The Indian Conference on computervision, Graphics and imageprocessing (ICVGIP) is a forum bringing together researchers and practitioners in these related areas, coming from national and international academic insti...
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ISBN:
(数字)9783540683025
ISBN:
(纸本)9783540683018
The Indian Conference on computervision, Graphics and imageprocessing (ICVGIP) is a forum bringing together researchers and practitioners in these related areas, coming from national and international academic institutes, from government research and development laboratories, and from industry. ICVGIP has been held biannually since its inception in 1998, attracting more participants every year, including international participants. The proceedings of ICVGIP 2006, published in Springer's series Lecture Notes in computer Science, comprise 85 papers that were selected for presentation from 284 papers, which were submitted from all over the world. Twenty-nine papers were oral presentations, and 56 papers were presented as posters. For the first time in ICVGIP, the review process was double-blind as common in the major international conferences. Each submitted paper was assigned at least three reviewers who are experts in the relevant area. It was difficult to select such a few papers, as there were many other deserving, but those could not be accommodated.
This 4-volumes set constitutes the proceedings of the ICPR 2022 Workshops of the 26;International Conference on Pattern Recognition Workshops, ICPR 2022, Montreal, QC, Canada, August 2023. The 167 full papers presente...
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ISBN:
(数字)9783031377457
ISBN:
(纸本)9783031377440
This 4-volumes set constitutes the proceedings of the ICPR 2022 Workshops of the 26;International Conference on Pattern Recognition Workshops, ICPR 2022, Montreal, QC, Canada, August 2023. The 167 full papers presented in these 4 volumes were carefully reviewed and selected from numerous submissions. ICPR workshops covered domains related to pattern recognition, artificial intelligence, computervision, image and sound analysis. Workshops’ contributions reflected the most recent applications related to healthcare, biometrics, ethics, multimodality, cultural heritage, imagery, affective computing, etc.
This 4-volumes set constitutes the proceedings of the ICPR 2022 Workshops of the 26;International Conference on Pattern Recognition Workshops, ICPR 2022, Montreal, QC, Canada, August 2023. The 167 full papers presente...
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ISBN:
(数字)9783031377426
ISBN:
(纸本)9783031377419
This 4-volumes set constitutes the proceedings of the ICPR 2022 Workshops of the 26;International Conference on Pattern Recognition Workshops, ICPR 2022, Montreal, QC, Canada, August 2023. The 167 full papers presented in these 4 volumes were carefully reviewed and selected from numerous submissions. ICPR workshops covered domains related to pattern recognition, artificial intelligence, computervision, image and sound analysis. Workshops’ contributions reflected the most recent applications related to healthcare, biometrics, ethics, multimodality, cultural heritage, imagery, affective computing, etc.
UreteroPelvic Junction Obstruction(UPJO)is a common hydronephrosis disease in children that can result in an even progressive loss of renal *** is an economical,radiationless,noninvasive,and high noise preliminary dia...
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UreteroPelvic Junction Obstruction(UPJO)is a common hydronephrosis disease in children that can result in an even progressive loss of renal *** is an economical,radiationless,noninvasive,and high noise preliminary diagnostic step for *** intelligence has been widely applied to medical fields and can greatly assist doctors'diagnostic *** demand for a highly secure network environment in transferring electronic medical data online,therefore,has led to the development of blockchain *** this study,we built and tested a framework that integrates a deep learning diagnosis model with blockchain *** diagnosis model is a combination of an attention-based pyramid semantic segmentation network and a discrete wavelet transformation-processed residual classification *** also compared the performance between benchmark models and our *** diagnosis model outperformed benchmarks on the segmentation task and classification task with MloU=87.93,MPA=93.52,and accuracy=91.77%.For the blockchain system,we applied the InterPlanetary File System protocol to build a secure and private sharing *** framework can automatically grade the severity of UPJO using ultrasound images,guarantee secure medical data sharing,assist in doctors'diagnostic ability,relieve patients'burden,and provide technical support for future federated learning and linkage of the Internet of Medical Things(loMT).
To get the high compression ratio as well as the high-quality reconstructed image, an effective image compression scheme named irregular segmentation region coding based on spiking cortical model（ISRCS） is presented...
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To get the high compression ratio as well as the high-quality reconstructed image, an effective image compression scheme named irregular segmentation region coding based on spiking cortical model（ISRCS） is presented. This scheme is region-based and mainly focuses on two issues. Firstly, an appropriate segmentation algorithm is developed to partition an image into some irregular regions and tidy contours, where the crucial regions corresponding to objects are retained and a lot of tiny parts are eliminated. The irregular regions and contours are coded using different methods respectively in the next step. The other issue is the coding method of contours where an efficient and novel chain code is employed. This scheme tries to find a compromise between the quality of reconstructed images and the compression ratio. Some principles and experiments are conducted and the results show its higher performance compared with other compression technologies, in terms of higher quality of reconstructed images, higher compression ratio and less time consuming.
A novel adaptable accurate way for calculating the polar FFT and the log-polar FFT is developed in this paper, namely, Multilayer Fractional Fourier Transform ( MLFFT). MLFFT is a necessary addition to the pseudopolar...
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A novel adaptable accurate way for calculating the polar FFT and the log-polar FFT is developed in this paper, namely, Multilayer Fractional Fourier Transform ( MLFFT). MLFFT is a necessary addition to the pseudopolar FFT for the following reasons: It has lower interpolation errors in both polar and log-polar Fourier transforms, it reaches better accuracy with the nearly same computing complexity as the pseudopolar FFT, and provides a mechanism to increase the accuracy by increasing the user-defined computing level. This paper demonstrates both MLFFT itself and its advantages theoretically and experimentally. By emphasizing applications of MLFFT in image registration with rotation and scaling, our experiments suggest two major advantages of MLFFT: 1) Scaling up to 5 and arbitrary rotation angles or scaling up to 10 without rotation can be recovered by MLFFT, while, currently, the result recovered by the state-of-the-art algorithms is the maximum scaling of 4. 2) No iteration is needed to recover large rotation and scaling values of images by MLFFT;hence, it is more efficient than the pseudopolar-based FFT methods for image registration.
This paper describes models and algorithms for the real-time segmentation of foreground from background layers in stereo video sequences. Automatic separation of layers from color/contrast or from stereo alone is know...
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This paper describes models and algorithms for the real-time segmentation of foreground from background layers in stereo video sequences. Automatic separation of layers from color/contrast or from stereo alone is known to be error-prone. Here, color, contrast, and stereo matching information are fused to infer layers accurately and efficiently. The first algorithm, Layered Dynamic Programming (LDP), solves stereo in an extended six-state space that represents both foreground/background layers and occluded regions. The stereo-match likelihood is then fused with a contrast-sensitive color model that is learned on-the-fly and stereo disparities are obtained by dynamic programming. The second algorithm, Layered Graph Cut (LGC), does not directly solve stereo. Instead, the stereo match likelihood is marginalized over disparities to evaluate foreground and background hypotheses and then fused with a contrast-sensitive color model like the one used in LDP. Segmentation is solved efficiently by ternary graph cut. Both algorithms are evaluated with respect to ground truth data and found to have similar performance, substantially better than either stereo or color/contrast alone. However, their characteristics with respect to computational efficiency are rather different. The algorithms are demonstrated in the application of background substitution and shown to give good quality composite video output.
In this paper, we derive a technique for analysis of local distortions which affect data in real-world applications. In the paper, we focus on image data, specifically handwritten characters. Given a reference image a...
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In this paper, we derive a technique for analysis of local distortions which affect data in real-world applications. In the paper, we focus on image data, specifically handwritten characters. Given a reference image and a distorted copy of it, the method is able to efficiently determine the rotations, translations, scaling, and any other distortions that have been applied. Because the method is robust, it is also able to estimate distortions for two unrelated images, thus determining the distortions that would be required to cause the two images to resemble each other. The approach is based on a polynomial series expansion using matrix powers of linear transformation matrices. The technique has applications in pattern recognition in the presence of distortions.
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