appeared, and we will discuss in this paper three types, the minimum filter will choose the minimum value from the ascending ordered kernel, and the maximum filter will choose the maximum value and finally, the median filter will choose the middle value. This paper will compare these rank filters by using MATLAB, which is a scientific and engineering application, and there will be a function for removing noises for the mentioned three rank filters besides determining the performance for each type of rank filter and comparing them to find out the best type of filtering between rank filters mentioned. In this paper, the noise applied to the digital image is salt and pepper noise which is the most famous noise resulting usually from weather effects. The result of this paper is proving that median filtering has the best performance of removing salt and pepper noise compared with maximum and minimum filtering, and it is proved by using subjective and objective notice. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
29. REMOTE SENSING CLASSIFICATION USING MULTI-SENSOR SUPER-RESOLUTION ALGORITHM.
- Author
-
Belov, Alexander and Denisova, Anna
- Subjects
REMOTE sensing ,HIGH resolution imaging ,DATA fusion (Statistics) ,IMAGE analysis ,SUPPORT vector machines - Abstract
Super-resolution image fusion aims to produce an image with finer spectral and spatial details than the input images. However, the super-resolution fusion is mainly applied to enhance a visual representation of the images and its potential benefits to the final thematic classification is an open question. In this paper, we present an experimental investigation of the remote sensing image classification performance in the case of the multi-sensor super-resolution image fusion. The research aims to compare classification performance obtained for the fused image and the low resolution input ones using different standard-of-the-art classifiers and feature extraction methods. Input data are supposed to be multispectral data obtained in visible and near infrared spectral ranges by the different remote sensing systems. To perform a multi-sensor super-resolution image fusion, we used a gradient-descent optimization approach with a B-TV regularization successfully adapted for remote sensing images with different spatial and spectral sampling characteristics by the authors of the paper. As for features, we applied brightness in spectral channels, attribute profiles and local feature attribute profiles. The classification was performed using support vector machines and random forest classifiers that have been proved to be very effective for remote sensing data classification. The experimental research included the multi-sensor input data simulation for four remote sensing systems, the super-resolution image fusion of all simulated images and the thematic classification of the fused image and the images obtained as an average input for each of the simulated imaging systems. The spatial resolution of the fused image was in 2, 3, 4 and 5 times better than the spatial resolution of the modeled input images. The average bandwidth of the fused image was 29 nm whereas for the input low resolution images it was in the range from 37 to 83 nm. Experimental results have shown that random forest classification is better to use with fusion, whereas support vector machines demonstrated better results without fusion. The feature extraction test showed that extended attribute profiles enhance the random forest classification accuracy of the fused image. Thus, the classification results have shown that super-resolution image fusion leads to the classification accuracy increase in the case of random forest classifier and there is no need to apply fusion in the case of support vector machines. [ABSTRACT FROM AUTHOR]
- Published
- 2020
30. Enhancing cold storage efficiency using image processing and IoT-enabled notifications.
- Author
-
Nagarale, Sanjiwani, Bora, Vibha, and Sonaskar, Sandeep
- Subjects
COLD storage ,IMAGE processing ,IMAGE analysis ,GAS detectors ,INTERNET of things - Abstract
Effective administration of cold storage facilities plays a significant responsibility in maintaining the quality of perishable items and reducing wastage within the supply chain. This study unveils a creative method that employs the combined pros of image processing, the Internet of Things (IoT), and a notification system to enhance the efficiency of cold storage procedures. The IoT framework enhances this system by enabling seamless communication between the cold storage framework and a central monitoring hub. The proposed paper mainly focuses on real-time monitoring of the cold storage environment and if there is any power outage notification will be sent to the person through SMS using GSM. The notification system is integrated to provide timely alerts to relevant stakeholders. In the current implementation of the project, the DHT11 Temperature and Humidity Sensor, the MQ-3 Ethylene gas sensor is integrated into a low-cost and Wi-Fi-enabled Node-MCU Microcontroller for Cold Storage monitoring purposes. The Node-MCU posts data to a cloud-based platform, where they are dealt with and scrutinized. Integration of image processing techniques assists real-time monitoring of cold storage environment. Cameras placed strategically within the storage units capture images of stored goods, enabling the system to analyze factors such as product quantity, placement, and quality. Advanced image analysis algorithms identify anomalies, spoilage, or potential issues, thus securing the upkeep of product integrity throughout the storage duration. This real-time responsiveness minimizes the risk of product spoilage and verifies adherence to regulatory standards. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
31. Construction of Curvelet Transform as an extension of Wavelet Transform.
- Author
-
Mane, Sachin L., Bhosale, Bharat N., and Shedge, Shubham D.
- Subjects
CURVELET transforms ,WAVELET transforms ,IMAGE analysis ,IMAGE processing ,SIGNAL processing - Abstract
Curvelet Transform typically offers significantly superior performance in image analysis, multi-resolution and multidi-rectional representation as compared to Wavelet Transform. This paper exploites strong relationship between Wavelet Transform and Curvelet Transform. Also we use mother Wavelet to construct Curvelet Transform as an extension of Wavelet Transform which has broad implications, particularly for the field of signal and image processing. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
32. Automated harvest collecting machine using machine learning.
- Author
-
Ramasendran, Narendran, Thiruchelvam, Vinesh, Cherskoy, Vladislav, Ravinchandra, Krishna, Raman, Thaneshvar Sri Ram, and Sivanesan, Siva Kumar
- Subjects
MACHINE learning ,IMAGE analysis ,HARVESTING machinery ,FRUIT ,AUTOMATION ,PALMS - Abstract
The research paper focuses on developing a custom machine learning model using Google's Teachable Machine for the classification of palm fruit images as healthy or rotten conditions. The study demonstrates successful integration of the customized model with a Raspberry Pi-based system for real-time object recognition. It achieved a remarkable 100% accuracy rate on a dedicated dataset for the palm-fruit classification. The seamless integration of the model allowed for live streaming image analysis, enabling the system to convey specific messages based on the identified fruit condition. Moreover, the work showcases the potential of custom machine learning models for real-time automation and decision-making applications. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
33. Binary classification of brain tumor using machine learning algorithms.
- Author
-
Dua, Saurabh, Chakravarthy, V. Deeban, and Sharma, Ishita
- Subjects
BRAIN tumors ,TUMOR classification ,IMAGE analysis ,BRAIN anatomy ,MACHINE learning - Abstract
Brain tumors and other cancers of the nervous system are among the primary causes of life's loss in today's world. The growth of abnormal cells in the brain can result in brain tumors, which are classified as either benign or malignant. While detecting brain tumors at an early stage is crucial for improving patient outcomes, it can be challenging due to the lack of early symptoms and the complexity of the brain's structure. Machine learning has emerged as a promising tool for detecting brain tumors accurately. In recent years, researchers have developed various machine learning models that utilize image analysis techniques to identify brain tumors based on CT and MRI scans. In this research paper, we compare the performance of several ML algorithms on a Brain Tumor Dataset to evaluate and analyze appropriate algorithm for correctly identifying the presence of a tumor in the brain. We analyzed the efficiency of the algorithms based on metrics such as sensitivity, specificity, accuracy, and false positive rate. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
34. Pneumonia identification and interpretation using convolutional neural network.
- Author
-
Sachdeva, Rohan, Banthia, Sumedha, and Madhavan, P.
- Subjects
CONVOLUTIONAL neural networks ,DEEP learning ,PNEUMONIA ,IMAGE analysis ,X-ray imaging ,HEBBIAN memory - Abstract
Pneumonia is a prevalent and severe infectious disease affecting millions worldwide. It is the most common cause of death, claiming more than four million lives yearly. Early detection of pneumonia is critical for prompt and effective treatment, which can significantly improve patient outcomes. In recent years, deep learning has now become a predominant tool for image analysis in the medical sector. This could significantly improve pneumonia detection's accuracy and efficiency, ultimately saving lives. The primary purpose of this paper is to propose a solution to this critical public health problem. In particular, we propose to develop a deep-learning system using X-ray images of the chest. The proposed model deals with the VGG16 architecture. It is trained on many X-rays, both with/without pneumonia of the chest, so the model can accurately distinguish between normal and infected lungs. The model can significantly improve the speed and accuracy of pneumonia detection, enabling medical professionals to diagnose and treat patients more effectively and efficiently. Ultimately, this could lead to more timely and accurate diagnoses that could help save lives and reduce the burden of pneumonia on individuals and communities. The model can classify pneumonia at 92.10% validation accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
35. An optimized deep learning model for fast and accurate brain tumor segmentation.
- Author
-
Agrawal, Vivek, Kaswan, Kuldeep Singh, and Kumar, Sanjay
- Subjects
DEEP learning ,BRAIN tumors ,CONVOLUTIONAL neural networks ,K-means clustering ,IMAGE analysis - Abstract
The proper segmentation of brain tumors is essential in medical image analysis field. Convolutional neural networks (CNNs), which make up the majority of deep learning approaches, have demonstrated encouraging results in automating this process. This research paper presents a novel two-step approach for brain segmenting and detecting tumors. In the first step, we employ a DenseNet121 model with transfer learning, fine-tuning the pre-trained weights obtained from the Image Net dataset, to recognize tumors in the brain. The second step involves utilizing a ResUnet model, which combines the strengths of ResNet50 and Unet models, for accurate segmentation. To validate our approach, we conduct experiments on "The Cancer Imaging account (TCIA)'s dataset "The Cancer Genome Atlas (TCGA)" and compare the results with other models of deep learning such as K-means clustering and VGG16. Our experimental findings demonstrate that our proposed approach achieves superior accuracy and efficiency compared to the alternative models. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
36. An extensive study on edge detection methods: A review.
- Author
-
Madhavi, Godi and Sathiya, R. D.
- Subjects
EDGE detection (Image processing) ,DIGITAL images ,IMAGE analysis - Abstract
An overview of the various methods and algorithms for edge detection in digital images is given in this survey paper. A fundamental issue in image processing is edge detection, which involves in locating the borders which separates various regions in an image. The survey starts with an explanation of edges and their importance in an image analysis, then reviews some of the most widely used edge detection techniques, including the canny edge detector, the Sobel operator, and the Laplacian of gaussian. The following section discusses some of the drawbacks and shortcomings of these techniques. A discussion of current trends and potential future directions in the field of edge detection rounds out the survey. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
37. Hyperspectral image anomaly detection.
- Author
-
Eqbal, Shahid, Rizvi, Aliya, Chandra, Astuti, and Singh, Anjali
- Subjects
ANOMALY detection (Computer security) ,INTRUSION detection systems (Computer security) ,RECEIVER operating characteristic curves ,ELECTROMAGNETIC fields ,IMAGE analysis ,ELECTROMAGNETIC spectrum ,HYPERSPECTRAL imaging systems ,IMAGE processing - Abstract
Hyperspectral image processing is a field of investigation for electromagnetic spectrum to obtain spectrum for each pixel inside the image of a scene with the reason of locating, figuring out and detecting the overall analysis of the image. Anomaly detection makes use of such bands that incorporates precise traits carefully associated with goal gadgets. This paper affords a take a look at on the detection of anomalies for Hyper Spectral pictures based on nicely-designed dictionaries: heritage dictionary and capability abnormality lexicon. The detection method used is "Joint sparse representation" (JSR)-based dictionary selection. This paper approaches for the quantitative analysis of the receiver operating characteristics of the different Hyperspectral data sets. For the quantitative evaluation of this data via the implemented method with the various other methods, the ROC curves with point sensible self-assurance intervals had been plotted. Low Rank Sparse Representation method adorns an amazing overall performance at the AVIRIS-1, AVIRIS-2 and HYDICE sets giving a qualitative comparison of 95.25% Experimental analysis pointed out to the matter of fact that detecting anomalies based on potential abnormality and historical past lexicon production is capable of attain advanced consequences in comparison with different winning strategies. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
38. A framework for privacy preserving medical content based image retrieval.
- Author
-
Kamrani, Abdelhalim, Zenkouar, Khalid, and Najah, Said
- Subjects
IMAGE retrieval ,MOMENTS method (Statistics) ,PRIVACY ,IMAGE analysis - Abstract
In this paper a general framework for a privacy preserving medical Content Based Image Retrieval (CBIR) is presented. We illustrate how a homomorphic encryption scheme can be used to implement a medical diagnose system while protecting the privacy of the patients. The fully homomorphic encryption presented by Gentry [17] allows for the computation of any features over an encrypted image. We make an attempt to exploit the robust capabilities of the theory of moments in image analysis in order to develop a robust diagnose system while maintaining the security aspect. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
39. Comparative analysis of dorsal palm vein pattern images in biometric identification problems.
- Author
-
Konnova, Natalia S. and Mizinov, Pavel V.
- Subjects
BIOMETRIC identification ,HUMAN fingerprints ,COVID-19 pandemic ,VEINS ,IMAGE analysis ,COMPARATIVE studies - Abstract
The use of biometrics extends to all possible areas of human activity. For example, identification, banking services, and even paying for public transport have all been made possible through biometric systems. Particular attention is given to biometric systems based on palm vein patterns. Several factors contributed to the popularity of this modality. First, its universality is inherent to the majority of people. The distinctiveness of an individual's vein pattern allows this parameter to be used as a unique identifier. Assume that the vein pattern does not charge much overtime, indicating the constancy of this modality. The biometric systems developed for vein pattern modality allow reading the user's parameters in a non-contact way, providing a high level of hygiene, which is especially important during the COVID-19 pandemic. The main advantage of this approach is to ensure the high security of the identification data. The venous pattern can only be seen in the near-infrared (NIR) range, making it very difficult to obtain biometric characteristics under normal circumstances since veins, unlike fingerprints, are protected by the skin. A literature survey showed that research attacks on biometric systems have been carried out when an attacker had full access to the database or created an artifact based on an image obtained by an infrared (IR) camera. This paper discusses the possibility of obtaining an image of the venous pattern in the dorsal part of the palm without using the IR camera. It conducts the comparative analysis of the acquired image with the standard produced in the IR range. Furthermore, the conclusions, based on the study, about using images obtained without the IR camera to manufacture artifacts for a presentation attack on the biometric systems are also presented. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
40. Immunohistochemistry BC image analysis: A review.
- Author
-
Razzaq, Hasanain H., Ghazali, Rozaida, and George, Loay E.
- Subjects
IMAGE analysis ,COMPUTER software ,IMMUNOHISTOCHEMISTRY ,MACHINE learning ,BREAST ,IMAGE intensifiers ,HUMAN error - Abstract
Breast cancer (BC) is the most prevalent type of cancer among women. However, it can be cured if detected in early stages and treated in proper way. As pathologists play the lead role in the whole process of diagnosis and prognosis, the process might include inaccuracy due to external factors such as interobserver and human error. Besides, in most places of the world there is lack of experienced pathologists and cancer experts. Therefore, computer programs can help extensively to compensate for these setbacks. Computer programs can be trained through Machine Learning (ML) algorithms in order to perform the scoring process on BC medical images such as histopathological and immunohistochemistry (IHC) images. IHC staining is a technique in which a set of biomarkers are used to detect the positive reaction to a certain type of treatment. By using whole slide imaging (WSI) for digitizing the IHC samples, the images can be prepared in digital form to be used as the entry to the computer programs. To provide an automated scoring program, researchers take advantage of image processing techniques combined with ML algorithms for segmentation and classification of positive cells based on the stain color and intensity. Since image quality highly rely on the laboratory equipment, operator's experience and other factors such as stain intensity, the programs may utilize image enhancement methods for more accurate segmentation and classification results. In this paper some of these methods are investigated based on preprocessing, segmentation, and classification steps for four biomarkers i.e., ER, PR, Her2, and Ki67. The results show that lack of IHC datasets and high computational time as well as complexity of implementation are the main obstacles that need to be investigated by researchers. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
41. The design and implementation of the seamless line displacement detection system.
- Author
-
Yifeng, Yang and Palaoag, Thelma D.
- Subjects
IMAGE recognition (Computer vision) ,IMAGE analysis ,CLASSIFICATION algorithms ,MACHINE learning ,JOINT use of railroad facilities ,RAILROAD safety measures ,INTRUSION detection systems (Computer security) - Abstract
China's urban railway network already covers 80 percent of the country. The support of a continuous line is inextricably linked to the rapid expansion of railways. The seamless line has a complex structure, a long working period, and is susceptible to environmental and other factors. This paper mainly studies the design and implementation of seamless line displacement data detection by using image recognition technology and machine learning technology. This topic trains and learns the digital content in the seamless line displacement scale through the machine learning SVM classification algorithm, and then identifies the scale mark and other data by image analysis to calculate the final displacement. In this way, the accuracy of the identified displacement data is very high. This study detects the displacement of the seamless line and gives early warning when the displacement exceeds the safety range. After receiving the early warning information, the management personnel can handle it in time to avoid danger. Some railway portions in Harbin have been subjected to the study. The research can assist railway management departments in detecting seamless line facilities and improving maintenance efficiency. At the same time, it can provide timely warnings of potentially risky information, reducing the likelihood of accidents. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
42. Image segmentation based on active contour in chest X-ray image.
- Author
-
Oktamuliani, Sri and Saijo, Yoshifumi
- Subjects
COMPUTER vision ,IMAGE analysis ,MEDICAL research ,LUNGS ,UNIVERSITY hospitals - Abstract
Chest X-ray (CXR) imaging is often used to diagnose pneumonia caused by COVID-19. The advantage of CXR is that it is cheap, fast, widespread, and uses less radiation. Studies of distinguishing object regions from one to another or image segmentation have been used in the medical field for further image analysis. This paper has proposed a method for segmenting lungregions using region-based active contour to measure the area segmented in CXR images. Active contour segmentation was performed using a computer vision image segmented in MATLAB. We assessed the CXR image in Andalas University Hospital and composed the data consisting of lung opacity (pneumonia) and regular. The result shows that, in both the left and right of the chest, the area in the pixel of the lung affected by COVID-19 is smaller than in the normal lung. In conclusion, this technique will benefit biomedical research investigating the regional lung. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
43. Advanced machine learning techniques for satellite image processing.
- Author
-
Kumaraswamy, Eelandula, Kommabatla, Mahender, Reddy, I. Rajasri, Karre, Ravikiran, Kasanagottu, Srinivas, and Ramu, Moola
- Subjects
REMOTE-sensing images ,IMAGE processing ,MACHINE learning ,IMAGE analysis ,REMOTE sensing ,HAZARD mitigation ,EARTHQUAKE resistant design ,DIGITAL image processing ,WILDFIRE prevention - Abstract
Satellite images mainly utilized in the events of a natural disaster management, identifying geographical information, viz land cover classes namely, buildings, roads, vegetation, water, agriculture land, crop types, plants, bare ground, cities, atmosphere conditions. Machine Learning (ML) approaches have been utilized effectively to develop a model for classification, detection, and segmentation tasks. Therefore, Satellite image processing and analysis purpose, ML techniques plays vital role and remotely sensed data become essential while training the model. The aim of this study is to investigate the various of ML techniques in satellite image analysis. However, to predict the various events in advance across the globe, it is necessary to focus more on remote sensed data and data processing techniques for accurate classification. Even though remote sensing quality has been increased and artificial intelligence solutions are equally increased. This paper addressed various types of advanced ML techniques utilized in the classification and assessment of satellite images and used to track the earthquakes, faulting, landslides, floodings, wildfire, and hazards associated with the stated activities. Still there is a gap and interference in the approaches and it is important to fill the gap by thorough review of recent classification approaches. In this connection it is necessary to look in depth to the state-of-the-art ML techniques of satellite image processing. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
44. Non-destructive quality control of apples based on discriminant analysis of their hyperspectral images.
- Author
-
Balabanov, P. V., Divin, A. G., Zhirkova, A. A., Lyubimova, D. A., and Egorov, A. S.
- Subjects
DISCRIMINANT analysis ,IMAGE analysis ,QUALITY control ,ORDER picking systems ,INDEPENDENT variables - Abstract
In this paper visible and near-infrared hyperspectral imaging (VIS-NIR) was used to classify apples during sorting. At the same time the initial data from the SPECIM hyperspectrometer were subjected to preliminary processing in order to determine the NDVI vegetation indices, etc., which played the role of independent variables (predictors) in the discriminant analysis. Experimental results show that the multiple discriminant image analysis method has great potential for identifying apple defects such as rot, scab, wormholes, as well as for sorting apples by quality. At the same time errors of the second kind during the rejection of defect-free apples amounted to less than 4%. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
45. Problematic issues in the organization of customs control related to the application of artificial intelligence methods.
- Author
-
Saidov, Abdusobir, Dustmukhamedov, Alisher, and Khakimova, Fazilat
- Subjects
ARTIFICIAL intelligence ,DIGITAL technology ,DECISION support systems ,X-ray imaging ,X-ray detection ,IMAGE analysis - Abstract
The paper considers the problem of using end-to-end digital technologies and platform solutions using elements of artificial intelligence. Identification of goods prohibited to be transported across the customs border is accepted as the main object of research. Problematic issues of unambiguous identification of prohibited goods at the border with the use of inspection and inspection complexes are formulated. The dual energy method for recognizing the materials of a controlled object by the effective atomic number and the mathematical foundations of a computed tomography are outlined. Recommendations on the implementation of the method of nonlinear inverse projection and nonlinear tomosynthesis at a small number of projection angles of the controlled object of the developed model in practice are given. Around the world, the time of customs clearance of foreign trade cargoes is one of the key criteria for assessing the effectiveness of the customs service. The use of end-to-end digital technologies and platform solutions using artificial intelligence elements and decision support systems will make it possible to carry out customs control "invisibly" and quickly throughout the entire chain of goods movement [1]. As a tool to speed up customs procedures, prevent and suppress offenses at the border, high-tech equipment is used, including large-sized inspection complexes (IC). Currently, the customs authorities of the Republic of Uzbekistan are equipped with modern IC, which allow you to get an x-ray image of the vehicle and the goods transported in it. However, the analysis of X-ray images and the detection of items prohibited for transportation in them requires high qualifications from a customs officer. Consequently, the task of studying the methods of artificial intelligence and recognition of goods prohibited for transport across the customs border is relevant. Therefore, the task of researching artificial intelligence methods and automatic recognition of goods prohibited for transportation across the customs border is relevant. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
46. Detection of diabetes using tongue image analysis.
- Author
-
Sridhar, B., Kumar, C. Ashok, and Anjaneyulu, P.
- Subjects
IMAGE analysis ,TONGUE ,DIABETES - Abstract
Tongue diagnosis is an essential part of diagnosing most illnesses, so tongue diagnosis has gained a lot of attention from experts. Tongue diagnosing is generally done by handling pictures of the tongue; however, this isn't done in Western medication. The objective of this paper is to overcome any barrier among Chinese and Western medication, just as improve the nature of division, so we proposed a consecutive strategy for preparing tongue pictures. The initial two kinds of quantitative highlights, chromatic and textural scales, are extricated from tongue pictures utilizing picture handling methods in the initial step of this system. The tongue's shape is extracted in the second shape detection process using an edge detector and an area increasing algorithm. The colour intensity extraction method is used to detect pimples and cracks in the third phase. The Level Set Algorithm produces excellent segmentation outcomes. Then the normal and abnormal tongue's mean and entropy values were compared. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
47. Comparison of conventional edge detection methods performance in lung segmentation of COVID19 patients.
- Author
-
Supriyanti, Retno, Wibowo, Farid FR, Ramadhani, Yogi, and Widodo, Haris B
- Subjects
LUNGS ,COVID-19 ,IMAGE segmentation ,IMAGE analysis ,IMAGE processing - Abstract
Digital image processing techniques have been widely used in the medical field, including in medical image analysis. One part of the image processing technique that plays an essential role in medical image analysis is image segmentation. This paper will discuss the performance of conventional edge detection, which consists of the Sobel, Canny, Prewitt, and Robert methods in segmenting the lungs, especially COVID19 patients. Based on the conventional edge detection method, we conducted a trial of measuring the lung area and the white patches contained therein. In addition, we compared the area between the lungs of normal patients and the lungs of Covid patients. The experimental results show that of the four types of conventional edge detection methods used, all of them have almost the same performance both in processing time and the results of the calculation of lung area obtained. Based on the experimental results, the conventional edge detection method can be considered for further development. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
48. USING SELF-PROPELLED SPRAYERS FOR THE TARGETED APPLICATION OF HERBICIDES.
- Author
-
Elbl, Jakub, Lukas, Vojtěch, Mezera, Jiří, Huňady, Igor, and Kintl, Antonín
- Subjects
IMAGE analysis ,DRONE aircraft ,MACHINE tools ,SPECTRAL imaging ,FIELD research - Abstract
The presented paper deals with the targeted application of pre-emergent herbicides. The field experiment was established in 2022 and 2023; two plots sized 26 ha and 30 ha were selected and the accuracy of the application was tested. There were local outbreaks of weeds on selected plots of land, which had to be eliminated before sowing the main crop - soybeans. Both plots were monitored using an unmanned aerial vehicle (UAV) - Mavic 3M with the multispectral camera. After the end of the monitoring, spectral analysis of images taken by UAV was made using the Pix4D software and vegetation indexes were calculated. The weed outbreaks were identified based on multispectral maps using the MagicTooll algorithm. The final application map was prepared in the QGIS program - two zones with and without herbicide application were defined. The application was carried out by the JD 4150i machine (9 sections per 30 m), the regulatory map was transferred to the machine terminal (4600) via the MyJD link. Subsequently, the entire application process was monitored and analysed using the MyJD programme. The accuracy of the application was analysed by comparing the prescription map and the actual dose applied. The accuracy of application was found to be significantly greater than 90%. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
49. EVALUATION OF THE QUALITY OF MATERIALS FOR PRODUCTION BY 3D PRINTING USING IMAGE ANALYSIS.
- Author
-
Gajewski, Kamil, Turlej, Tymoteusz, Zięba, Julia, and Wermiński, Bartosz
- Subjects
THREE-dimensional printing ,IMAGE analysis ,IMAGE quality analysis ,MICROSCOPY ,AVIATION medicine - Abstract
3D printing technology is used in many areas, especially in prototyping new parts and elements. Assessment of the quality of powders for the production of 3D printing filament is extremely important because it has a direct impact on the quality and reliability of printed objects. High-quality powders are essential to ensure optimal performance and print accuracy. In the case of 3D printing, filament is the basic material that is processed and layered to create three-dimensional objects. To ensure the quality and durability of printed items, the powders used to produce the filament must meet specific requirements. When evaluating the quality of powders, various factors such as physical, chemical and mechanical properties are taken into account. Powders should be of sufficient purity and uniformity to avoid inclusions or misprints. Mechanical properties such as strength, flexibility and abrasion resistance are also important, especially in the case of objects that are subjected to loads or forces. Evaluating the quality of powders also allows you to determine their compliance with specific industry standards and regulations. For specialist applications such as medicine or the aerospace industry, there are strict safety and quality requirements. Suitable powders must meet these standards to ensure the safety of end users and the effective functioning of printed parts. The paper presents the results of the use of optical microscopy techniques combined with image analysis to assess the quality of recycled materials for filament production. The results of grain size characteristics of powders using a grain size and size analyzer were presented. Using the analysis of processed images, it was classified whether a given recycled material has the appropriate parameters for use in the production of 3D printing. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
50. Defocused image restoration using Wiener and inverse filter in context of security application.
- Author
-
Goilkar, Suhasini S. and Yadav, Dinkar M.
- Subjects
IMAGE reconstruction ,IMAGE analysis ,IMAGE processing ,IMAGE stabilization ,TELEVISION in security systems ,PHOTOGRAPHIC lenses - Abstract
Many times, the use of image processing techniques becomes a vital tool in investigation processes of security applications. This paper elaborates an attempt made in image processing domain which is needed in investigation processes of security applications to achieve deblurring of images using Wiener and Inverse filters and the comparative study on the basis of image parameters. For accurate investigation and observations, improved quality of the defocused images can be obtained using the filtering process through which blur or noise can be removed. Such blur or noise gets induced in the captured images due to shaking of security camera while continuously moving to capture the area under surveillance, improper maintenance of such camera system or unlearned lenses of the camera. The improper illumination of the area under surveillance or the fast movement of the object itself also creates sometimes a blur in the captured images. In this work, two filters namely Wiener filter and Inverse filter are used to remove the noise and to restore the images after its quality improvement. Further to understand the quality improvement of the images, an analysis of restored images is done on the basis of image parameters. Finally, the obtained results are reported in tabulated form along with the graphs of original and restored image parameters. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.