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Progress in Pattern Recognition, image Analysis, computer vision, and Applications 1

引用

丛书名： Lecture Notes in computer Science

1000年

作者： Isabelle Bloch Roberto M. Cesar

ISBN: (数字)9783642166877

ISBN: (纸本)9783642166860

Pattern recognition is a central topic in contemporary computer sciences, with continuously evolving topics, challenges, and methods, including machine learning, content-based image retrieval, and model- and knowledge-based - proaches, just to name a few. the Iberoamerican Congress on Pattern Recog- tion (CIARP) has become established as a high-quality conference, highlighting the recent evolution of the domain. these proceedings include all papers presented during the 15th edition of this conference, held in Sao Paulo, Brazil, in November 2010. As was the case for previous conferences, CIARP 2010 attracted parti- pants from around the world with the aim of promoting and disseminating - going research on mathematical methods and computing techniques for pattern recognition, computer vision, image analysis, and speech recognition, as well as their applications in such diverse areas as robotics, health, entertainment, space exploration, telecommunications, data mining, document analysis,and natural language processing and recognition, to name only a few of them. Moreover, it provided a forum for scienti?c research, experience exchange, sharing new kno- edge and increasing cooperation between research groups in pattern recognition and related areas. It is important to underline that these conferences have contributed sign- icantly to the growth of national associations for pattern recognition in the Iberoamerican region, all of them as members of the International Association for Pattern Recognition (IAPR).

关键词： Pattern Recognition image processing and computer vision Artificial Intelligence computer Imaging, vision, Pattern Recognition and graphics Biometrics Algorithm Analysis and Problem Complexity

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Facet-JFA: Faster computation of discrete Voronoi diagrams 14

Facet-JFA: Faster computation of discrete Voronoi diagrams

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Masood, Talha Bin Malladi, Hari Krishna Natarajan, Vijay Dept. of Computer Science and Automation Indian Institute of Science Bangalore India Supercomputer Education and Research Centre Indian Institute of Science Bangalore India

ISBN: (纸本)1595930361

Jump Flooding is a method for propagating labels across a given plane from different seeds. It has been used to compute the discrete Voronoi tessellation of a given plane efficiently. We introduce a version of JFA, which optimizes the number of pixels processed by computing only the faces of the Voronoi tessellation. the pixels in the interior of the Voronoi regions are not processed resulting in a 1-skeleton representation of the Voronoi tessellation in 2D and a 2-skeleton representation in 3D. We describe an implementation of this algorithm on a GPU using CUDA and demonstrate its performance benefits on multiple data sets. As an application of the proposed algorithm, we present a GPU based method for extraction of channel centerlines in biomolecules. the fast computation of the discrete Voronoi diagram is exploited to extract channels in molecular dynamics simulation trajectories on-the-fly, thereby supporting the interactive visual analysis of static and dynamic channel structures. Copyright is held by the authors.

关键词： graphics processing unit

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A Robust Pose Transformational GAN for Pose Guided Person image Synthesis 7th

A Robust Pose Transformational GAN for Pose Guided Person Im...

引用

7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Karmakar, Arnab Mishra, Deepak Indian Institute of Space Science and Technology ThiruvananthapuramKerala695547 India

ISBN: (纸本)9789811586965

Generating photorealistic images of human subjects in any unseen pose have crucial applications in generating a complete appearance model of the subject. However, from a computer vision perspective, this task becomes significantly challenging due to the inability of modelling the data distribution conditioned on pose. Existing works use a complicated pose transformation model with various additional features such as foreground segmentation, human body parsing etc. to achieve robustness that leads to computational overhead. In this work, we propose a simple yet effective pose transformation GAN by utilizing the Residual Learning method without any additional feature learning to generate a given human image in any arbitrary pose. Using effective data augmentation techniques and cleverly tuning the model, we achieve robustness in terms of illumination, occlusion, distortion and scale. We present a detailed study, both qualitative and quantitative, to demonstrate the superiority of our model over the existing methods on two large datasets. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Large dataset

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image Analysis 1

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丛书名： Lecture Notes in computer Science

1000年

ISBN: (数字)9783540730408

ISBN: (纸本)9783540730392

the present volume contains the proceedings of the Scandinavian conference on image Analysis, SCIA 2007, held at Hotel Hvide Hus, Aalborg, Denmark, June 10–14, 2007. Initiated in 1979 by Torleiv Orhaug in Sweden, SCIA 2007 represented the 15th in the biennial series of conferences. It is arranged in turn by the Scandi- vian countries of Sweden, Finland, Denmark, and Norway, making it a regional conference. However, judging by the nationalities of contributing authors and participants, it is also an international conference. Worldwide, there is no lack of conferences on image analysis. Indeed, hardly a day passes without an announcement of yet another conference. therefore the pattern recognition societies of the Scandinavian countries take particular pride in being able to continue the SCIA tradition. SCIA has indeed matured over the many years it has been in existence, but in our opinion SCIA has maintained ?- ibility and has been able to adopt and incorporate necessary changes and adju- ments over that time span. An important key to the success of SCIA is the constant and continuing high quality of the scienti?c content. Furthermore, the relaxed and friendly atmosphere of the conference itself is well known within the community. the objective to keep in mind must be to continue along those lines.

关键词： image processing and computer vision Pattern Recognition computer graphics

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A stochastic image denoising algorithm using 3-D block filtering under a non-local means framework 12

A stochastic image denoising algorithm using 3-D block filte...

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Pai, Sreekanth G. Jiji, C.V. Department of Electronics and Communication College of Engineering Trivandrum Kerala 695016 India

ISBN: (纸本)9781450316606

In this paper, we propose an iterative approach for image denoising using random sampling and 3-D transforms. To denoise an image block first we form an array of similar blocks followed by a sparse 3-D transform. thresholding in 3-D transform domain followed by non-local means approach reconstructs the denoised image block. By processing all overlapping blocks and aggregating them using suitable weights we obtain the denoised estimate. To measure the completeness of the denoising process we apply a robust median estimator to estimate the output noise. the above steps are iterated till the output noise is minimum. By comparing with the state-of-the-art algorithms, we show that the proposed method outperforms the competing algorithms in terms of PSNR, structural similarity index and visual similarity. © 2012 ACM.

关键词： image denoising

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Disparity based compression technique for focused plenoptic images 14

Disparity based compression technique for focused plenoptic ...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Choudhury, Chandrajit Chaudhuri, Subhasis Dept. of Electrical Engineering Indian Institute for Technology Bombay Mumbai India

ISBN: (纸本)1595930361

In this paper we present a novel method for compression of focused plenoptic (light field) images. Plenoptic image is an array of lenslet images where the consecutive images act as stereo images. Adjacent lenslet images have overlapping regions leading to higher levels of redundancy than observed in a normal image. We use this overlap to appropriately select a set of reference images from which the remaining lenslet images can be approximately reconstructed. these reference images can be compressed using jpeg. the residue of the reconstructed image from the original image is compressed using either DWT-SPIHT or DCT based compression. Using the proposed method we are able to compress the image at high PSNR at a low bits per pixel (bpp). Copyright 2014 ACM.

关键词： image compression

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A non-local MRF model for heritage architectural image completion 12

A non-local MRF model for heritage architectural image compl...

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Gupta, Deepan Chhajer, Vaidehi Mishra, Anand Jawahar, C.V. Center for Visual Information Technology IIIT Hyderabad India

ISBN: (纸本)9781450316606

MRF models have shown state-of-the-art performance for many computer vision tasks. In this work, we propose a non-local MRF model for image completion problem. the goal of image completion is to fill user specified "target" region with patches of "source" regions in a way that is visually plausible to an observer. We represent the patches in the target region of the image as random variables in an MRF, and introduce a novel energy function on these variables. Each variable takes a label from a label set which is a collection of patches of the source region. the quality of the image completion is determined by the value of the energy function. the non-locality in the MRF is achieved through long range pairwise potentials. these long range pairwise potentials are defined to capture the inherent repeating patterns present in heritage architectural images. We minimize this energy function using Belief Propagation to obtain globally optimal image completion. We have tested our method on a wide variety of images and shown superior performance over previously published results for this task. © 2012 ACM.

关键词： Belief propagation

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image Segmentation and Geometric Feature Based Approach for Fast Video Summarization of Surveillance Videos 1

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3rd Workshop on computer vision Applications, WCVA 2018, held in conjunction with the 11th indian conference on computer vision, graphics and image processing, ICVGIP 2018

作者： Rohan, Raju Dhanakshirur ara Patel, Zeba Yadavannavar, Smita C. Sujata, C. Mudengudi, Uma KLE Technological University Hubli India

ISBN: (数字)9789811513879

ISBN: (纸本)9789811513862

In this paper, we propose a geometric feature and frame segmentation based approach for video summarization. Video summarization aims to generate a summarized video with all the salient activities of the input video. We propose to retain the salient frames towards generation of video summary. We detect saliency in foreground and background of the image separately. We propose to model the image as MRF (Markov Random Field) and use MAP (Maximum a-posteriori) as final solution to segment the image into foreground and background. the salient frame is defined by the variation in feature descriptors using the geometric features. We propose to combine the probabilities of foreground and background segments being salient using DSCR (Dempster Shafer Combination Rule). We consider the summarized video as a combination of salient frames for a user defined time. We demonstrate the results using several videos in BL-7F dataset and compare the same with state of art techniques using retention ratio and condensation ratio as quality parameters. © Springer Nature Singapore Pte Ltd, 2019.

关键词： image segmentation

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Insta Net: Recurrent Residual Network for Instagram Filter Removalg± 13

Insta Net: Recurrent Residual Network for Instagram Filter R...

引用

13th indian conference on computer vision, graphics, and image processing, ICVGIP 2022

作者： Kansal, Priya Nathan, Sabari Couger Inc Shibuya Tokyo Japan

ISBN: (纸本)9781450398237

Social media filters cause various manipulations in the original image. While these manipulations make the images visibly pleasant, they can affect their authenticity and the performance of further image-processing tasks. therefore, removing these social media filters is a crucial pre-processing step for the data pipeline of any image-processing task. In this paper, a recurrent residual UNet-based model is proposed to remove the filters. the recurrent residual block allows the units of each layer to evolve over discrete time steps so that the activity of each unit is modulated by the activities of its neighboring units. Because of this characteristic, the networks learn contextual information more efficiently and effectively. Assuming that a filter can be noise, the proposed model learns the feature representation of the filter, and then the original, unfiltered image is retrieved by taking the residuals of the input image and the filter representation. the experimental results show that the proposed model outperforms both supervised and unsupervised methods by a considerable margin in the IFFI dataset. © 2022 ACM.

关键词： image processing

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Salient object detection using a fuzzy theoretic approach 12

Salient object detection using a fuzzy theoretic approach

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Kapoor, Aditi Biswas, K.K. Hanmandlu, M. Amar Nath and Shashi Khosla School of Information Technology Indian Institute of Technology Delhi Hauz Khas New Delhi - 110016 India Department of Computer Science and Engineering Indian Institute of Technology Delhi Hauz Khas New Delhi - 110016 India Department of Electrical Engineering Indian Institute of Technology Delhi Hauz Khas New Delhi - 110016 India

ISBN: (纸本)9781450316606

Color is the most dominant feature used by the human brain to perceive an image region to be salient. Since size, shape, location and colors of salient objects vary widely, a fuzzy rule based system is proposed in this paper which uses color proximity, color spread, connected components and presence of a face as linguistic variables. these rules are learned using a genetic algorithm. the colorspace used is the CIELab colorspace which closely conforms with human perception of colors. A publicly available image dataset is used for training and testing the system. Comparisons with existing state-of-the-art methods in terms of precision, recall and F-Measure have been presented. © 2012 ACM.

关键词： Color

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