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Dictionary based approach for facial expression recognition from static images 10

Dictionary based approach for facial expression recognition ...

引用

10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

作者： Sharma, Krishan Rameshan, Renu School of Computing and Electrical Engineering Indian Institute of Technology MandiHimachal Pradesh India

We present a simple approach for facial expression recognition from images using the principle of sparse representation using a learned dictionary. Visual appearance based feature descriptors like histogram of oriented gradients (HOG), local binary patterns (LBP) and eigenfaces are used. We use Fisher discrimination dictionary which has discrimination capability in addition to being reconstructive. the classification is based on the fact that each expression class with in the dictionary spans a subspace and these subspaces have non-overlapping directions so that they are widely separated. Each test feature point has a sparse representation in the union of subspaces of dictionary formed by labeled training points. To check recognition performance of the proposed approach, extensive experimentation is done over Jaffee and CK databases. Results show that the proposed approach has better classification accuracy than state-of-the-art techniques. © Springer International Publishing AG 2017.

关键词： Local binary pattern

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ColCompNeT: Deep Learning-Based Colorization-Based Coding Network 1

引用

4th International conference on Machine Intelligence and Signal processing, MISP 2022

作者： Mishra, Dipti Singh, Satish Kumar Mahindra University Hyderabad500043 India Indian Institute of Information Technology Allahabad Allahabad211012 India

ISBN: (数字)9789819900855

ISBN: (纸本)9789819900848

Converting a grayscale image to a visually plausible and perceptually meaningful color image is an exciting research topic in computer vision and graphics. However, predicting the chrominance channels from a grayscale image is an ill-posed problem, especially when the objective is to produce the same color as in the ground truth image, like for the application of compression using colorization or video/image restoration. Compression using colorization efficiently reduces the storage space and transmission bandwidth by two-thirds and maintains the image’s visual quality. However, colorizing decompressed gray images exactly as ground truth with maximum accuracy is a challenging task. In this paper, learning-based CNN network ColCompNeT has been proposed in which concept of parallel training of colorization and compression network is utilized. With the proposed algorithm, a maximum bit saving of 36.45% is achieved with the improved objective and subjective performance when compared with the state-of-the-art methods. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Network coding

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Feature prominence-based weighting scheme for video tracking 12

Feature prominence-based weighting scheme for video tracking

引用

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

作者： Candemir, Sema Palaniappan, Kannappan Bunyak, Filiz Rao, Raghuveer M. Seetharaman, Guna Dept. of Computer Science University of Missouri Columbia MO United States Army Research Lab. Adelphi MD United States Air Force Research Lab. Rome NY United States

ISBN: (纸本)9781450316606

this paper introduces a new mechanism called Feature Prominence to combine evidence from multiple feature operators for more reliable target detection and localization during video tracking. Feature prominence is measured using the statistical p-value estimated from a non-parametric local kernel density estimate of the a posteriori feature distribution. More prominent features have lower p-values and this ordering can be used to either discard low prominence features (high p-values) or reduce their weight during the feature fusion process to produce a more reliable fused feature likelihood map for locating the target at a subsequent time during tracking. the proposed feature fusion method is embedded within a test-bed tracking system. Detection and tracking performance of feature prominence as well as three other fusion methods are evaluated using the peak-recall and distance accuracy measures. Experimental results show that feature prominence outperforms these other feature fusion methods. © 2012 ACM.

关键词： Target tracking

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NrityaGuru: A dance tutoring system for Bharatanatyam using kinect 6th

NrityaGuru: A dance tutoring system for Bharatanatyam using ...

引用

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

作者： Aich, Achyuta Mallick, Tanwi Bhuyan, Himadri B. G. S. Das, Partha Pratim Majumdar, Arun Kumar Indian Institute of Technology Kharagpur Kharagpur721302 India

ISBN: (纸本)9789811300196

indian Classical Dance (ICD) is a living heritage of India. Traditionally Gurus (teachers) are the custodians of this heritage. they practice and pass on the legacy through their Shishyas (disciples), often in undocumented forms. the preservation of the heritage, thus, remains limited in time and scope. Emergence of digital multimedia technology has created the opportunity to preserve heritage by ensuring that it can be accessible over a long period of time. However, there have been only limited attempts to use effective technologies either in the pedagogy of learning dance or in the preservation of heritage of ICD. In this context, the paper presents NrityaGuru – a tutoring system for Bharatanatyam – a form of ICD. Using Kinect Xbox to capture dance videos in multi-modal form, we design a system that can help a learner dancer identify deviations in her dance postures and movements against the prerecorded benchmark performances of the tutor (Guru). © Springer Nature Singapore Pte Ltd. 2018.

关键词： Multimedia systems

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Anomaly from motion: Unsupervised extraction of visual irregularity via motion prediction 6th

Anomaly from motion: Unsupervised extraction of visual irreg...

引用

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

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

ISBN: (纸本)9789811300196

the problem of automatically extracting anomalous events from any given video is a problem that has been researched from the early days of computer vision. It has still not been fully solved, showing that it is indeed not a trivial problem. the various challenges involved are lack of proper definition, varying scene structure and objects of interest in the scene, just a few to name. In this paper we propose a novel method to extract outliers from motion alone. We employ a stacked LSTM encoder-decoder structure to model the regular motion patterns of the given video sequence. the discrepancy between the motion predicted using the model and the actual observed motion in the scene is analyzed to detect anomalous activities. We perform extensive experimentation on the benchmark datasets of crowd anomaly analysis. We report State of the Art results across all the datasets. © Springer Nature Singapore Pte Ltd. 2018.

关键词： computer vision

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Supervised Hashing for Retrieval of Multimodal Biometric Data 1

引用

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： Sumesh, T.A. Namboodiri, Vinay Gupta, Phalguni Department of CSE Indian Institute of Technology Kanpur Kanpur India

ISBN: (数字)9789811513879

ISBN: (纸本)9789811513862

Biometric systems commonly utilize multi-biometric approaches where a person is verified or identified based on multiple biometric traits. However, requiring systems that are deployed usually require verification or identification from a large number of enrolled candidates. these are possible only if there are efficient methods that retrieve relevant candidates in a multi-biometric system. To solve this problem, we analyze the use of hashing techniques that are available for obtaining retrieval. We specifically based on our analysis recommend the use of supervised hashing techniques over deep learned features as a possible common technique to solve this problem. Our investigation includes a comparison of some of the supervised and unsupervised methods viz. Principal Component Analysis (PCA), Locality Sensitive Hashing (LSH), Locality-sensitive binary codes from shift-invariant kernels (SKLSH), Iterative quantization: A procrustean approach to learning binary codes (ITQ), Binary Reconstructive Embedding (BRE) and Minimum loss hashing (MLH) that represent the prevalent classes of such systems and we present our analysis for the following biometric data: Face, Iris, and Fingerprint for a number of standard datasets. the main technical contributions through this work are as follows: (a) Proposing Siamese network based deep learned feature extraction method (b) Analysis of common feature extraction techniques for multiple biometrics as to a reduced feature space representation (c) Advocating the use of supervised hashing for obtaining a compact feature representation across different biometrics traits. (d) Analysis of the performance of deep representations against shallow representations in a practical reduced feature representation framework. through experimentation with multiple biometrics traits, feature representations, and hashing techniques, we can conclude that current deep learned features when retrieved using supervised hashing can be a standard pipeline adopte

关键词： Biometrics

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vision based pose estimation of multiple peg-in-hole for robotic assembly 10

Vision based pose estimation of multiple peg-in-hole for rob...

引用

10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

作者： Nagarajan, Pitchandi Perumaal, S. Saravana Yogameena, B. Department of Mechanical Engineering Thiagarajar College of Engineering Madurai625015 India Department of Electronics and Communication Engineering Thiagarajar College of Engineering Madurai625015 India

vision sensors are used to estimate the pose (position and orienta-tion) of mating components in a vision assisted robotic peg-in-hole assembly which is a crucial step in aligning the mating hole-component with the corresponding moving peg-component. the accuracy of this estimation decides the performance of peg-in-hole robotic assembly with an appropriate mapping between the image and task environment using a fixed overhead camera or camera on robot arm. the wheel and hub assembly in automobile has multiple holes and pegs in their mating parts which lead to more complex pose estimation procedure. the success rate of the assembly process (without jamming) is affected by an inaccurate pose estimation which leads to lateral and/or axial misalignment between the mating components during its insertion phase. On this consideration, this work proposes a pose estimation algorithm for a multiple peg-in-hole assembly with the use of genetic algorithm based two-stage camera calibration procedure. the proposed algorithm has also been tested for its performance in estimating the pose of the multiple pegs in wheel-hub of a car. the result reveals that the proposed method estimates the pose of the pegs accurately with minimum re-projection error. © Springer International Publishing AG 2017.

关键词： Genetic algorithms

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Word-Wise Handwriting Based Gender Identification Using Multi-Gabor Response Fusion 4th

Word-Wise Handwriting Based Gender Identification Using Mult...

引用

4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

作者： Asadzadeh Kaljahi, Maryam Vidya Varshini, P.V. Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Guru, D.S. Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Vellore Institute of Technology VelloreTamil Nadu India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China Department of Studies in Computer Science Manasagangotri University of Mysuru Mysore India

ISBN: (纸本)9789811393600

Handwriting based gender identification at the word level is challenging due to free style writing, use of different scripts, and inadequate information. this paper presents a new method based on Multi-Gabor Response (MGR) fusion for gender identification at the word level. It first explores weighted-gradient features for word segmentation from text line images. For each word, the proposed method obtains eight Gabor response images. then it performs sliding window operation over MGR images to smooth the values. For each smoothed MGR images, we perform fusion operation that chooses the Gabor response value which contributes to the highest peak in the histogram. this process results in a feature matrix, which is fed to CNN for gender identification. Experimental results on our dataset (multi scripts) apart from English, and benchmark databases, namely, IAM, KHATT, and QUWI, which contain handwritten English and Arabic text, show that the proposed method outperforms the existing methods. © Springer Nature Singapore Pte Ltd. 2019.

关键词： image segmentation

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Automated industrial quality control of pipe stacks using computer vision 4th

Automated industrial quality control of pipe stacks using co...

引用

4th International conference on computer vision and image processing, CVIP 2019

作者： Chatterjee, Sayantan Chaudhuri, Bidyut B. Nandi, Gora C. Department of Robotics and Artificial Intelligence Indian Institute of Information Technology Allahabad AllahabadUttar Pradesh211012 India Department of Computer Vision and Pattern Recognition Indian Statistical Institute Kolkata KolkataWest Bengal700108 India

ISBN: (纸本)9789811540141

In this work, we describe an automated quality assurance system for pipes in warehouses and yards using simple handheld and mobile equipment like smartphone cameras. Currently, quality inspection for bent and crooked pipe ends is done manually, which entails additional labour costs and is relatively slower than a mechanised approach. We propose an efficient and robust method of detecting the perfectly circular cross-sections of the pipes in stacks using an adaptive variation of Hough Transform algorithm. As a multistage approach, our proposed method first intensifies the foreground features relative to the background and then applies Canny edge detection to obtain the gradient details. the gradient directions are in turn fed to the modified Hough Transform algorithm using Hough gradient to detect the cross-sections. the novel Hough Transform modification features a region-based processing in a "coarse to fine" manner by dividing the image into smaller grids and detecting circular cross-sections per grid, which has computational advantages of using a smaller accumulator and is less memory intensive. Experiments were performed on real industrial images to validate the efficiency of the proposed algorithm and the results show that the proposed method can successfully and accurately highlight the perfectly circular cross-sections while leaving the faulty pipe-ends undetected. © Springer Nature Singapore Pte Ltd 2020.

关键词： Hough transforms

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Cross-view kernel similarity metric learning using pairwise constraints for person re-identification 21

Cross-view kernel similarity metric learning using pairwise ...

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Ali, T.M. Feroz Chaudhuri, Subhasis Indian Institute of Technology Bombay Maharashtra Mumbai India

ISBN: (纸本)9781450391276

Person re-identification is the task of matching pedestrian images across non-overlapping cameras. In this paper, we propose an efficient kernel based similarity metric learning for learning non-linear features using small scale training data for practical person re-ID systems. the method employs non-linear mappings combined with cross-view discriminative subspace learning and cross-view distance metric learning based on pairwise similarity constraints. It is a natural extension of Cross-view Quadratic Discriminant Analysis (XQDA) from linear to non-linear model using kernels. In addition to outperforming XQDA, the proposed method is computationally very efficient compared to its baselines. Extensive experiments on four benchmark datasets show that our method attains competitive performance against state-of-the-art methods. Our code is available at https://***/ferozalitm/Efficient-Kernel-XQDA. © 2021 ACM.

关键词： Benchmarking

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