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A CNN Based Framework for Unistroke Numeral recognition in Air-Writing

A CNN Based Framework for Unistroke Numeral Recognition in A...

引用

International Workshop on Frontiers in Handwriting recognition

作者： Prasun Roy Subhankar Ghosh Umapada Pal Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata WB India

Air-writing refers to virtually writing linguistic characters through hand gestures in three dimensional space with six degrees of freedom. In this paper a generic video camera dependent convolutional neural network (CNN) based air-writing framework has been proposed. Gestures are performed using a marker of fixed color in front of a generic video camera followed by color based segmentation to identify the marker and track the trajectory of marker tip. A pre-trained CNN is then used to classify the gesture. The recognition accuracy is further improved using transfer learning with the newly acquired data. The performance of the system varies greatly on the illumination condition due to color based segmentation. In a less fluctuating illumination condition the system is able to recognize isolated unistroke numerals of multiple languages. The proposed framework achieved 97.7%, 95.4% and 93.7% recognition rate in person independent evaluation over English, Bengali and Devanagari numerals, respectively.

关键词： Training Testing Sensors Cameras Writing Image color analysis Convolution

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Off-line signature identification using background and foreground information

Off-line signature identification using background and foreg...

引用

2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011

作者： Pal, Srikanta Alireza, Alaei Pal, Umapada Blumenstein, Michael School of Information and Communication Technology Griffith University Gold Coast QLD Australia Department of Studies in Computer Science University of Mysore Mysore 570 006 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9780769545882

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. © 2011 IEEE.

关键词： Biometrics

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ARNET: ACTIVE-REFERENCE NETWORK FOR FEW-SHOT IMAGE SEMANTIC SEGMENTATION

ARNET: ACTIVE-REFERENCE NETWORK FOR FEW-SHOT IMAGE SEMANTIC ...

引用

2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Shi, Guangchen Wu, Yirui Palaiahnakote, Shivakumara Pal, Umapada Lu, Tong College of Computer and Information Hohai University China Department of Computer System and Information Technology University of Malaya Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute India National Key Lab for Novel Software Technology Nanjing University China

ISBN: (纸本)9781665438643

To make predictions on unseen classes, few-shot segmentation becomes a research focus recently. However, most methods build on pixel-level annotation requiring quantity of manual work. Moreover, inherent information on same-category objects to guide segmentation could have large diversity in feature representation due to differences in size, appearance, layout, and so on. To tackle these problems, we present an active-reference network (ARNet) for few-shot segmentation. The proposed active-reference mechanism not only supports accurately co-occurrent objects in either support or query images, but also relaxes high constraint on pixel-level labeling, allowing for weakly boundary labeling. To extract more intrinsic feature representation, a category-modulation module (CMM) is further applied to fuse features extracted from multiple support images, thus forgetting useless and enhancing contributive information. Experiments on PASCAL-5i dataset show the proposed method achieves a m-IOU score of 56.5% for 1-shot and 59.8% for 5-shot segmentation, being 0.5% and 1.3% higher than current state-of-the-art method. © 2021 IEEE

关键词： Semantic Segmentation

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A method to generate synthetically warped document image 4th

A method to generate synthetically warped document image

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4th International Conference on computer vision and Image Processing, CVIP 2019

作者： Garai, Arpan Biswas, Samit Mandal, Sekhar Chaudhuri, Bidyut B. Department of Computer Science and Technology Indian Institute of Engineering Sciences and Technology Shibpur HowrahWest Bengal711103 India Techno India University Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9789811540141

The digital camera captured document images may often be warped and distorted due to different camera angles or document surfaces. A robust technique is needed to solve this kind of distortion. The research on dewarping of the document suffers due to the limited availability of benchmark public dataset. In recent times, deep learning based approaches are used to solve the problems accurately. To train most of the deep neural networks a large number of document images is required and generating such a large volume of document images manually is difficult. In this paper, we propose a technique to generate a synthetic warped image from a flat-bedded scanned document image. It is done by calculating warping factors for each pixel position using two warping position parameters (WPP) and eight warping control parameters (WCP). These parameters can be specified as needed depending upon the desired warping. The results are compared with similar real captured images both qualitative and quantitative way. © Springer Nature Singapore Pte Ltd 2020.

关键词： Deep neural networks

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A robust block oriented watermarking scheme in spatial domain 1

引用

4th International Conference on Information and Communications Security, ICICS 2002

作者： Das, Tanmoy Kanti Maitra, Subhamoy Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B T Road Calcutta700 108 India Applied Statistics Unit Indian Statistical Institute 203 B T Road Calcutta700 108 India

In this paper we present an invisible spatial domain watermarking technique. The technique divides the image into n small blocks and the intensity of some of these blocks are modified depending on the key, which is a ... 详细信息

ISBN: (数字)9783540361596

ISBN: (纸本)3540001646

关键词： Frequency domain analysis

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Automatic bridge crack detection - A texture analysis-based approach 6

Automatic bridge crack detection - A texture analysis-based ...

引用

6th IAPR TC3 International Workshop on Artificial Neural Networks for pattern recognition, ANNPR 2014

作者： Chanda, Sukalpa Bu, Guoping Guan, Hong Jo, Jun Pal, Umapada Loo, Yew-Chaye Blumenstein, Michael School of Information and Communication Technology Griffith University QLD4222 Australia School of Engineering Griffith University QLD4222 Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India

ISBN: (纸本)9783319116556

To date, identifying cracks in bridges and determining bridge conditions primarily involve manual labour. Bridge inspection by human experts has some drawbacks such as the inability to physically examine all parts of the bridge, sole dependency on the expert knowledge of the bridge inspector. Moreover it requires proper training of the human resource and overall it is not cost effective. This article proposes an automatic bridge inspection approach exploiting wavelet-based image features along with Support Vector Machines for automatic detection of cracks in bridge images. A two-stage approach is followed, where in the first stage a decision is made as whether an image should undergo a pre-processing step (depending on image characteristics), and later in the second stage, wavelet features are extracted from the image using a sliding window-based technique. We obtained an overall accuracy of 92.11% while conducting experiments even on noisy and complex bridge images. © Springer International Publishing Switzerland 2014.

关键词： Crack detection

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Cryptanalysis of optimal differential energy watermarking (DEW) and a modified robust scheme 3rd

Cryptanalysis of optimal differential energy watermarking (D...

引用

3rd International Conference on Cryptology in India, INDOCRYPT 2002

ISBN: (纸本)9783540362319

In this paper we provide a cryptanalysis of the well known "Optimal Differential Energy Watermarking (DEW)" scheme. The DEW scheme divides the image into some disjoint regions (each region containing two subregions). The watermark is basically a secret binary string where each individual bit information is inserted in one of the regions by modifying the high frequency DCT (Discrete Cosine Transform) coefficients. This modification creates required energy difference between two subregions. We here modify the high frequency components so that this energy difference vanishes and in turn extraction of watermark signal becomes impossible, making the cryptanalysis successful. Moreover, we modify the DEW scheme by inserting the bit information in low frequency components instead of high frequency components and propose an oblivious robust watermarking strategy which can trace the buyer too. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Discrete cosine transforms

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ICDAR 2019 CROHME + TFD: Competition on recognition of handwritten mathematical expressions and typeset formula detection 15

ICDAR 2019 CROHME + TFD: Competition on recognition of handw...

引用

15th IAPR International Conference on Document Analysis and recognition, ICDAR 2019

作者： Mahdavi, Mahshad Zanibbi, Richard Mouchere, Harold Viard-Gaudin, Christian Garain, Utpal Document and Pattern Recognition Lab Rochester Institute of Technology RochesterNY United States Christian Viard-Gaudin LS2N-UMR CNRS 6004 University of Nantes Nantes France Computer Vision and Pattern Recognition Unit Centre for Artif. Intel. and Mach. Leaning Indian Statistical Institute Kolkata India

ISBN: (纸本)9781728128610

We summarize the tasks, protocol, and outcome for the 6th Competition on recognition of Handwritten Mathematical Expressions (CROHME), which includes a new formula detection in document images task (+ TFD). For CROHME + TFD 2019, participants chose between two tasks for recognizing handwritten formulas from 1) online stroke data, or 2) images generated from the handwritten strokes. To compare LATEX strings and the labeled directed trees over strokes (label graphs) used in previous CROHMEs, we convert LATEX and stroke-based label graphs to label graphs defined over symbols (symbol-level label graphs, or symLG). More than thirty (33) participants registered for the competition, with nineteen (19) teams submitting results. The strongest formula recognition results were produced by the USTC-iFLYTEK research team, for both stroke-based (81%) and image-based (77%) input. For the new typeset formula detection task, the Samsung R&D Institute Ukraine (Team 2) obtained a very strong F-score (93%). System performance has improved since the last CROHME-still, the competition results suggest that recognition of handwritten formulae remains a difficult structural pattern recognition task. © 2019 IEEE.

关键词： Trees (mathematics)

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A new wrist vein biometric system

A new wrist vein biometric system

引用

2014 IEEE Symposium Series on Computational Intelligence, IEEE SSCI 2014 - 2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management, CIBIM 2014

作者： Das, Abhijit Pal, Umapada Ballester, Miguel Angel Ferrer Blumenstein, Michael Institute for Integrated and Intelligent Systems Griffith University GriffithQLD Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India IDeTIC University of Las Palmas de Gran Canaria Spain

ISBN: (纸本)9781479945344

In this piece of work a wrist vein pattern recognition and verification system is proposed. Here the wrist vein images from the PUT database were used, which were acquired in visible spectrum. The vein image only highlights the vein pattern area so, segmentation was not required. Since the wrist's veins are not prominent, image enhancement was performed. An Adaptive Histogram Equalization and Discrete Meyer Wavelet were used to enhance the vessel patterns. For feature extraction, the vein pattern is characterized with Dense Local Binary pattern (D-LBP). D-LBP patch descriptors of each training image are used to form a bag of features, which was used to produce the training model. Support Vector Machines (SVMs) were used for classification. An encouraging Equal Error Rate (EER) of 0.79% was achieved in our experiments. © 2014 IEEE.

关键词： Support vector machines

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A new efficient and adaptive sclera recognition system

A new efficient and adaptive sclera recognition system

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2014 IEEE Symposium Series on Computational Intelligence, IEEE SSCI 2014 - 2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management, CIBIM 2014

ISBN: (纸本)9781479945344

In this paper an efficient and adaptive biometric sclera recognition and verification system is proposed. Sclera segmentation was performed by Fuzzy C-means clustering. Since the sclera vessels are not prominent, in order to make them clearly visible image enhancement was required. Adaptive histogram equalization, followed by a bank of Discrete Meyer Wavelet was used to enhance the sclera vessel patterns. Feature extraction was performed by, Dense Local Directional pattern (D-LDP). D-LDP patch descriptors of each training image are used to form a bag of features;further Spatial Pyramid Matching was used to produce the final training model. Support Vector Machines (SVMs) are used for classification. The UBIRIS version 1 dataset was used here for experimentation of the proposed system. To investigate regarding sclera patterns adaptively with respect to change in environmental condition, population, data accruing technique and time span two different session of the mention dataset are utilized. The images in two sessions are different in acquiring technique, representation, number of individual and they were captured in a gap of two weeks. An encouraging Equal Error Rate (EER) of 3.95% was achieved in the above mention investigation. © 2014 IEEE.

关键词： Support vector machines

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