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computer vision - ECCV 2004 1

引用

丛书名： Lecture Notes in computer Science

1000年

作者： Tomás Pajdla Jiří Matas

ISBN: (数字)9783540246732

ISBN: (纸本)9783540219811

Welcome to the proceedings of the 8th European conference on computer - sion! Following a very successful ECCV 2002, the response to our call for papers was almost equally strong – 555 papers were submitted. We accepted 41 papers for oral and 149 papers for poster presentation. Several innovations were introduced into the review process. First, the n- ber of program committee members was increased to reduce their review load. We managed to assign to program committee members no more than 12 papers. Second, we adopted a paper ranking system. Program committee members were asked to rank all the papers assigned to them, even those that were reviewed by additional reviewers. third, we allowed authors to respond to the reviews consolidated in a discussion involving the area chair and the reviewers. Fourth, thereports,thereviews,andtheresponsesweremadeavailabletotheauthorsas well as to the program committee members. Our aim was to provide the authors with maximal feedback and to let the program committee members know how authors reacted to their reviews and how their reviews were or were not re?ected in the ?nal decision. Finally, we reduced the length of reviewed papers from 15 to 12 pages. thepreparationofECCV2004wentsmoothlythankstothee?ortsofthe- ganizing committee, the area chairs, the program committee, and the reviewers. We are indebted to Anders Heyden, Mads Nielsen, and Henrik J. Nielsen for passing on ECCV traditions and to Dominique Asselineau from ENST/TSI who kindly provided his GestRFIA conference software. We thank Jan-Olof Eklundh and Andrew Zisserman for encouraging us to organize ECCV 2004 in Prague.

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

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computer vision - ECCV 2004 1

引用

丛书名： Lecture Notes in computer Science

1000年

作者： Tomás Pajdla Jiří Matas

ISBN: (数字)9783540246718

ISBN: (纸本)9783540219835

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

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computer vision - ECCV 2004 1

引用

丛书名： Lecture Notes in computer Science

1000年

作者： Tomáš Pajdla Jiří Matas

ISBN: (数字)9783540246725

ISBN: (纸本)9783540219828

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

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Detection of Coal Quarry and Coal Dump Regions Using the Presence of Mine Water Bodies from Landsat 8 OLI/TIRS images 1

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7th International conference on computer vision and image processing, CVIP 2022

作者： Mukherjee, Jit Mukherjee, Jayanta Chakravarty, Debashish Dept. Computer Science and Engineering Birla Institute of Technology Ranchi Mesra India Dept. Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Dept. Mining Engineering Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (数字)9783031314179

ISBN: (纸本)9783031314162

Surface mining has major environmental, social, and economical adversities, which makes it an active area of research in remote sensing. Surface coal mining has additional adversities of coal seam fires. thus, the detection, classification, and monitoring of such regions have various research challenges. the surface coal mining land classes cover smaller areas compared to mid-resolution satellite images making them challenging to detect. Coal quarry and coal dump regions are such kinds of smaller land classes. they can be detected as a single land class as discussed in the literature. However, these land classes are observed to be difficult to detect separately as they follow near similar spectral characteristics. Hence, this paper proposes a novel technique to separate these regions using the presence of water bodies. Coal dump regions do not have water bodies, whereas some coal quarry regions may have water bodies. Such quarry regions are detected at first and further, they are used to train an unsupervised single class support vector machine (SVM ). this model is used to detect the coal dump regions by detecting the outliers. the proposed technique provides average precision and recall for coal quarry, and coal dump regions as [84.88%, 61.44%], and [70.91%, 52.79%], respectively over the seasons. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Quarries

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Single image dehazing using image boundary constraint and nearest neighborhood optimization 18

Single image dehazing using image boundary constraint and ne...

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Proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Sidharth Gautam Tapan Kumar Gandhi B. K. panigrahi Indian Institute of Technology New Delhi India

ISBN: (纸本)9781450366151

Single image dehazing is an ill-posed problem that requires assumptions, priors and constraints to solve. In this paper, boundary constraint utilizing median filter has been proposed on the image radiance for the rough estimation of transmission-map in haze images. Furthermore, for the refinement of estimated transmission-map, the proposed method use a multi-dimensional feature space that uses nearest neighborhood optimization under the framework of non-local principle. Experimental results manifest that proposed method is effective and results in visually appealing dehaze images that can be useful for subsequent user or computer based application.

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Sequence Recognition in Bharatnatyam Dance 7th

Sequence Recognition in Bharatnatyam Dance

引用

7th International conference on computer vision and image processing, CVIP 2022

作者： Bhuyan, Himadri Dhaipule, Rohit Das, Partha Pratim Indian Institute of Technology Kharagpur West Bengal Kharagpur721302 India

ISBN: (纸本)9783031314063

Bharatanatyam is the oldest indian Classical Dance (ICD) which is learned and practiced across India and the world. Adavu is the core of this dance form. there exist 15 Adavus and 58 variations. Each Adavu variation comprises a well-defined set of motions and postures (called dance steps) that occur in a particular order. So, while learning Adavus, students not only learn the dance steps but also take care of its sequence of occurrences. this paper proposed a method to recognize these sequences. In this work, firstly, we recognize the involved Key Postures (KPs) and motions in the Adavu using Convolutional Neural Network (CNN) and Support Vector Machine (SVM), respectively. In this, CNN achieves 99% and SVM’s recognition accuracy becomes 84%. Next, we compare these KP and motion sequences with the ground truth to find the best match using the Edit Distance algorithm with an accuracy of 98%. the paper contributes hugely to the state-of-the-art in the form of digital heritage, dance tutoring system, and many more. the paper addresses three novelties;(a) Recognizing the sequences based on the KPs and motions rather than only KPs as reported in the earlier works. (b) the performance of the proposed work is measured by analyzing the prediction time per sequence. We also compare our proposed approach with the previous works that deal with the same problem statement. (c) It tests the scalability of the proposed approach by including all the Adavu variations, unlike the earlier literature, which uses only one/two variations. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Support vector machines

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Real Time Incremental Foveal Texture Mapping for Autonomous Vehicles

Real Time Incremental Foveal Texture Mapping for Autonomous ...

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25th IEEE/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Kumar, Ashish McBride, James R. Pandey, Gaurav Indian Inst Technol Dept Elect Engn Kanpur Uttar Pradesh India Ford Motor Co Res & Innovat Ctr Dearborn MI 48121 USA

ISBN: (纸本)9781538680940

We propose an end-to-end real time framework to generate high resolution graphics grade textured 3D map of urban environment. the generated detailed map finds its application in the precise localization and navigation of autonomous vehicles. It can also serve as a virtual test bed for various vision and planning algorithms as well as a background map in the computer games. In this paper, we focus on two important issues: (i) incrementally generating a map with coherent 3D surface, in real time and (ii) preserving the quality of color texture. To handle the above issues, firstly, we perform a pose-refinement procedure which leverages camera image information, Delaunay triangulation and existing scan matching techniques to produce high resolution 3D map from the sparse input LIDAR scan. this 3D map is then texturized and accumulated by using a novel technique of ray-filtering which handles occlusion and inconsistencies in pose-refinement. Further, inspired by human fovea, we introduce foveal-processing which significantly reduces the computation time and also assists ray-filtering to maintain consistency in color texture and coherency in 3D surface of the output map. Moreover, we also introduce texture error (TE) and mean texture mapping error (MTME), which provides quantitative measure of texturing and overall quality of the textured maps.

关键词： three-dimensional displays Laser radar Cameras Real-time systems image color analysis Global Positioning System

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Fish-Eye image Based Cross Traffic Alert System 7th

Fish-Eye Image Based Cross Traffic Alert System

引用

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

作者： Bandyopadhyay, Madhurima Kumar, Ankit Pathayapurakkal, Anoop Pvt. Ltd. Bengaluru India

ISBN: (纸本)9789811586965

In the automotive industry, the use of advanced driver assisted systems (ADAS) is gaining a lot of traction. ADAS systems are designed to assist the driver by providing information regarding road users, lane information, traffic infrastructures etc., and improve the road safety aspects of an automobile. In this paper, we propose a real-time cross traffic alert (CTA) system based on fisheye camera images having ~180° field of view (FOV). the main purpose of CTA is to avoid unwanted collision with any approaching target by issuing an alert to the driver at a T-junction. the main components of the proposed CTA algorithm are, sparse optical flow vector tracking, object cluster formation and estimation of time to collision (TTC) for each object cluster. the TTC calculation can be performed without explicit depth reconstruction. We compute TTC based on homography estimation between similar features of consecutive image frames. Under good weather condition, the performance of the proposed CTA algorithm in detecting approaching target is reasonably good with a true positive rate of nearly 88%, but with significant false positive rate to the tune of 19%. A major contributing factor to this high false positive is identified to be the inability to distinguish a real crossing object and objects moving parallel to the host vehicle just using TTC estimates. To suppress the unintended false positive cases, we propose a novel solution based on statistics of the flow vector cluster and showcase its efficacy. the classification result for the proposed approach indicates that we have achieved to reduce the false positive rate to nearly 10% while maintaining the true positive rate. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Automotive industry

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New Tampered Features for Scene and Caption Text Classification in Video Frame

New Tampered Features for Scene and Caption Text Classificat...

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International Workshop on Frontiers in Handwriting Recognition

作者： Sangheeta Roy Palaiahnakote Shivakumara Umapada Pal Tong Lu Chew Lim Tan Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China Department of Computer Science National University of Singapore

ISBN: (纸本)9781509009824

the presence of both caption/graphics/superimposed and scene texts in video frames is the major cause for the poor accuracy of text recognition methods. this paper proposes an approach for identifying tampered information by analyzing the spatial distribution of DCT coefficients in a new way for classifying caption and scene text. Since caption text is edited/superimposed, which results in artificially created texts comparing to scene texts that exist naturally in frames. We exploit this fact to identify the presence of caption and scene texts in video frames based on the advantage of DCT coefficients. the proposed method analyzes the distributions of both zero and non-zero coefficients (only positive values) locally by moving a window, and studies histogram operations over each input text line image. this generates line graphs for respective zero and non-zero coefficient coordinates. We further study the behavior of text lines, namely, linearity and smoothness based on centroid location analysis, and the principal axis direction of each text line for classification. Experimental results on standard datasets, namely, ICDAR 2013 video, 2015 video, YVT video and our own data, show that the performances of text recognition methods are improved significantly after-classification compared to before-classification.

关键词： Discrete cosine transforms Text recognition image color analysis Linearity Bars Character recognition Mathematical model

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A Chaos Based Robust and Secure image Hashing Framework 18

A Chaos Based Robust and Secure Image Hashing Framework

引用

Proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Satendra Pal Singh Gaurav Bhatnagar Indian Institute of Technology Jodhpur India

ISBN: (纸本)9781450366151

In this paper, a robust image hashing framework is presented using discrete cosine transformation and singular value decomposition. Firstly, the input image is normalized using geometric moment and normalized coefficients are divided into non-overlapping blocks. the selected blocks based on a peace-wise non-linear chaotic map are transformed using discrete cosine transom followed by singular value decomposition. then a feature matrix is constructed in reliance on Hessian matrix and the final hash values are obtained. the proposed hashing system is resilient to different content-preserving image distortions such as geometric and filtering operations. the simulated results demonstrate the efficiency proposed framework in terms of security and robustness.

关键词： Discrete cosine transformation

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