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

共 1496 条记录，以下是421-430 订阅

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An Efficient Multi Lingual Optical Character Recognition System for indian Languages Through Use of Bharati Script 4th

An Efficient Multi Lingual Optical Character Recognition Sys...

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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： Vorugunti, Chandra Sekhar Chakravarthy, Srinivasa Pulabaigari, Viswanath Indian Institute of Information Technology-SriCity Chittoor India Indian Institute of Technology-Madras Chennai India

ISBN: (纸本)9789811393600

Optical character recognition performs a critical part in interpreting videos and documents. Document specific issues like low image quality, distortions, composite background, noise etc. and language specific issues like cursive connectivity among the characters etc. makes OCR challenging and erroneous for indian languages. The language specific challenges can be overcome by computing the script-based features and can achieve better accuracy. Computing the script based invariant features and patterns is computationally complex and error prone. In this background, we put forward Bharathi script (*** ) based OCR system in which the inherent drawbacks of indian scripts i.e. Hindi, Tamil, Telugu etc. are eliminated. The proposed OCR model has been tested on a synthetic dataset of documents of Bharathi script (in which Hindi scripts are converted to Bharathi script). Thorough experimental analysis with varied levels of noise confirms the promising results of character recognition accuracy of the proposed OCR model which out-performs the state-of-the-art OCR systems for indian scripts. The proposed model achieves 76.70% with test documents consists of 50% noise and 99.98% with test documents of 0% noise. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Optical character recognition

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A Temporal Activity Graph Kernel for Human Activity Classification 2018

A Temporal Activity Graph Kernel for Human Activity Classifi...

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

作者： Shobhanjana Kalita Arindam Karmakar Shyamanta M. Hazarika Dept of Computer Science & Engg Tezpur University Tezpur Assam Dept of Mechanical Engg IIT Guwahati Guwahati Assam

ISBN: (纸本)9781450366151

Human activities are predominantly spatio-temporal, involving spatial changes over time. Qualitative spatial relations between interacting entities are often used to describe spatial change. To derive such qualitative spatial relations, the interacting entities are approximated as a single bounding box or set of bounding boxes. A set of bounding boxes abstracting a single entity has been termed as an extended object; where each box is bounding a component. Extended object abstraction of spatial entities has been shown to be more effective for representation of human activities [10]. The temporal aspect of an activity is characterized through changing spatial relations between components of interacting extended objects over time. In this paper, we propose Temporal Activity Graph (TAG) based representation model to keep track of the sequences of relations between components of the extended objects. A kernel is designed for classification of spatio-temporal interactions in the TAG based model. The TAG kernel uses concepts of label sequence similarity and interestingness to compute similarity of a pair of TAGs. The TAG kernel is a generic solution that can be used with any kernel based method. Here, the kernel is used within a Support Vector Machine classifier. The TAG kernel based classification of activities is found on par with the state-of-the-art approaches for experiments performed on the Mind's Eye, the UT Interaction, and the SBU Kinect Interaction datasets.

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EXPERTNet: Exigent Features Preservative Network for Facial Expression Recognition 2018

EXPERTNet: Exigent Features Preservative Network for Facial ...

引用

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

作者： Monu Verma Jaspreet Kaur Bhui Santosh K. Vipparthi Girdhari Singh Dept. of Computer Science and Engineering Malaviya National Institute of Technology Jaipur India

ISBN: (纸本)9781450366151

Facial expressions have essential cues to infer the humans state of mind, that conveys adequate information to understand individuals' actual feelings. Thus, automatic facial expression recognition is an interesting and crucial task to interpret the humans cognitive state through the machine. In this paper, we proposed an Exigent Features Preservative Network (EXPERTNet), to describe the features of the facial expressions. The EXPERTNet extracts only pertinent features and neglect others by using exigent feature (ExFeat) block, mainly comprises of elective layer. Specifically, elective layer selects the desired edge variation features from the previous layer outcomes, which are generated by applying different sized filters as 1 x 1, 3 x 3, 5 x 5 and 7 x 7. Different sized filters aid to elicits both micro and high-level features that enhance the learnability of neurons. ExFeat block preserves the spatial structural information of the facial expression, which allows to discriminate between different classes of facial expressions. Visual representation of the proposed method over different facial expressions shows the learning capability of the neurons of different layers. Experimental and comparative analysis results over four comprehensive datasets: CK+, MMI DISFA and GEMEP-FERA, ensures the better performance of the proposed network as compared to existing networks.

关键词： EXPERTNet Exigent features Feature extraction Facial expression recognition

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HSD-CNN: Hierarchically self decomposing CNN architecture using class specific filter sensitivity analysis 2018

HSD-CNN: Hierarchically self decomposing CNN architecture us...

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

作者： K. SaiRam Jayanta Mukherjee Amit Patra Partha Pratim Das Indian Institute of Technology Kharagpur

ISBN: (纸本)9781450366151

Conventional convolutional neural networks (CNN) are trained on large domain datasets and are hence typically over-represented and inefficient in limited class applications. An efficient way to convert such large many-class pre-trained networks into small few-class networks is through a hierarchical decomposition of its feature maps. To alleviate this issue, we propose an automated framework for such decomposition in Hierarchically Self Decomposing CNN (HSD-CNN), in four steps. HSD-CNN is derived automatically using a class-specific filter sensitivity analysis that quantifies the impact of specific features on a class prediction. The decomposed hierarchical network can be utilized and deployed directly to obtain sub-networks for a subset of classes, and it is shown to perform better without the requirement of retraining these sub-networks. Experimental results show that HSD-CNN generally does not degrade accuracy if the full set of classes is used. Interestingly, when operating on known subsets of classes, HSD-CNN has an improvement in accuracy with a much smaller model size requiring much fewer operations. HSD-CNN flow is verified on the CIFAR10, CIFAR100 and CALTECH101 datasets. We report accuracies up to 85.6% (94.75%) on scenarios with 13 (4) classes of CIFAR100, using a pre-trained VGG-16 network on the full dataset. In this case, the proposed HSD-CNN requires 3.97x fewer parameters and has 71.22% savings in operations, in comparison to baseline VGG-16 containing features for all 100 classes.

关键词： classification hierarchical neural networks CNN clustering model transfer sub-networks

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A Deep Learning Architecture Based Dimensionality Reduction and Online Signature Verification 4th

A Deep Learning Architecture Based Dimensionality Reduction ...

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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： Vorugunti, Chandra Sekhar Pulabaigari, Viswanath Indian Institute of Information Technology-SriCity Chittoor India

ISBN: (纸本)9789811393600

In this paper, we propose a novel hybrid deep learning based autoencoder-CNN-Softmax architecture aims at obtaining reduced dimension feature set from raw feature set. The reduced feature set forms an input to CNN layers to learn deep global features. These global features are used to train the SoftMax layer for online signature classification. Ability to reduce the noisy features and to discover the hidden corelated features makes the proposed architecture light weight and efficient to use in critical applications like online signature verification (OSV) and to deploy in resource constraint mobile devices. We demonstrate the superiority of our model for feature correlation learning and signature classification by conducting experiments on standard datasets MCYT, SUSIG. The experimentation confirms that the proposed model achieves better accuracy (lower error rates) with a lesser number of features compared to the current state-of-the-art models. The proposed models yield state-of-the-art performance of 0.4% EER on MCYT-100 dataset and 3.47% with SUSIG dataset. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Classification (of information)

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Single image dehazing using image boundary constraint and nearest neighborhood optimization 11

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

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Gautam, Sidharth Gandhi, Tapan Kumar Panigrahi, B. K. Indian Inst Technol 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.

关键词： Median filters

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Non-rigid Registration using Spectral Graph Wavelet Features 11

Non-rigid Registration using Spectral Graph Wavelet Features

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Thottupattu, Alphin J. Yadav, Shivin Sharma, Avinash Int Inst Informat Technol Hyderabad India

ISBN: (纸本)9781450366151

image registration is an essential step in many computer vision applications which demands high accuracy for significantly random and complex deformations. In medical image processing applications image registration is a basic preprocessing step to non-rigidly align images from different acquisition environments to an atlas image. We propose a novel non-rigid image registration method to get more reliable registration even under noisy conditions with manageable time complexity. The proposed method explores the the inherent multi-resolution capability of wavelets to perform nonrigid registration in a graph environment. The inherent time complexity of wavelet feature map calculation is avoided using Chebyshev Polynomial approximations for the wavelet operators.

关键词： image registration

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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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Dynamic Hand Gesture Recognition using Convolutional Neural Network with RGB-D Fusion 18

Dynamic Hand Gesture Recognition using Convolutional Neural ...

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

作者： Bindu Verma Ayesha Choudhary School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi Delhi

ISBN: (纸本)9781450366151

In this paper, we propose a novel, real-time dynamic hand gesture recognition framework using convolutional neural network with depth and RGB data fusion. Hand gestures are a natural form of communication between humans as well as between human and machine. They also find important applications in areas such as sign language recognition, man-machine interaction and behavior understanding. Natural hand gestures are complex hand movements in space and time and are challenging to recognize. In our proposed framework, we use both RGB and depth data to automatically recognize dynamic hand gestures. Initially, we work with RGB and depth data separately. We find the motion history of the gesture performed with RGB data and independently with depth data to store the motion information of the moving hands. Motion history of the performed gesture stores the rich information of the movement. Then, we use transfer learning on two separate VGG16 networks, where one network is fine-tuned using RGB motion history while the other network is fine-tuned using depth motion history, to configure them for dynamic hand gesture recognition problem. Then, using the two fine-tunned VGG16 networks, we extract the features of both the motion history images obtained from RGB and depth data separately, for each dynamic hand gesture. We then, integrate the features obtained from both the networks using weighted summation, to accurately and robustly recognize the dynamic hand gesture. We perform experiments on standard and the publicly available dynamic hand gesture datasets and show that our method outperforms state of the art methods.

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A Chaos Based Robust and Secure image Hashing Framework 11

A Chaos Based Robust and Secure Image Hashing Framework

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Singh, Satendra Pal Bhatnagar, Gaurav Indian Inst Technol Jodhpur Rajasthan 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： image Hashing Normalization Discrete cosine transformation Singular value decomposition

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