245 results on '"Image texture"'
Search Results
2. Xizang Minzu University Reports Findings in Information and Data Encoding and Encryption (Medical Image Encryption using Biometric Image Texture Fusion)
- Subjects
Medical imaging equipment -- Safety and security measures -- Reports -- Research ,Computers - Abstract
2023 NOV 21 (VerticalNews) -- By a News Reporter-Staff News Editor at Information Technology Newsweekly -- New research on Information Technology - Information and Data Encoding and Encryption is the [...]
- Published
- 2023
3. SVD-based modeling for image texture classification using wavelet transformation
- Author
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Selvan, Srinivasan and Ramakrishnan, Srinivasan
- Subjects
Wavelet transforms -- Usage ,Object recognition (Computers) -- Research ,Pattern recognition -- Research ,Business ,Computers ,Electronics ,Electronics and electrical industries - Abstract
The article presents a new model for image texture classification based on wavelet transformation and singular value decomposition. The proposed model is capable of improving recognition rates using a lower number of parameters on large databases.
- Published
- 2007
4. Investigators from Wuhan University Release New Data on Computer Graphics (Self-supervised Monocular Depth Estimation Based On Image Texture Detail Enhancement)
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Graphics software ,Computers ,News, opinion and commentary - Abstract
2021 JUL 28 (VerticalNews) -- By a News Reporter-Staff News Editor at Computer Weekly News -- A new study on Computers - Computer Graphics is now available. According to news [...]
- Published
- 2021
5. Researchers at Harbin Engineering University Have Reported New Data on Image Processing (Graph Wavelet Transform for Image Texture Classification)
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Image processing -- Equipment and supplies -- Research ,Signal processing -- Research ,Digital signal processor ,Image processor ,Computers ,News, opinion and commentary - Abstract
2021 JUN 9 (VerticalNews) -- By a News Reporter-Staff News Editor at Computer Weekly News -- New research on Image Processing is the subject of a report. According to news [...]
- Published
- 2021
6. Dimension estimation of discrete-time fractional Brownian motion with applications to image texture classification
- Author
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Lui, Szu-Chu and Chang, Shyang
- Subjects
Brownian motion -- Research ,Electrical engineering -- Research ,Industrial engineering -- Mathematics ,Random noise theory -- Models ,Business ,Computers ,Electronics ,Electronics and electrical industries - Abstract
The Wold decomposition theorem was applied to discrete-time fractional Gaussian noise (DGFN) so that the spectral properties could be examined. The regular part of DFGN was noted and its PSD was carefully computed. A new fractal dimension estimator was developed from the relationship between the regular PSD versus frequency and the fractal dimension of DFGN. The proposed algorithm withstands amplitude scaling and shifting.
- Published
- 1997
7. Image texture synthesis-by-analysis using moving-average models
- Author
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Cadzow, James A., Wilkes, D. Mitchell, Peters, Richard Alan, III, and Xingkang Li
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Image processing -- Research ,Algorithms -- Usage ,Aerospace and defense industries ,Business ,Computers ,Electronics ,Electronics and electrical industries - Abstract
Texture synthesis is a necessary component of realistic scene generation. In particular, it is necessary for the simulation of image backgrounds for the testing of automatic target recognizers. We present a synthesis-by-analysis model for texture replication or simulation. This model can closely replicate a given textured image or produce another image which, although distinctly different from the original, has the same general visual characteristics and the same first and second-order gray-level statistics as the original image. In effect, such a synthetic image looks like a continuation of the original scene; as if another picture of the scene were taken adjacent to the original. The texture synthesis algorithm proposed herein contains three distinct components: A moving-average (MA) filter, a filter excitation function, and a gray-level histogram. The analysis portion of the texture synthesis algorithm derives the three from a given image. The synthesis portion convolves the MA filter kernel with the excitation function, adds noise, and modifies the histogram of the result. The advantages of this texture model over others include conceptually and computationally simple and robust parameter estimation, inherent stability, parsimony in the number of parameters, and synthesis through convolution. We 1) describe a procedure for deriving the correct MA kernel using a signal enhancement algorithm; 2) demonstrate the effectiveness of the model by using it to mimic several diverse textured images; 3) discuss its applicability to the problem of infrared background simulation; and 4) include detailed algorithms for the implementation of the model.
- Published
- 1993
8. Research Conducted at Wuhan University Has Updated Our Knowledge about Computer Graphics (Dense Multiview Stereo Based On Image Texture Enhancement)
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Graphics software ,Computers ,News, opinion and commentary - Abstract
2020 DEC 30 (VerticalNews) -- By a News Reporter-Staff News Editor at Computer Weekly News -- Current study results on Computers - Computer Graphics have been published. According to news [...]
- Published
- 2020
9. Findings from Cumhuriyet University in the Area of Molecular Graphics and Modelling Described (DNA sequence similarity analysis using image texture analysis based on first-order statistics)
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DNA sequencing -- Models -- Analysis -- Statistics -- Reports ,Base sequence -- Models -- Statistics -- Analysis -- Reports ,DNA -- Models -- Reports -- Analysis -- Statistics ,Genes ,Anopheles ,Protein structure prediction ,Editors ,Computational biology ,Computers ,News, opinion and commentary - Abstract
2020 JUN 3 (VerticalNews) -- By a News Reporter-Staff News Editor at Computer Weekly News -- Fresh data on Molecular Graphics and Modelling are presented in a new report. According [...]
- Published
- 2020
10. Studies from Shanghai University Have Provided New Data on Information Technology (A Sea-sky-line Detection Method Based On Gaussian Mixture Models and Image Texture Features)
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Gaussian processes -- Reports -- Methods -- Research -- Analysis ,Algorithms ,Technology ,Oceans ,Editors ,Computers - Abstract
2020 JAN 7 (VerticalNews) -- By a News Reporter-Staff News Editor at Information Technology Newsweekly -- Research findings on Information Technology are discussed in a new report. According to news [...]
- Published
- 2020
11. Reports from Hubei Polytechnic University Provide New Insights into Information Systems (Deep Learning In Drebin: Android Malware Image Texture Median Filter Analysis and Detection)
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Malware -- Analysis ,Image processing equipment ,Technology ,Image processing ,Editors ,Computers - Abstract
2019 AUG 27 (VerticalNews) -- By a News Reporter-Staff News Editor at Information Technology Newsweekly -- Researchers detail new data in Information Technology - Information Systems. According to news reporting [...]
- Published
- 2019
12. Patent Issued for Computer Implemented System And Method For Image Texture Determination (USPTO 10,255,685)
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Tata Consultancy Services Ltd. -- Intellectual property ,Consulting services -- Intellectual property -- Methods ,Management consultants ,Technology ,Editors ,Computers ,News, opinion and commentary - Abstract
2019 APR 24 (VerticalNews) -- By a News Reporter-Staff News Editor at Computer Weekly News -- Tata Consultancy Services Limited (Mumbai, India) has been issued patent number 10,255,685, according to [...]
- Published
- 2019
13. Patent Application Titled 'Method and System for 360 Degree Head-Mounted Display Monitoring between Software Program Modules Using Video Or Image Texture Sharing' Published Online (USPTO 20170295361)
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Video post production -- Methods -- Intellectual property ,Patents -- Methods ,Patent/copyright issue ,Computers ,News, opinion and commentary - Abstract
2017 NOV 1 (VerticalNews) -- By a News Reporter-Staff News Editor at Computer Weekly News -- According to news reporting originating from Washington, D.C., by VerticalNews journalists, a patent application [...]
- Published
- 2017
14. Patent Application Titled 'Computer Implemented System and Method for Image Texture Determination' Published Online (USPTO 20160284100)
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Tata Consultancy Services Ltd. -- Intellectual property ,Patents -- Methods ,Consulting services -- Intellectual property -- Methods ,Patent/copyright issue ,Computers ,News, opinion and commentary - Abstract
2016 OCT 19 (VerticalNews) -- By a News Reporter-Staff News Editor at Computer Weekly News -- According to news reporting originating from Washington, D.C., by VerticalNews journalists, a patent application [...]
- Published
- 2016
15. Research Conducted at International University Has Updated Our Knowledge about Liver Fibrosis (Liver fibrosis staging using CT image texture analysis and soft computing)
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Liver diseases -- Research ,Liver ,Fibrosis -- Research ,CAT scans ,Computers ,News, opinion and commentary - Abstract
By a News Reporter-Staff News Editor at Computer Weekly News -- Investigators publish new report on Gastroenterology. According to news reporting out of Antalya, Turkey, by VerticalNews editors, research stated, [...]
- Published
- 2014
16. Recent Data from N. Shrivastava and Co-Authors Highlight Findings in Computer Graphics (An effective scheme for image texture classification based on binary local structure pattern)
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Graphics software -- Analysis ,Computers ,News, opinion and commentary - Abstract
By a News Reporter-Staff News Editor at Computer Weekly News -- Data detailed on Computer Graphics have been presented. According to news reporting originating from Kolkata, India, by VerticalNews correspondents, [...]
- Published
- 2014
17. Texture Segmentation by Genetic Programming
- Author
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Andy Song and Vic Ciesielski
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Time Factors ,Computer science ,Feature extraction ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Scale-space segmentation ,Genetic programming ,Machine learning ,computer.software_genre ,Evolutionary computation ,Pattern Recognition, Automated ,Image texture ,Artificial Intelligence ,Image Processing, Computer-Assisted ,Computer Simulation ,Segmentation ,Vision, Ocular ,Models, Statistical ,Models, Genetic ,Contextual image classification ,Computers ,business.industry ,Computational Biology ,Pattern recognition ,Image segmentation ,Computational Mathematics ,Neural Networks, Computer ,Artificial intelligence ,business ,computer ,Algorithms ,Software - Abstract
This paper describes a texture segmentation method using genetic programming (GP), which is one of the most powerful evolutionary computation algorithms. By choosing an appropriate representation texture, classifiers can be evolved without computing texture features. Due to the absence of time-consuming feature extraction, the evolved classifiers enable the development of the proposed texture segmentation algorithm. This GP based method can achieve a segmentation speed that is significantly higher than that of conventional methods. This method does not require a human expert to manually construct models for texture feature extraction. In an analysis of the evolved classifiers, it can be seen that these GP classifiers are not arbitrary. Certain textural regularities are captured by these classifiers to discriminate different textures. GP has been shown in this study as a feasible and a powerful approach for texture classification and segmentation, which are generally considered as complex vision tasks.
- Published
- 2008
18. Multiresolution texture models for brain tumor segmentation in MRI
- Author
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J. Hossen, Shaheen Ahmed, and Khan M. Iftekharuddin
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Feature extraction ,Motion ,Image texture ,Cut ,Computer Graphics ,Image Processing, Computer-Assisted ,Medical imaging ,Humans ,Computer vision ,Segmentation ,AdaBoost ,Child ,Mathematics ,Principal Component Analysis ,Models, Statistical ,Contextual image classification ,Brain Neoplasms ,Computers ,business.industry ,Brain ,Pattern recognition ,Image segmentation ,Models, Theoretical ,Magnetic Resonance Imaging ,Fractals ,ROC Curve ,Multivariate Analysis ,Artificial intelligence ,business ,Algorithms ,Software - Abstract
In this study we discuss different types of texture features such as Fractal Dimension (FD) and Multifractional Brownian Motion (mBm) for estimating random structures and varying appearance of brain tissues and tumors in magnetic resonance images (MRI). We use different selection techniques including KullBack - Leibler Divergence (KLD) for ranking different texture and intensity features. We then exploit graph cut, self organizing maps (SOM) and expectation maximization (EM) techniques to fuse selected features for brain tumors segmentation in multimodality T1, T2, and FLAIR MRI. We use different similarity metrics to evaluate quality and robustness of these selected features for tumor segmentation in MRI for real pediatric patients. We also demonstrate a non-patient-specific automated tumor prediction scheme by using improved AdaBoost classification based on these image features.
- Published
- 2011
19. From Image to Data Using Common Image‐Processing Techniques
- Author
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Laura R. Sysko and Michael A. Davis
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Histology ,Computer science ,Feature extraction ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Top-hat transform ,Image processing ,Biochemistry ,Automation ,Image texture ,Digital image processing ,Image Processing, Computer-Assisted ,Computer vision ,Image Cytometry ,Feature detection (computer vision) ,Electronic Data Processing ,Microscopy ,Standard test image ,Computers ,business.industry ,Binary image ,General Medicine ,Models, Theoretical ,Medical Laboratory Technology ,Calibration ,Artificial intelligence ,business ,Algorithms ,Software - Abstract
A digital microscopy image is an array of number values, which with adequate contrast can be interpreted as spatial information. Through processing and analysis by mathematical means, using computer-assisted imaging software programs, raw image data contrast can be enhanced to improve the extraction of image features for measurement and analysis. This mathematical feature extraction (referred to as segmentation) provides the basis for general image processing. The methods discussed in this unit address common image analysis challenges such as object counting with touching objects, objects within other objects, and object identification in a field with uneven illumination or uneven brightness, along with step-by-step procedures for achieving these results.
- Published
- 2010
20. Texture-based computer-assisted diagnosis for fiberscopic images
- Author
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Stephan Rupp, Christian Winter, A Kage, Thomas Wittenberg, and Christian Münzenmayer
- Subjects
Ground truth ,Fiber (mathematics) ,business.industry ,Computer science ,Computers ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Reproducibility of Results ,Texture (geology) ,Pattern Recognition, Automated ,Image texture ,Computer-aided diagnosis ,Image Interpretation, Computer-Assisted ,Image Processing, Computer-Assisted ,Fiber Optic Technology ,Humans ,Computer vision ,Computer Simulation ,Artificial intelligence ,Diagnosis, Computer-Assisted ,Image sensor ,business ,Artifacts ,Algorithms ,Optical Fibers ,Software - Abstract
Flexible endoscopes based on fiber bundles are still widely used despite the recent success of so-called tipchip endoscopes. This is partly due to the costs and that for extremely thin diameters (below 3 mm) there are still only fiberscopes available. Due to the inevitable artifacts caused by the transition from the fiber bundles to the sensor chip, image and texture analysis algorithms are severely handicapped. Therefore, texture-based computer-assisted diagnosis (CAD) systems could not be used in such domains without image preprocessing. We describe a CAD system approach that includes an image filtering algorithm to remove the fiber image artifacts first and then applies conventional color texture algorithms that have been applied to other endoscopic disciplines in the past. The concept is evaluated on an image database with artificially rendered fiber artifacts so that ground truth information is available.
- Published
- 2009
21. Mammographic images segmentation using texture descriptors
- Author
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Wellington Pinheiro dos Santos, Carlos A. B. Mello, George D. C. Cavalcanti, and Angelica A. Mascaro
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Diagnostic Imaging ,Databases, Factual ,Computer science ,Local binary patterns ,Scale-space segmentation ,Breast Neoplasms ,Medical Oncology ,Pattern Recognition, Automated ,Breast cancer ,Image texture ,Histogram ,medicine ,Image Processing, Computer-Assisted ,Mammography ,Cluster Analysis ,Humans ,Segmentation ,Computer vision ,Breast ,Cluster analysis ,medicine.diagnostic_test ,business.industry ,Computers ,Pattern recognition ,Image segmentation ,medicine.disease ,Female ,Artificial intelligence ,business ,Algorithms ,Software - Abstract
Tissue classification in mammography can help the diagnosis of breast cancer by separating healthy tissue from lesions. We present herein the use of three texture descriptors for breast tissue segmentation purposes: the Sum Histogram, the Gray Level Co-Occurrence Matrix (GLCM) and the Local Binary Pattern (LBP). A modification of the LBP is also proposed for a better distinction of the tissues. In order to segment the image into its tissues, these descriptors are compared using a fidelity index and two clustering algorithms: k-Means and SOM (Self-Organizing Maps).
- Published
- 2009
22. Significance of MPEG-7 textural features for improved mass detection in mammography
- Author
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Georgia D. Tourassi, Nevine H. Eltonsy, Adel Elmaghraby, and Aleksey Fadeev
- Subjects
Feature extraction ,Breast Neoplasms ,Pattern Recognition, Automated ,Image texture ,Histogram ,medicine ,Mammography ,Humans ,Computer vision ,Diagnosis, Computer-Assisted ,Mathematics ,Sampling scheme ,Models, Statistical ,Receiver operating characteristic ,Artificial neural network ,medicine.diagnostic_test ,business.industry ,Computers ,Reproducibility of Results ,Pattern recognition ,ROC Curve ,Homogeneous ,Radiographic Image Interpretation, Computer-Assisted ,Programming Languages ,Artificial intelligence ,Neural Networks, Computer ,business ,Algorithms ,Software - Abstract
� Abstract—The purpose of the study is to investigate the significance of MPEG-7 textural features for improving the detection of masses in screening mammograms. The detection scheme was originally based on morphological directional neighborhood features extracted from mammographic regions of interest (ROIs). Receiver Operating Characteristics (ROC) was performed to evaluate the performance of each set of features independently and merged into a back-propagation artificial neural network (BPANN) using the leave-one-out sampling scheme (LOOSS). The study was based on a database of 668 mammographic ROIs (340 depicting cancer regions and 328 depicting normal parenchyma). Overall, the ROC area index of the BPANN using the directional morphological features was Az = 0.85 ± 0.01. The MPEG-7 edge histogram descriptor-based BPNN showed an ROC area index of Az = 0.71± 0.01 while homogeneous textural descriptors using 30 and 120 channels helped the BPNN achieve similar ROC area indexes of Az = 0.882 ± 0.02 and Az = 0.877 ± 0.01 respectively. After merging the MPEG-7 homogeneous textural features with the directional neighborhood features the performance of the BPANN increased providing an ROC area index of Az = 0.91 ± 0.01. MPEG-7 homogeneous textural descriptor significantly improved the morphology-based detection scheme.
- Published
- 2007
23. The Effects of Sampling Rate on the Texture Separability of Ultrasound Images
- Author
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Jenho Tsao, Men Tzung Lo, and Chao Wei Lai
- Subjects
Computer science ,Feature extraction ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Texture (music) ,Pattern Recognition, Automated ,Image texture ,Sampling (signal processing) ,Image Interpretation, Computer-Assisted ,Computer Graphics ,Image Processing, Computer-Assisted ,Humans ,Computer vision ,Ultrasonography ,Models, Statistical ,Computers ,business.industry ,Ultrasound ,Reproducibility of Results ,Pattern recognition ,Equipment Design ,Fibrosis ,Liver ,Feature (computer vision) ,Artificial intelligence ,business ,Algorithms ,Software - Abstract
Ultrasound machine has been a useful diagnosis tool for several decades, and many researches tried to use computerized algorithms to help physicians diagnose diseases according to the ultrasound texture patterns. However, the effects of sampling format and the sampling rate on the texture feature were not treated properly. In this paper, the authors try to evaluate the effects of the scan conversion done at imaging stage and the sampling rate used at the texture feature extraction stage. They demonstrate the indispensability of considering sampling format and sampling rate according to the feature used, and their proposed method would improve the separability of texture feature for coarse and homogeneous ultrasound images.
- Published
- 2006
24. Automated computer screening of chest radiographs for pneumoconiosis
- Author
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A F Turner, William B. Thompson, and Richard P. Kruger
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Male ,medicine.medical_specialty ,medicine.diagnostic_test ,Fourier Analysis ,business.industry ,Computers ,Radiography ,Pneumoconiosis ,General Medicine ,medicine.disease ,Coal Mining ,Pattern Recognition, Automated ,Image texture ,medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Radiology ,Diagnosis, Computer-Assisted ,Diagnostic screening ,business ,Chest radiograph - Abstract
The results of two complementary approaches for performing diagnostic screening for the presence of coal workers' pneumoconiosis (CWP) from the routine posterior-anterior chest radiograph are presented. The first is a digital approach utilizing the measurement of image texture, while the second uses hybrid optical-digital methods involving the optical Fourier transform. Both approaches yield classification results comparable to experienced radiologists.
- Published
- 1976
25. A thresholding method for automatic cell image segmentation
- Author
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H Borst, P Gais, and W Abmayr
- Subjects
Cell Nucleus ,Cytoplasm ,Histology ,business.industry ,Segmentation-based object categorization ,Computer science ,Computers ,Binary image ,Cytological Techniques ,Scale-space segmentation ,Epithelial Cells ,Image segmentation ,Thresholding ,Cell Compartmentation ,Image texture ,Histogram ,Humans ,Segmentation ,Computer vision ,Artificial intelligence ,Anatomy ,business - Abstract
An algorithm for automatic segmentation of PAP-stained cell images and its digital implementation is described. First, the image is filtered in order to eliminate the granularily and small objects in the image which may upset the segmentation procedure. In a second step, information on gradient and compactness is extracted from the filtered image and stored in three histograms as functions of the extinction. From these histograms, two extinction thresholds are computed. These thresholds are suitable to separate the nucleus from the cytoplasm, and the cytoplasm from the background in the filtered image. Masks are determined in this way, and finally used to analyse the nucleus and the cytoplasm in the original image.
- Published
- 1979
26. 基于 LTCP 特征的计算机生成图像鉴别算法.
- Author
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刘 淑 琴
- Subjects
PIXELS ,COMPUTERS ,ALGORITHMS ,TEXTURES ,IMAGE ,COLOR image processing - Abstract
Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She 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
- 2019
- Full Text
- View/download PDF
27. Color and texture analysis using emerging parallel architectures.
- Author
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Igual, Francisco D, Mayo, Rafael, Hartley, Timothy DR, Çatalyürek, Ümit V, Ruiz, Antonio, and Ujaldon, Manuel
- Subjects
COMPUTERS ,COLOR ,IMAGE analysis ,CENTRAL processing units ,GRAPHICS processing units - Abstract
While image texture is effective for use in pattern-recognition and image-analysis algorithms, textural features are time-consuming to calculate on standard CPUs. Therefore, we present novel implementations of textural-feature algorithms on graphics processors (GPUs), enabling fast color and texture analysis. Since different textural-feature calculations exhibit diverse characteristics, we focus on using general and algorithm-specific techniques to exploit the inherent parallelism and computational power of a GPU. Common operations required during the textural-feature pipeline range from streaming computations to recursive procedures, from arithmetically intensive transcendental functions to matrix operations. Some of these kernels are well-suited to GPUs, while others require considerable programming effort to fully exploit the memory hierarchy due to their memory-usage patterns. In this paper, different strategies for computing textural features on GPUs are compared with counterpart implementations on multicore CPUs, and experimental results show GPU results reaching a speedup of 500 times for certain operations. [ABSTRACT FROM PUBLISHER]
- Published
- 2011
- Full Text
- View/download PDF
28. E-resources on academic libraries in arts and science colleges affiliated to Manonmaniam Sundaranar University, Tirunelveli.
- Author
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Paneerselvam, Kalai Selvi, Kattari, Kannan, and Arasakumar, Ganesa Murthy
- Subjects
ACADEMIC libraries ,DIGITAL libraries ,LIBRARY science ,ART colleges ,ONLINE databases ,COMPUTERS - Abstract
The academic library meets the needs of end-users to provide an essential source of information and knowledge. Because of the availability of books data with changes and a vast library of the educational environment, is extracted with academic libraries, basic from books data of the knowledge library to promote the significant book to perform the Analysis is to improve the book of resources, and promote a better future of the decision to manage effectively. Electronic resources are defined as resources that require access and data collection of electronic products to the computer. A complete text-based reference text, electronic publications and collections, and other multi-media products, and training users access electronic resources. The previous system does not improve student skill and student performance in the academic library based on the knowledge. This method improves the student skill and student performance in the academic library, concentrating on the student knowledge. Then use the data collection, data-pre-processing, online database, Classification and finally, predict the result. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
29. Recognition Method for Earthquake-induced Building Damage from Unmanned-aerial-vehicle-based Images Using Bag of Words and Histogram Intersection Kernel Support Vector Machine.
- Author
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Ying Zhang, Hong-Mei Guo, Wen-Gang Yin, Zhen Zhao, Chang-Jiang Lu, and Yang-Yang Yu
- Subjects
EARTHQUAKE damage ,SUPPORT vector machines ,MACHINE learning ,HISTOGRAMS ,EARTHQUAKE magnitude ,DRONE aircraft ,COMPUTERS - Abstract
The commonly used artificial visual interpretation and existing object-oriented computer automatic recognition methods have some disadvantages, such as low efficiency and insufficient accuracy in recognizing earthquake-induced building damage in unmanned aerial vehicle (UAV)-based images. In this paper, we report the latest progress in research on machine learning algorithms in artificial intelligence, then propose a new method of recognizing earthquakeinduced building damage. Using the bag of words (BoW) model, scale-invariant feature transformation (SIFT) characteristics were clustered to build an eigenvector tag library with K clustering centers as visual words. After images were expressed by visual words as eigenvectors with unified dimensions, a histogram intersection kernel (HIK) was then employed to construct the histogram intersection kernel support vector machine (HIK-SVM) to classify images and recognize earthquake-induced building damage. Building damage due to the magnitude 6.0 earthquake that occurred in Luxian, Sichuan Province on September 16, 2021 was analyzed as an example. When the proposed method was applied to recognize earthquake damage using UAV-based images, the average recognition accuracy reached 91.7%. The experimental results verified the feasibility and validity of the proposed method. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
30. Application of Deep Learning in Histopathology Images of Breast Cancer: A Review.
- Author
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Zhao, Yue, Zhang, Jie, Hu, Dayu, Qu, Hui, Tian, Ye, and Cui, Xiaoyu
- Subjects
DEEP learning ,MACHINE learning ,BREAST imaging ,BREAST cancer ,COMPUTERS ,ARTIFICIAL intelligence - Abstract
With the development of artificial intelligence technology and computer hardware functions, deep learning algorithms have become a powerful auxiliary tool for medical image analysis. This study was an attempt to use statistical methods to analyze studies related to the detection, segmentation, and classification of breast cancer in pathological images. After an analysis of 107 articles on the application of deep learning to pathological images of breast cancer, this study is divided into three directions based on the types of results they report: detection, segmentation, and classification. We introduced and analyzed models that performed well in these three directions and summarized the related work from recent years. Based on the results obtained, the significant ability of deep learning in the application of breast cancer pathological images can be recognized. Furthermore, in the classification and detection of pathological images of breast cancer, the accuracy of deep learning algorithms has surpassed that of pathologists in certain circumstances. Our study provides a comprehensive review of the development of breast cancer pathological imaging-related research and provides reliable recommendations for the structure of deep learning network models in different application scenarios. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
31. Computer vision-based classification of concrete spall severity using metaheuristic-optimized Extreme Gradient Boosting Machine and Deep Convolutional Neural Network.
- Author
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Nguyen, Hieu and Hoang, Nhat-Duc
- Subjects
- *
CONVOLUTIONAL neural networks , *COMPUTER vision , *CONCRETE , *FEATURE extraction , *COMPUTERS , *METAHEURISTIC algorithms - Abstract
This paper presents alternative solutions for classifying concrete spall severity based on computer vision approaches. Extreme Gradient Boosting Machine (XGBoost) and Deep Convolutional Neural Network (DCNN) are employed for categorizing image samples into two classes: shallow spall and deep spall. To delineate the properties of a concrete surface subject to spall, texture descriptors including local binary pattern, center symmetric local binary pattern, local ternary pattern, and attractive repulsive center symmetric local binary pattern (ARCS-LBP) are employed as feature extraction methods. In addition, the prediction performance of XGBoost is enhanced by Aquila optimizer metaheuristic. Meanwhile, DCNN is capable of performing image classification directly without the need for texture descriptors. Experimental results with a dataset containing real-world concrete surface images and 20 independent model evaluations point out that the XGBoost optimized by the Aquila metaheuristic and used with ARCS-LBP has achieved an outstanding classification performance with a classification accuracy rate of roughly 99%. • Propose a computer vision method for classifying concrete spall severity. • Extreme Gradient Boosting Machine and deep neural networks are employed. • Use local binary pattern variants as image texture descriptors. • The boosting machine is finetuned by the Aquila optimizer. • The proposed method achieves a high classification accuracy rate = 0.99. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
32. A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches.
- Author
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Li, Xintong, Li, Chen, Rahaman, Md Mamunur, Sun, Hongzan, Li, Xiaoqi, Wu, Jian, Yao, Yudong, and Grzegorzek, Marcin
- Subjects
IMAGE analysis ,COMPUTERS ,COMPUTER vision ,COMPUTER-aided diagnosis ,COMPUTER algorithms ,FEATURE extraction ,IMAGE segmentation - Abstract
With the development of Computer-aided Diagnosis (CAD) and image scanning techniques, Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis. Therefore, WSI analysis has become the key to modern digital histopathology. Since 2004, WSI has been used widely in CAD. Since machine vision methods are usually based on semi-automatic or fully automatic computer algorithms, they are highly efficient and labor-saving. The combination of WSI and CAD technologies for segmentation, classification, and detection helps histopathologists to obtain more stable and quantitative results with minimum labor costs and improved diagnosis objectivity. This paper reviews the methods of WSI analysis based on machine learning. Firstly, the development status of WSI and CAD methods are introduced. Secondly, we discuss publicly available WSI datasets and evaluation metrics for segmentation, classification, and detection tasks. Then, the latest development of machine learning techniques in WSI segmentation, classification, and detection are reviewed. Finally, the existing methods are studied, and the application prospects of the methods in this field are forecasted. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
33. PCB Component Detection Using Computer Vision for Hardware Assurance.
- Author
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Zhao, Wenwei, Gurudu, Suprith Reddy, Taheri, Shayan, Ghosh, Shajib, Mallaiyan Sathiaseelan, Mukhil Azhagan, and Asadizanjani, Navid
- Subjects
PRINTED circuit design ,COMPUTERS ,COMPUTER vision ,AUTOMATIC optical inspection ,IMAGE processing ,MACHINE learning ,PRINTED circuits - Abstract
Printed circuit board (PCB) assurance in the optical domain is a crucial field of study. Though there are many existing PCB assurance methods using image processing, computer vision (CV), and machine learning (ML), the PCB field is complex and increasingly evolving, so new techniques are required to overcome the emerging problems. Existing ML-based methods outperform traditional CV methods; however, they often require more data, have low explainability, and can be difficult to adapt when a new technology arises. To overcome these challenges, CV methods can be used in tandem with ML methods. In particular, human-interpretable CV algorithms such as those that extract color, shape, and texture features increase PCB assurance explainability. This allows for incorporation of prior knowledge, which effectively reduces the number of trainable ML parameters and, thus, the amount of data needed to achieve high accuracy when training or retraining an ML model. Hence, this study explores the benefits and limitations of a variety of common computer vision-based features for the task of PCB component detection. The study results indicate that color features demonstrate promising performance for PCB component detection. The purpose of this paper is to facilitate collaboration between the hardware assurance, computer vision, and machine learning communities. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
34. Deepfake Video Detection Based on MesoNet with Preprocessing Module.
- Author
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Xia, Zhiming, Qiao, Tong, Xu, Ming, Wu, Xiaoshuai, Han, Li, and Chen, Yunzhi
- Subjects
DEEPFAKES ,COMPUTERS ,DEEP learning ,SOCIAL media - Abstract
With the development of computer hardware and deep learning, face manipulation videos represented by Deepfake have been widely spread on social media. From the perspective of symmetry, many forensics methods have been raised, while most detection performance might drop under compression attacks. To solve this robustness issue, this paper proposes a Deepfake video detection method based on MesoNet with preprocessing module. First, the preprocessing module is established to preprocess the cropped face images, which increases the discrimination among multi-color channels. Next, the preprocessed images are fed into the classic MesoNet. The detection performance of proposed method is verified on two datasets; the AUC on FaceForensics++ can reach 0.974, and it can reach 0.943 on Celeb-DF which is better than the current methods. More importantly, even in the case of heavy compression, the detection rate can still be more than 88%. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
35. Big Data Image Processing Based on Coefficient 3D Reconstruction Model.
- Author
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Sun, Hujun
- Subjects
BIG data ,IMAGE processing ,ELECTRONIC data processing ,COMPUTERS ,DATA mining ,NONRELATIONAL databases ,COMPUTER equipment - Abstract
In the history of human society, information is an indispensable part of development, and extracting useful data from the huge amount of data can effectively solve some real-life problems. At the same time, with the continuous improvement of modern technology and computer hardware and equipment, the demand for information subjects is getting higher and higher. The field of human research is gradually moving in the direction of multiscientific exploration and deepening of related work. The related information disciplines have been studied by more scholars and experts in the field to a greater extent and put forward more demanding theories and methods. In the process of human social development, things are constantly changing and updating, which makes data mining technology more and more attention. This article first describes the relevant basic theoretical knowledge of 3D reconstruction, and secondly, it analyzes the big data platform technology, which mainly includes the analysis of the big data platform architecture, the description of the Hadoop distributed architecture, and the analysis of the HBase nonrelational database. Finally, it studies the video image feature extraction technology of video content and uses this to study the design of a big data image processing platform for 3D reconstruction models. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
36. Benchmarking Various Radiomic Toolkit Features While Applying the Image Biomarker Standardization Initiative toward Clinical Translation of Radiomic Analysis.
- Author
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Lei, Mingxi, Varghese, Bino, Hwang, Darryl, Cen, Steven, Lei, Xiaomeng, Desai, Bhushan, Azadikhah, Afshin, Oberai, Assad, and Duddalwar, Vinay
- Subjects
BIOMARKERS ,COMPUTER software ,COMPUTERS ,RADIOGRAPHY ,BENCHMARKING (Management) ,INTER-observer reliability ,DIAGNOSTIC imaging ,WORKFLOW ,DIGITAL diagnostic imaging - Abstract
The image biomarkers standardization initiative (IBSI) was formed to address the standardization of extraction of quantifiable imaging metrics. Despite its effort, there remains a lack of consensus or established guidelines regarding radiomic feature terminology, the underlying mathematics and their implementation across various software programs. This creates a scenario where features extracted using different toolboxes cannot be used to build or validate the same model leading to a non-generalization of radiomic results. In this study, IBSI-established phantom and benchmark values were used to compare the variation of the radiomic features while using 6 publicly available software programs and 1 in-house radiomics pipeline. All IBSI-standardized features (11 classes, 173 in total) were extracted. The relative differences between the extracted feature values from the different software programs and the IBSI benchmark values were calculated to measure the inter-software agreement. To better understand the variations, features are further grouped into 3 categories according to their properties: 1) morphology, 2) statistic/histogram and 3)texture features. While a good agreement was observed for a majority of radiomics features across the various tested programs, relatively poor agreement was observed for morphology features. Significant differences were also found in programs that use different gray-level discretization approaches. Since these software programs do not include all IBSI features, the level of quantitative assessment for each category was analyzed using Venn and UpSet diagrams and quantified using two ad hoc metrics. Morphology features earned lowest scores for both metrics, indicating that morphological features are not consistently evaluated among software programs. We conclude that radiomic features calculated using different software programs may not be interchangeable. Further studies are needed to standardize the workflow of radiomic feature extraction. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
37. Computer aid screening of COVID-19 using X-ray and CT scan images: An inner comparison.
- Author
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Sethy, Prabira Kumar, Behera, Santi Kumari, Anitha, Komma, Pandey, Chanki, and Khan, M.R.
- Subjects
COMPUTED tomography ,COVID-19 ,X-ray imaging ,X-rays ,MACHINE learning ,DEEP learning ,COMPUTERS ,FEATURE extraction - Abstract
The objective of this study is to conduct a critical analysis to investigate and compare a group of computer aid screening methods of COVID-19 using chest X-ray images and computed tomography (CT) images. The computer aid screening method includes deep feature extraction, transfer learning, and machine learning image classification approach. The deep feature extraction and transfer learning method considered 13 pre-trained CNN models. The machine learning approach includes three sets of handcrafted features and three classifiers. The pre-trained CNN models include AlexNet, GoogleNet, VGG16, VGG19, Densenet201, Resnet18, Resnet50, Resnet101, Inceptionv3, Inceptionresnetv2, Xception, MobileNetv2 and ShuffleNet. The handcrafted features are GLCM, LBP & HOG, and machine learning based classifiers are KNN, SVM & Naive Bayes. In addition, the different paradigms of classifiers are also analyzed. Overall, the comparative analysis is carried out in 65 classification models, i.e., 13 in deep feature extraction, 13 in transfer learning, and 39 in the machine learning approaches. Finally, all classification models perform better when applying to the chest X-ray image set as comparing to the use of CT scan image set. Among 65 classification models, the VGG19 with SVM achieved the highest accuracy of 99.81%when applying to the chest X-ray images. In conclusion, the findings of this analysis study are beneficial for the researchers who are working towards designing computer aid tools for screening COVID-19 infection diseases. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
38. Radiology and Enterprise Medical Imaging Extensions (REMIX).
- Author
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Erdal, Barbaros S., Prevedello, Luciano M., Qian, Songyue, Demirer, Mutlu, Little, Kevin, Ryu, John, O’Donnell, Thomas, and White, Richard D.
- Subjects
ACADEMIC medical centers ,BUSINESS intelligence ,COMPUTED tomography ,COMPUTER software ,COMPUTERS ,DATABASE management ,DIAGNOSTIC imaging ,DIGITAL image processing ,INFORMATION retrieval ,MEDICAL ethics ,PICTURE archiving & communication systems ,PRIVACY - Abstract
Radiology and Enterprise Medical Imaging Extensions (REMIX) is a platform originally designed to both support the medical imaging-driven clinical and clinical research operational needs of Department of Radiology of The Ohio State University Wexner Medical Center. REMIX accommodates the storage and handling of “big imaging data,” as needed for large multi-disciplinary cancer-focused programs. The evolving REMIX platform contains an array of integrated tools/software packages for the following: (1) server and storage management; (2) image reconstruction; (3) digital pathology; (4) de-identification; (5) business intelligence; (6) texture analysis; and (7) artificial intelligence. These capabilities, along with documentation and guidance, explaining how to interact with a commercial system (e.g., PACS, EHR, commercial database) that currently exists in clinical environments, are to be made freely available. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
39. Face recognition technology in classroom environment based on ResNet neural network.
- Author
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Fang, Jian, Lin, Xiaomei, Tian, Jia, and Wu, Yue
- Subjects
HUMAN facial recognition software ,FACE perception ,CLASSROOM environment ,CONVOLUTIONAL neural networks ,COMPUTERS ,FACE ,FEATURE extraction - Abstract
With the rapid development of electronic computers and information technology, face recognition is widely used in fields such as enterprises, entertainment, information security, and daily life. However, the current face recognition technology is still relatively poor in distinguishing facial features, resulting in a low accuracy of face recognition, which cannot meet increasing application requirements. For this reason, it is necessary to develop more accurate face recognition technology. Residual neural network (ResNet) neural network is a deep residual learning convolutional neural network, which can be used to solve the degeneration problem (i.e., after adding more layers to the neural network, the performance drops rapidly) of deep-learning neural networks. We aim to study the specific principles, calculation methods, and characteristics of the ResNet neural network, analyze the complexity of the classroom environment, propose the use of ResNet network for face feature extraction, and use additive angular margin loss for deep face recognition (ArcFace) as the loss function of the ResNet network to improve the ResNet network. Because the ArcFace function has the advantages of high performance, easy programming, low complexity and high training efficiency. We use this scheme to conduct field tests of face recognition on many students in the classroom. The test results show that this scheme enhances the discrimination of facial features and improves the accuracy of face recognition. In the case of different numbers of people, facial defects, and strong light exposure, the system can still detect and recognize faces stably. In the case of facial defects, the recognition accuracy rate is still 63.3%, and the recognition accuracy rate is still more than 60% under the illumination of strong light, and the recognition accuracy rate under correct conditions is more than 70%. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
40. Is there functional vascular information in anatomical MR sequences? A preliminary in vivo study
- Author
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Winzenrieth, Renaud, Claude, Isabelle, Hobatho, Marie-Christine, and Sebag, Guy
- Subjects
Legg-Calve-Perthes disease -- Research ,Magnetic resonance imaging -- Analysis ,Blood circulation disorders -- Diagnosis ,Biological sciences ,Business ,Computers ,Health care industry - Abstract
According to the type of sequences used, either morphological or dynamic functional study can be performed using magnetic resonance imaging (MRI). The aim of this study is to find out if vascular information found, in dynamic MR sequences, already exists in anatomical MR sequences in the particular case of Legg-Calve-Perthes disease (LCPD). LCPD is due to a loss of circulation to the femoral head in a growing child resulting in avascular necrosis and leading to possible distortion of size and shape of the proximal lemur. MRI acquisitions consist in performing two anatomical sequences and one dynamic sequence with a gadolinium-diethylenetriamiue pentaacetic acid (Gd-DTPA) injection. Five new parametric images characterizing hyper-and hypo-vascularized areas are computed from the dynamic MR sequence. For each new image, the two corresponding anatomical images are found and registered. Then, four types of regions of interest (ROIs) are extracted: healthy hyper-and hypo-vascularized areas and pathological hyper-and hypo-vascularized areas. First-order statistical parameters and texture parameters (Haraliek's method, run length method, fractal parameters, autoregressive factors and Laws' texture energy method) are computed in each ROI. Then, a statistical study based on a T test is performed. Results show that some parameters could discriminate the four ROI types. Hence, dynamic vascular image and intrinsic anatomical image characteristics seem to be correlated. Finally, the disease can be evaluated with objective parameters using only anatomical sequences. Index Terms--Image texture analysis, Legg-Calve-Perthe disease, magnetic resonance imaging, vascular imaging.
- Published
- 2006
41. 3D iterative tomographic reconstruction using graphics processors.
- Author
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Zolotarev, S. and Vengrinovich, V.
- Subjects
COMPUTER algorithms ,ONLINE algorithms ,COMPUTER programming ,GRAPHICS processing units ,COMPUTER graphics equipment ,SUPERCOMPUTERS ,COMPUTERS ,CENTRAL processing units - Abstract
An original parallel computional algorithm is developed for 3D tomographic reconstruction in a conic beam based on graphics processors. [ABSTRACT FROM AUTHOR]
- Published
- 2009
- Full Text
- View/download PDF
42. Computer-aided image analysis of focal hepatic lesions in ultrasonography: preliminary results.
- Author
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Se Hyung Kim, Jeong Min Lee, Kwang Gi Kim, Jong Hyo Kim, Jae Young Lee, Joon Koo Han, Byung Ihn Choi, Kim, Se Hyung, Lee, Jeong Min, Kim, Kwang Gi, Kim, Jong Hyo, Lee, Jae Young, Han, Joon Koo, and Choi, Byung Ihn
- Subjects
COMPUTER-aided design ,LIVER diseases ,RADIOLOGISTS ,ULTRASONIC imaging ,MEDICAL imaging systems - Abstract
Purpose: To develop a computer-aided image analysis (CAIA) algorithm for analyzing US features of focal hepatic lesions and to correlate the feature values of CAIA with radiologists' grading.Materials and Methods: Two abdominal radiologists, blinded to the final diagnosis, independently evaluated sonographic images of 51 focal hepatic lesions in 47 patients: hemangiomas (n = 19), hepatic simple cysts or cystic lesions (n = 14), hepatocellular carcinoma (n = 11), metastases (n = 6), and focal fat deposition (n = 1). All images were graded using a 3- to 5-point scale, in terms of border (roundness, sharpness, and the presence of peripheral rim), texture (echogenicity, homogeneity, and internal artifact), posterior enhancement, and lesion conspicuity. Using a CAIA, texture and morphological parameters representing radiologists' subjective evaluations were extracted. Correlations between the radiologists and the CAIA for assessing parameters in corresponding categories were computed by means of weighted kappa statistics and Spearman correlation test.Results: A good agreement was achieved between CAIA and radiologists for grading echogenicity (weighted kappa = 0.675) and the presence of hyper- or hypoechoic rim (weighted kappa = 0.743). Several CAIA-derived features representing homogeneity of the lesions showed good correlations (correlation coefficient (gamma) = 0.603 approximately 0.641) with radiologists' grading (P < 0.05). For internal artifact (gamma = 0.469-0.490) and posterior enhancement (gamma = -0.516) of the cyst and lesion conspicuity (gamma = 0.410), a fair correlation between CAIA and radiologists was obtained (P < 0.05). However, parameters representing lesions' border such as sharpness (gamma = 0.252-0.299) and roundness (gamma = -0.134-0.163) showed no significant correlation (P > 0.05).Conclusion: As a preliminary step in US computer-aided diagnosis for focal hepatic lesions, a CAIA algorithm was constructed with a good agreement and correlation with human observers in most US features. In addition, these features should be weighted highly when a computer-aided diagnosis for characterizing focal liver lesions on US is designed and developed. [ABSTRACT FROM AUTHOR]- Published
- 2009
- Full Text
- View/download PDF
43. Computer analysis of surface cracks in structural elements.
- Author
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Yasnii, P., Marushchak, P., Konovalenko, I., and Bishchak, R.
- Subjects
IMAGE processing ,COMPUTERS ,SURFACE defects ,DENSITY functionals ,LEAST squares - Abstract
We propose a method of computer processing of images of cracks. To discern cracks on an image, a segmentation method which includes an analysis of the brightness density function and binary transformation is used. To determine orientations of a crack, we calculate the locus of points of its skeleton with their subsequent approximation by the method of least squares. As a measure of damage, the area of cracks within the boundaries of the analyzed region was used. We investigate images on the surface of a roll of a continuous billet casting machine. By the moment the roll was removed from service, it had been operated in 4500 heats (the weight of which was 350 t). The cause of its removal from service was the cracking of its working surface. [ABSTRACT FROM AUTHOR]
- Published
- 2008
- Full Text
- View/download PDF
44. Histogram feature-based Fisher linear discriminant for face detection.
- Author
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Haijing Wang, Peihua Li, and Tianwen Zhang
- Subjects
ARTIFICIAL neural networks ,ESTIMATION theory ,COMPUTER science ,COMPUTERS ,RESEARCH - Abstract
The face pattern is described by pairs of template-based histogram and Fisher projection orientation under the framework of AdaBoost learning in this paper. We assume that a set of templates are available first. To avoid making strong assumptions about distributional structure while still retaining good properties for estimation, the classical statistical model, histogram, is used to summarize the response of each template. By introducing a novel “Integral Histogram Image”, we can compute histogram rapidly. Then, we turn to Fisher linear discriminant for each template to project histogram from d-dimensional subspace to one-dimensional subspace. Best features, used to describe face pattern, are selected by AdaBoost learning. The results of experiments demonstrate that the selected features are much more powerful to represent the face pattern than the simple rectangle features used by Viola and Jones and some variants. [ABSTRACT FROM AUTHOR]
- Published
- 2008
- Full Text
- View/download PDF
45. Accelerating Popular Tomographic Reconstruction Algorithms on Commodity PC Graphics Hardware.
- Author
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Fang Xu and Mueller, Klaus
- Subjects
APPLICATION-specific integrated circuits ,COMPUTER graphics ,COMPUTERS ,DIGITAL image processing ,ELECTRONIC circuits ,REDUCED instruction set computers - Abstract
The task of reconstructing an object from its projections via tomographic methods is a time-consuming process due to the vast complexity of the data. For this reason, manufacturers of equipment for medical computed tomography (CT) rely mostly on special application specified integrated circuits (ASICs) to obtain the fast reconstruction times required in clinical settings. Although modern CPUs have gained sufficient power in recent years to be competitive for two-dimensional (2D) reconstruction, this is not the case for three-dimensional (3D) reconstructions, especially not when iterative algorithms must be applied. The recent evolution of commodity PC computer graphics boards (GPUs) has the potential to change this picture in a very dramatic way. In this paper we will show how the new floating point GPUs can be exploited to perform both analytical and iterative reconstruction from X-ray and functional imaging data. For this purpose, we decompose three popular three-dimensional (3D) reconstruction algorithms (Feldkamp filtered backprojection, the simultaneous algebraic reconstruction technique, and expectation maximization) into a common set of base modules, which all can be executed on the GPU and their output linked internally. Visualization of the reconstructed object is easily achieved since the object already resides in the graphics hardware, allowing one to run a visualization module at any time to view the reconstruction results. Our implementation allows speedups of over an order of magnitude with respect to CPU implementations, at comparable image quality. [ABSTRACT FROM AUTHOR]
- Published
- 2005
- Full Text
- View/download PDF
46. A hybrid-based texture synthesis approach.
- Author
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Chang-Hsing Wu, Yueh-Yi Lai, and Wen-Kai Tai
- Subjects
COMPUTER graphics ,TEXTURES ,COMPUTERS - Abstract
Texture synthesis has been an active research area in computer graphics, vision, and image processing. A hybrid texture synthesis approach that combines the strength of Ashikhmin?s [1] and Liang?s [11] algorithms is presented in this paper. Using patches from the input sample texture to extend the input itself, more suitable valid candidate pixels in the input sample can be more precisely determined. An extra original position array is used to record the valid pixel position in the extended region of input sample texture such that the candidate pixel can be retrieved efficiently. Pasting patches with more global information of texture features to initialize the output texture, the texture synthesis process tends to grow larger pieces in the output texture so that the perceptual similarity is reserved as much as possible. Many classes of textures have been used to test our approach. As the experimental results showed, the proposed hybrid approach effectively and efficiently synthesizes high-quality textures from a wide variety of textures. [ABSTRACT FROM AUTHOR]
- Published
- 2004
47. Object-based coding of stereo image sequences using three-dimensional models
- Author
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Malassiotis, Sotiris and Strintzis, Michael Gerassimos
- Subjects
Image coding -- Research ,Three-dimensional graphics -- Models ,Business ,Computers ,Electronics ,Electronics and electrical industries - Abstract
An object-based stereo image coding algorithm is proposed. The algorithm relies on modeling of the object structure using 3-D wire-frame models and motion estimation using globally rigid and locally deformable motion models. Algorithms for the estimation of motion and structure parameters from stereo images are described. Motion parameters are used to construct predicted images by mapping the image texture on the object surface. Coding of object parameters, appearing background regions, and prediction errors are investigated, and experimental results with standard stereo image sequences depicting general scenes are presented. The proposed algorithm is seen to be very efficient for applications such as stereoscopic video transmission. Furthermore, it is especially well suited to advanced applications such as generation and transmission of intermediate views for multiview receiver systems and applications in which objectwise editing of the bit stream is required. The latter include video production using preanalyzed scenes and virtual reality applications. Index Terms - 3-D models, object-based coding, stereo sequences.
- Published
- 1997
48. Coding of dynamic texture for mapping on 3-D scenes.
- Author
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DeKnuydt, Bert, Desmet, Stef, and Van Eycken, Luc
- Subjects
- *
THREE-dimensional imaging , *DATA compression , *MATHEMATICAL models , *DATA transmission systems , *SIMULATION methods & models - Abstract
As the availability of powerful 3-D scene renderers grows, the demand for high visual quality 3-D scenes is increasing. Besides more detailed geometric and texture information, this presupposes the ability to map dynamic textures. This is obviously needed to model movies, computers, and TV screens but also, for example, for the landscape as seen from inside a moving vehicle or shadow and lighting effects that are not modeled separately. Downloading the complete scene to the user, before letting him interact with the scene, becomes very impractical and inefficient with huge scenes. If, as is often the case, a back channel is available, on-demand downloading allows the user to start interacting with the scene immediately. Specifically for dynamic texture, if we know the viewpoint of the user (or several users), we can code the texture taking into account the viewing conditions, i.e., coding and transmitting each part of the texture with the required resolution only. Applications that would benefit from view-dependent coding of dynamic textures include (but are not limited to) multiplayer three-dimensional (3-D) games, walkthroughs of dynamic constructions or scenes, and 3-D simulations of dynamic systems. In this paper, the feasibility of such a scheme based on an adapted optimal level allocation video codec is shown together with the huge data-rate reductions that can be achieved with it [ABSTRACT FROM PUBLISHER]
- Published
- 1999
- Full Text
- View/download PDF
49. The Influence of Different Segmentation Methods on the Extraction of Imaging Histological Features of Hepatocellular Carcinoma CT.
- Author
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Zhao, Sen, Ren, Wenyan, Zhuang, Yan, and Wang, Zhixue
- Subjects
CLINICAL pathology equipment ,COMPUTED tomography ,COMPUTER software ,COMPUTERS ,HEPATOCELLULAR carcinoma ,LIVER tumors ,THREE-dimensional imaging ,DIAGNOSIS - Abstract
In order to analyze the influence of different segmentation techniques on hepatocellular carcinoma (HCC) CT (Computed Tomography) imaging histological feature extraction, Grow Cut method and Graph Cut method are used to segment hepatocellular carcinoma from arterial CT images of HCC patients, and the stability and repeatability of imaging histological features are studied. Meanwhile, hierarchical clustering method is used to reduce the redundancy of features. The results show that the repeatability and redundancy mainly depend on the method of tumor segmentation. Semi-automatic segmentation method can improve the repeatability of image features, and hierarchical clustering can reduce the redundancy of features. Different segmentation techniques have different effects on the extraction of histological features of CT images of HCC. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
50. Research on High Performance Computing Published by Investigators at University of Malaga
- Subjects
Universities and colleges -- Spain -- Reports ,Computers ,News, opinion and commentary - Abstract
'While image texture is effective for use in pattern-recognition and image-analysis algorithms, textural features are time-consuming to calculate on standard CPUs. Therefore, we present novel implementations of textural-feature algorithms on [...]
- Published
- 2012
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