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

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Dynamic Hand Gesture Recognition using Convolutional Neural ...

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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Off-line Signature Identification Using Background and Foreground Information

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Off-line Signature Identification Using Background and Foreg...

proceedings of the Digital image Computing: Technqiues and Applications (DICTA)

作者： Srikanta Pal Alaei Alireza Umapada Pal Michael Blumenstein School of Information and Communication Technology Griffith University Gold Coast Australia Department of Studies in Computer Science University of Mysore Mysore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Biometric systems play an important role in the field of information security as they are extremely required for user authentication. Automatic signature recognition and verification is one of the biometric techniques, which is currently receiving renewed interest and is only one of several techniques used to verify the identities of individuals. Signatures provide a secure means for confirmation and authorization in legal documents. So nowadays, signature identification and verification becomes an essential component in automating the rapid processing of documents containing embedded signatures. In this paper, a technique for a bi-script off-line signature identification system is proposed. In the proposed signature identification system, the signatures of English and Bengali (Bangla) are considered for the identification process. Different features such as under sampled bitmaps, modified chain-code direction features and gradient features computed from both background and foreground components are employed for this purpose. Support Vector Machines (SVMs) and Nearest Neighbour (NN) techniques are considered as classifiers for signature identification in the proposed system. A database of 1554 English signatures and 1092 Bengali signatures are used to generate the experimental results. Various results based on different features are calculated and analysed. The highest accuracies of 99.41%, 98.45% and 97.75% are obtained based on the modified chain-code direction, under-sampled bitmaps and gradient features respectively using 1800 (1100 English+700 Bengali) samples for training and 846 (454 English+392 Bengali) samples for testing.

关键词： Feature extraction Support vector machines Handwriting recognition Training Educational institutions Kernel Databases

Proceedings of the 2016 International Conference on Image Pr...

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Foreword

proceedings of the 2016 International conference on image processing, computer vision, and Pattern Recognition, IPCV 2016 2016年

作者： Abd-Wahab, Mohd Helmy Al-Bakry, Abbas Al-Holou, Nizar Arabnia, Hamid R. Bhattacharya, Mahua Martinez Castillo, Juan Jose Daimi, Kevin Deligiannidis, Leonidas Djoudi, Lamia Atma Duong, Trung Eshaghian-Wilner, Mary Mehrnoosh Gravvanis, George A. Huang, Ruizhu Jandieri, George Kim, Byung-Gyu Kim, Tai-hoon Korovin, Iakov Lai, Guoming Lee, Hyo Jong Bin Mansor, Muhammad Naufal Marsh, Andrew Mostafaeipour, Ali Park, James J. Patil, Shashikant Ponalagusamy, R. Schaefer, Gerald Singh, Akash Solo, Ashu M.G. Swee, Sim Kok Thomas, Jaya Tinetti, Fernando G. Vladimir, Hahanov Wang, Shiuh-Jeng Yang, Mary Yoe, Hyun You, Jane Zhao, Wenbing Department of Computer Engineering University Tun Hussein Onn Malaysia Malaysia University of IT and Communications Baghdad Iraq Electrical and Computer Engineering Department IEEE/SEM-Computer Chapter University of Detroit Mercy DetroitMI United States University of Georgia United States ABV Indian Institute of Information Technology and Management MHRD Government of India India Acantelys Alan Turing Nikola Tesla Research Group and GIPEB Universidad Nacional Abierta Venezuela Computer Science and Software Engineering Programs Department of Mathematics Computer Science and Software Engineering University of Detroit Mercy DetroitMI United States Department of Computer Information Systems Wentworth Institute of Technology BostonMA United States Synchrone Technologies France Rutgers University State University of New Jersey New Jersey United States University of Southern California California United States Electrical Engineering University of California Los Angeles Los Angeles [UCLA CA United States Advanced Scientific Computing Applied Math and Applications Research Group Applied Mathematics and Numerical Computing and Department of ECE School of Engineering Democritus University of Thrace Xanthi Greece Texas Advanced Computing Center University of Texas AustinTX United States Georgian Technical University Tbilisi Georgia Institute of Cybernetics Georgian Academy of Science Georgia Multimedia Processing CommunicationsLab.[MPCL Department of Computer Science and Engineering College of Engineering SunMoon University Korea Republic of School of Information and Computing Science University of Tasmania Australia Southern Federal University Russia Computer Science and Technology Sun Yat-Sen University Guangzhou China Center for Advanced Image and Information Technology Division of Computer Science and Engineering Chonbuk National University Korea Republic of Faculty of Engineering Technology Kampus Uniciti Alam Universiti Malaysia Perlis UniMAP Malaysi

CluSpa: Computation Reduction in CNN Inference by exploiting Clustering and Sparsity 22

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CluSpa: Computation Reduction in CNN Inference by exploiting...

proceedings of the Second International conference on AI-ML Systems

作者： Imlijungla Longchar Amey Varhade Chetan Ingle Saurabh Baranwal Hemangee K. Kapoor CSE Indian Institute of Technology Guwahati IN

ISBN: (纸本)9781450398473

Convolutional Neural Networks (CNNs) have grown in popularity and usage tremendously over the last few years, spanning across different task such as computer vision tasks, natural language processing, video recognition, and recommender systems. Despite the algorithmic advancements that drove the growth of CNN still has considerable computational and memory overhead that poses challenges in achieving real-time performance. Each input image requires millions to even billions of elementary arithmetic operations before the network obtains the result. In CNNs, convolutional and pooling layers are followed by activation layers involving various activation functions. Hence, a lot of work has been done to reduce these costs in the last few years. Numerous optimizations have addressed at both hardware and software levels. In this paper, we propose a software-based solution for improving the performance of inference of networks. We suggest a technique for the approximate computation of the convolution operation based on clustering and sharing of weights. We have utilized Gaussian Mixture Models for clustering. We exploit weight sparsity to further reduce computations on top of the clustering method. We were able to achieve a considerable reduction in the MAC operations and the overall computation speedup on popular CNN architectures

关键词： Weight sharing CNNs Quantization Compression Deep Neural Networks DNNs Convolutional Neural Networks Performance Approximate Computing Sparsity Clustering Computation Reduction Inference

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Communications in computer and Information Science 2019年 1019 CCIS卷 v页

作者： Arora, Chetan Mitra, Kaushik Department of Computer Science and Engineering Indian Institute of Technology Delhi New Delhi India Department of Electrical Engineering Indian Institute of Technology Madras Chennai India

Anomaly Handwritten Text Detection for Automatic Descriptive Answer Evaluation 22

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Anomaly Handwritten Text Detection for Automatic Descriptive...

proceedings of the 2022 11th International conference on Computing and Pattern Recognition

作者： Nilanjana Chatterjee Palaiahnaakote Shivakumara Umapada Pal Tong Lu Yue Lu Computer Vision and Pattern Recognition Unit Indian Statistical Institute India Faculty of Computer Science and Information Technology University of Malaya Malaysia National Key Lab for Novel Software Technology Nanjing University China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University China

ISBN: (纸本)9781450397056

Although there are advanced technologies for character recognition, automatic descriptive answer evaluation is an open challenge for the document image analysis community due to large diversified handwritten text and answers to the question. This paper presents a novel method for detecting anomaly handwritten text in the responses written by the students to the questions. The method is proposed based on the fact that when the students are confident in answering questions, the students usually write answers legibly and neatly while they are not confident, they write sloppy writing which may not be easy for the reader to understand. To detect such anomaly handwritten text, we explore a new combination of Fourier transform and deep learning model for detecting edges. This result preserves the structure of handwritten text. For extracting features for classification of anomaly text and normal text, the proposed method studies the behavior of writing style, especially the variation at ascenders and descenders. Therefore, the proposed work draws principal axis which is invariant to rotation, scaling and some extent to distortion for the edge images. With respect to principal axis, the proposed method draws medial axis using uppermost and lowermost points. The distance between the medial axis and principal axis points are considered as feature vector. Further, the feature vector is passed to Artificial Neural Network for classification of anomaly text. The proposed method is evaluated by testing on our own dataset, standard dataset of gender identification (IAM) and handwritten forgery detection dataset (ACPR 2019). The results on different datasets show that the proposed work outperforms the existing methods.

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Communications in computer and Information Science 2019年 1020卷 v页

作者： Sundaram, Suresh Harit, Gaurav Electronics and Electrical Engineering Indian Institute of Technology Guwahati Guwahati India Computer Science and Engineering Indian Institute of Technology Jodhpur KarwarRajasthan India

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Communications in computer and Information Science 2020年 1249卷 v-vi页

作者： Venkatesh Babu, R. Prasanna, Mahadeva Namboodiri, Vinay P. Department of Computational and Data Sciences Indian Institute of Science Bangalore Bangalore India Department of Electrical Engineering Indian Institute of Technology Dharwad Dharwad India Indian Institute of Technology Kanpur Kanpur India

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Communications in computer and Information Science 2018年 841卷 VI页

作者： Rameshan, Renu Arora, Chetan Roy, Sumantra Dutta Indian Institute of Technology Mandi MandiHimachal Pradesh India Indraprastha Institute of Information Technology New Delhi India Indian Institute of Technology New Delhi India