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检索条件"任意字段=Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing"

共 1497 条记录，以下是481-490 订阅

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Subspace segmentation based metric learning 25

Subspace segmentation based metric learning

引用

25th IEEE International conference on image processing, ICIP 2018

作者： Dutta, Ujjal Kr Chandra Sekhar, C. Department of Computer Science and Engineering Indian Institute of Technology Madras India

ISBN: (纸本)9781479970612

Distance Metric Learning (DML) has been successfully applied in a variety of computer vision and image processing tasks. Laplacian Regularized Metric Learning (LRML) computes a distance metric by satisfying given sets of pairwise similarity and dissimilarity constraints while preserving the topological structure of the given data via a Laplacian regularizer which is dependent on an affinity matrix. This paper addresses the problem of semi-supervised DML using LRML for image data sampled from a union of low-dimensional subspaces by computing the affinity matrix using a self-representation based graph instead of traditional graph used in LRML, resulting in two variants of LRML called as L-NNLRS and L-NLSP. © 2018 IEEE.

关键词： Matrix algebra

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computer vision, Pattern Recognition, image processing, and graphics 1st ed. 2018

引用

丛书名： Communications in computer and Information Science

2018年

作者： Renu Rameshan Chetan Arora Sumantra Dutta Roy

ISBN: (数字)9789811300202

ISBN: (纸本)9789811300196

This book constitutes the refereed proceedings of the 6th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2017, held in Mandi, India, in December 2017. The 48 revised full papers presented in this volume were carefully reviewed and selected from 147 submissions. The papers are organized in topical sections on video processing; image and signal processing; segmentation, retrieval, captioning; pattern recognition applications.

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Deep neural network for foreground object segmentation: An unsupervised approach 6th

Deep neural network for foreground object segmentation: An u...

引用

6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Majumder, Avishek Venkatesh Babu, R. Indian Institute of Science BangaloreKarnataka560012 India

ISBN: (纸本)9789811300196

Saliency plays a key role in various computer vision tasks. Extracting salient regions from images and videos have been a well established problem of computer vision. While segmenting salient objects from images depend only on static information, temporal information in a video can make non salient objects be salient due to movement. Besides the temporal information, there are other challenges involved with video segmentation, such as 3D parallax, camera shake, motion blur, etc. In this work, we propose a novel unsupervised end to end trainable, fully convolutional deep neural network for object segmentation. Our model is robust and scalable across scenes, as it is tested unsupervisedly and can easily infer which objects constitute the foreground of the image. We run various tests on two well established benchmarks of video object segmentation, DAVIS and FBMS-59 datasets. We report our results and compare them against the state of the art methods. © Springer Nature Singapore Pte Ltd. 2018.

关键词： computer vision

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Classification of indian monuments into architectural styles 6th

Classification of indian monuments into architectural styles

引用

6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Sharma, Saurabh Aggarwal, Priyal Bhattacharyya, Akanksha N. Indu, S. Department of Computer Science and Engineering Delhi Technological University Delhi India Department of Electronics and Communication Engineering Delhi Technological University Delhi India

ISBN: (纸本)9789811300196

We propose two novel approaches to classify indian monuments according to their distinct architectural styles. While the historical significance of most indian monuments is well documented, the details of their architectural styles are not as well recorded. Different indian architectural styles often show certain similar features which makes classification a difficult task. Previous work has focused on European architecture and standard datasets are available for the same, but no standard dataset exists for indian architecture. Therefore, we have curated a dataset of indian monuments. In this paper, we propose two approaches to classify monuments according to their styles: Radon Barcodes and Convolutional Neural Networks. The first approach is fast and consumes less memory, but the second approach gives an accuracy of 82%, which is better than the 76% accuracy of the first method. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Network architecture

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Joint Concept Factorization for image Representation 10

Joint Concept Factorization for Image Representation

引用

10th International conference on Digital image processing (ICDIP)

作者： Long, Xianzhong Nanjing Univ Posts & Telecommun Sch Comp Sci & Technol 9 Wenyuan Rd Nanjing Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol 29 Jiangjun Rd Nanjing Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Key Lab Big Data Secur & Intelligent Proc 9 Wenyuan Rd Nanjing Jiangsu Peoples R China

ISBN: (数字)9781510622005

ISBN: (纸本)9781510622005

As one kind of popular clustering techniques, Concept Factorization (CF) has been widely employed in computer vision and pattern recognition fields. However, existing clustering algorithms based on CF do not consider the complementarity between multiple features. In order to solve this problem, many joint learning methods have been proposed in recent years, such as Joint Non-negative Matrix Factorization (JNMF), Laplacian Regularized Joint Non-negative Matrix Factorization (LJ-NMF). Inspired by these, Joint Concept Factorization (JCF) and Joint Locally Consistent Concept Factorization (JLCCF) schemes are proposed in this paper. Experimental results on image clustering show that the proposed schemes outperform some existing algorithms in terms of accuracy and normalized mutual information.

关键词： Non-negative Matrix Factorization Joint Concept Factorization Multi-Features Learning image Clustering

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image Matching Algorithm with Color Information based on SIFT 10

Image Matching Algorithm with Color Information based on SIF...

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10th International conference on Digital image processing (ICDIP)

作者： Yu, Xiaoyong Lei, Hao Du, Yunfei Li, Baopeng Yuan, Xiaobin Gao, Wei Song, Zongxi Zheng, Peiyun Chinese Acad Sci Xian Inst Opt & Precis Mech Space Opt Lab Xian 710019 Shaanxi Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Elect & Informat Technol Space Syst Beijing 100190 Peoples R China

ISBN: (数字)9781510622005

ISBN: (纸本)9781510622005

image matching is an important topic in the field of computer vision, in view of high robustness and accuracy, SIFT or the improved methods based on SIFT is generally used for image matching algorithms. The traditional SIFT method is implemented on grayscale images without regard to the color information of images, which may cause decreasing of the matching points and reduction of the matching accuracy. Prevailing color descriptors can effectively add color information into SIFT, however dramatically increase the complexity of algorithm. In this paper, a novel approach is proposed to take advantage of the color information for image matching based on SIFT. The proposed algorithm uses the gradient information of color channel as the compensation of luminance channel, which can effectively enhance the color information with SIFT. Experimental results show that the number of feature points and matching accuracy can be significantly promoted, while the complexity and performance of image matching algorithm are well trade-off.

关键词： image matching SIFT color compensation

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Gender Classification from Face images by Using Local Binary Pattern and Gray-Level Co-Occurrence Matrix 10

Gender Classification from Face Images by Using Local Binary...

引用

10th International conference on Machine vision (ICMV)

作者： Uzbas, Betul Arslan, Ahmet Selcuk Univ Dept Comp Engn Konya Turkey Konya Food & Agr Univ Dept Comp Engn Konya Turkey

ISBN: (数字)9781510619425

ISBN: (纸本)9781510619425

Gender is an important step for human computer interactive processes and identification. Human face image is one of the important sources to determine gender. In the present study, gender classification is performed automatically from facial images. In order to classify gender, we propose a combination of features that have been extracted face, eye and lip regions by using a hybrid method of Local Binary Pattern and Gray-Level Co-Occurrence Matrix. The features have been extracted from automatically obtained face, eye and lip regions. All of the extracted features have been combined and given as input parameters to classification methods (Support Vector Machine, Artificial Neural Networks, Naive Bayes and k-Nearest Neighbor methods) for gender classification. The Nottingham Scan face database that consists of the frontal face images of 100 people (50 male and 50 female) is used for this purpose. As the result of the experimental studies, the highest success rate has been achieved as 98% by using Support Vector Machine. The experimental results illustrate the efficacy of our proposed method.

关键词： Gender classification gray-level co-occurrence matrix local binary pattern image processing

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FACE-Face at classroom environment: Dataset and exploration 8

FACE-Face at classroom environment: Dataset and exploration

引用

8th International conference on image processing Theory, Tools and Applications, IPTA 2018

作者： Karnalim, Oscar Budi, Setia Santoso, Sulaeman Handoyo, Erico D. Toba, Hapnes Nguyen, Huyen Malhotra, Vishv Faculty of Information Technology Maranatha Christian University Indonesia UNSW Art Design University of New South Wales Sydney Australia Department of Computer Science and Engineering Indian Institute of Technology Guwahati India

ISBN: (纸本)9781538664278

The rapid development in face detection study has been greatly supported by the availability of large image datasets, which provide detailed annotations of faces on images. However, among a number of publicly accessible datasets, to our best knowledge, none of them are specifically created for academic applications. In this paper, we propose a systematic method in forming an image dataset tailored for classroom environment. We also made our dataset and its exploratory analyses publicly available. Studies in computer vision for academic application, such as an automated student attendance system, would benefit from our dataset. © 2018 IEEE.

关键词： Face recognition

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Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Network 31

Eye in the Sky: Real-time Drone Surveillance System (DSS) fo...

引用

IEEE/CVF conference on computer vision and Pattern Recognition (CVPR)

作者： Singh, Amarjot Patil, Devendra Omkar, S. N. Univ Cambridge Dept Engn Cambridge England Natl Inst Technol Warangal Andhra Pradesh India Indian Inst Sci Bangalore Karnataka India

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Drone systems have been deployed by various law enforcement agencies to monitor hostiles, spy on foreign drug cartels, conduct border control operations, etc. This paper introduces a real-time drone surveillance system to identify violent individuals in public areas. The system first uses the Feature Pyramid Network to detect humans from aerial images. The image region with the human is used by the proposed ScatterNet Hybrid Deep Learning (SHDL) network for human pose estimation. The orientations between the limbs of the estimated pose are next used to identify the violent individuals. The proposed deep network can learn meaningful representations quickly using ScatterNet and structural priors with relatively fewer labeled examples. The system detects the violent individuals in real-time by processing the drone images in the cloud. This research also introduces the aerial violent individual dataset used for training the deep network which hopefully may encourage researchers interested in using deep learning for aerial surveillance. The pose estimation and violent individuals identification performance is compared with the state-of-the-art techniques.

关键词： Personal area networks Drones Feature extraction Surveillance Pose estimation Training

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proceedings of 2nd International conference on computer vision & image processing 1st ed. 2018

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丛书名： Advances in Intelligent Systems and Computing

2018年

作者： Bidyut B. Chaudhuri Mohan S. Kankanhalli Balasubramanian Raman

ISBN: (数字)9789811078989

ISBN: (纸本)9789811078972

The book provides insights into the Second International conference on computer vision & image processing (CVIP-2017) organized by Department of computer Science and Engineering of indian Institute of Technology Roorkee. The book presents technological progress and research outcomes in the area of image processing and computer vision. The topics covered in this book are image/video processing and analysis; image/video formation and display; image/video filtering, restoration, enhancement and super-resolution; image/video coding and transmission; image/video storage, retrieval and authentication; image/video quality; transform-based and multi-resolution image/video analysis; biological and perceptual models for image/video processing; machine learning in image/video analysis; probability and uncertainty handling for image/video processing; motion and tracking; segmentation and recognition; shape, structure and stereo.

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