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检索条件"机构=Computer Vision and Pattern Recognition"

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Fast and efficent multimodal eye biometrics using projective dictionary pair learning

Fast and efficent multimodal eye biometrics using projective...

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Congress on Evolutionary Computation

作者： Abhijit Das Prabir Mondal Umapada Pal Miguel Angel Ferrer Michael Blumenstein Institute for Integrated and Intelligent Systems Griffith University Queensland Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India IDeTIC University of Las Palmas de Gran Canaria Las Palmas Spain School of Software University of Technology Sydney Australia

ISBN: (纸本)9781509006243

This work proposes a projective pairwise dictionary learning-based approach for fast and efficient multimodal eye biometrics. The work uses a faster Projective pairwise Discriminative Dictionary Learning (DL) in contrast to the traditional DL which uses synthesis DL. Projective Pairwise Discriminative Dictionary (PPDD) uses a synthesis dictionary and an analysis dictionary jointly to achieve the goal of pattern representation and discrimination. As the PPDD process of DL is in contrast to the use of l 0 or l 1 -norm sparsity constraints on the representation coefficients adopted in most traditional DL, it works faster than other DL. Moreover, the blending of synthesis dictionary and an analysis dictionary also enhance the feature representation of the complex eye patterns. We employed the combination of sclera and iris traits to establish multimodal biometrics. The experimental study and analysis conducted fulfill the hypothesis we considered. In this work we employed a part of the UBIRIS version 1 dataset to conduct the experiments.

关键词： Dictionaries Iris recognition Training Time complexity pattern recognition

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Evaluation of feature sensitivity to training data inaccuracy in detection of retinal lesions

Evaluation of feature sensitivity to training data inaccurac...

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Workshops on Image Processing Theory, Tools and Applications, IPTA

作者： Lauri Laaksonen Antti Hannuksela Ela Claridge Pauli Fält Markku Hauta-Kasari Hannu Uusitalo Lasse Lensu Machine Vision and Pattern Recognition Laboratory Lappeenranta university of Technology Lappeenranta Finland School of Computer Science The University of Birmingham United Kingdom School of Computing University of Eastern Finland Finland Department of Ophthalmology University of Tampere Finland TAUH Eye Center Tampere University Hospital Finland

computer aided diagnostic and segmentation tools have become increasingly important in reducing the workload of medical experts performing diagnosis, monitoring and documentation of various eye diseases such as age-related macular degeneration (AMD), diabetic retinopathy (DR) and glaucoma. Supervised methods have been developed for the segmentation and detection of lesions, and the reported performance has been good. The supervised methods, however, need representative data to properly train the classifier. Inaccuracies in the ground truth may have a significant impact on the performance of a supervised method as the training data are not representative. In this study, a quantitative evaluation of the sensitivity of different image features, including colour, texture, edge and higher-level features, to inaccuracy in the ground truth on exudates is presented. A mean decrease of approx. 20% in sensitivity and 13% in specificity was observed when using the most inaccurate training data.

关键词： Training data Sensitivity Retina Image color analysis Image segmentation Feature extraction Lesions

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Offline handwritten Devanagari word recognition: Information fusion at feature and classifier levels

Offline handwritten Devanagari word recognition: Information...

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Asian Conference on pattern recognition (ACPR)

作者： Bikash Shaw Ujjwal Bhattacharya Swapan Kumar Parui Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata

ISBN: (纸本)9781479961016

This article presents our recent study on fusion of information at feature and classifier output levels for improved performance of offline handwritten Devanagari word recognition. We consider here two state-of-the-art features, viz., Directional Distance Distribution (DDD) and Gradient-Structural-Concavity (GSC) features along with multi-class SVM classifiers. Here, we study various combinations of DDD features along with one or more features from the GSC feature set. We experiment by presenting different combined feature vectors as input to SVM classifiers. Also, the output vectors of different SVM classifiers fed with different feature vectors are combined by another SVM classifier. The combination of the outputs of two SVMs each being fed with a different feature vector provides superior performance to the performance of a single SVM classifier fed with the combined feature vector. Experimental results are obtained on a large handwritten Devanagari word sample image database of 100 Indian town names. The recognition results on its test samples show that SVM recognition output of DDD features combined with the SVM output of GSC features improves the final recognition accuracy significantly.

关键词： Handwriting recognition Support vector machines Feature extraction Shape Databases Automation

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Writer Identification from offline isolated Bangla characters and numerals

Writer Identification from offline isolated Bangla character...

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International Conference on Document Analysis and recognition

作者： Chandranath Adak Bidyut B. Chaudhuri Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Writer identification is an essential component in computational forensic. In this paper, we attempt to do this job based only on isolated characters and numerals. For that, at first, some points of interest (keypoints) on the image are detected by structural analysis and SIFT based detector. Then we calculate a set of features within a certain neighborhood of the keypoint and employ fusion rule on multiple probabilistic SVM classifiers output for writer identification. For experimental analysis, a database containing 212,300 isolated Bangla orthosyllabic characters and numerals are generated with the help of 100 writers. We obtain fairly good result to identify a writer. We also try to find a small set of highly discriminative characters storing extra information about the writing style of an individual.

关键词： Chlorine

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New texture-spatial features for keyword spotting in video images 3

New texture-spatial features for keyword spotting in video i...

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3rd IAPR Asian Conference on pattern recognition, ACPR 2015

作者： Shivakumara, Palaiahnakote Liang, Guozhu Roy, Sangheeta Pal, Umapada Lu, Tong Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia National Key Lab for Novel Software Technology Nanjing University Nanjing China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781479961009

Keyword spotting in video document images is challenging due to low resolution and complex background of video images. We propose the combination of Texture-Spatial-Features (TSF) for keyword spotting in video images without recognizing them. First, a segmentation method extracts words from text lines in each video image. Then we propose the set of texture features for identifying text candidates in the word image with the help of k-means clustering. The proposed method finds proximity between text candidates to study the spatial arrangement of pixels that result in feature vectors for spotting words in the input frame. The proposed method is evaluated on word images of different fonts, contrasts, backgrounds and font sizes, which are chosen from standard databases such as ICDAR 2013 video and our video data. Experimental results show that the proposed method outperforms the existing method in terms of recall, precision and f-measure. © 2015 IEEE.

关键词： Image segmentation

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CNN based common approach to handwritten character recognition of multiple scripts

CNN based common approach to handwritten character recogniti...

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International Conference on Document Analysis and recognition

作者： Durjoy Sen Maitra Ujjwal Bhattacharya Swapan K. Parui Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata-108 India

ISBN: (纸本)9781479918065

There are many scripts in the world, several of which are used by hundreds of millions of people. Handwritten character recognition studies of several of these scripts are found in the literature. Different hand-crafted feature sets have been used in these recognition studies. However, convolutional neural network (CNN) has recently been used as an efficient unsupervised feature vector extractor. Although such a network can be used as a unified framework for both feature extraction and classification, it is more efficient as a feature extractor than as a classifier. In the present study, we performed certain amount of training of a 5-layer CNN for a moderately large class character recognition problem. We used this CNN trained for a larger class recognition problem towards feature extraction of samples of several smaller class recognition problems. In each case, a distinct Support Vector Machine (SVM) was used as the corresponding classifier. In particular, the CNN of the present study is trained using samples of a standard 50-class Bangla basic character database and features have been extracted for 5 different 10-class numeral recognition problems of English, Devanagari, Bangla, Telugu and Oriya each of which is an official Indian script. recognition accuracies are comparable with the state-of-the-art.

关键词： Handwriting recognition Databases Accuracy Training

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Fast single image dehazing through Edge-Guided Interpolated Filter

Fast single image dehazing through Edge-Guided Interpolated ...

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IAPR International Conference on Machine vision Applications (MVA)

作者： Ximei Zhu Ying Li Yu Qiao Shenzhen Key lab of Computer Vision Pattern Recognition The Chinese University of Hong Kong Hong Kong SAR

ISBN: (纸本)9781479982479

Images and videos taken in foggy weather often suffer from low visibility. Recent studies demonstrate the effectiveness of dark channel prior [3] and guided filter [4] based approaches for image dehazing. However, these methods require high computational cost which makes them infeasible for realtime and embedding systems. In this paper, we propose Edge-Guided Interpolated Filter (EGIF) for fast image and video dehazing. The main contributions are twofold. Firstly, we develop Guided Interpolated Filter (GIF) to significantly speed up the estimation of transmission map, which is the most computational cost step in previous methods. Secondly, we utilize edge map as guidance image in GIF to enhance the fine details in dehazed images. Experimental results show that GIF can largely improve the computational efficiency and achieve comparable dehazing performance as previous guided filter based methods. EGIF can further enhance the sharpness of transmission map. Our method can achieve real-time processing for image of size 1024 × 768 with single CPU core (2GHz).

关键词： Image edge detection Interpolation Videos Real-time systems Image restoration computer vision Atmosphere

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Video Script Identification Using a Combination of Textural Features

Video Script Identification Using a Combination of Textural ...

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International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： Zumra Malik Ali Mirza Akram Bennour Imran Siddiqi Chawki Djeddi Center of Computer Vision & Pattern Recognition Bahria University Islamabad Pakistan Departement of Mathematics and Computer Science Larbi Tebessi University Tebessa Algeria

This paper presents a system for script recognition of the text appearing in video frames. The textual content in videos is generally extracted and recognized for development of text based indexing and retrieval systems. If the text in videos appears only in a single script, the output of text detector is directly fed to a video Optical Character recognition (OCR) system for recognition. However, in cases where text may appear in multiple scripts, a script recognition module is required to recognize the script of the text so that it can be processed by the respective OCR. We propose a video script recognition system that considers text in each script as a unique texture. A number of texture measures are extracted from text blocks and an artificial neural network is trained to learn to distinguish between different scripts. The system evaluated on video text blocks in five different scripts (Arabic, English, Urdu, Hindi and Chinese) reported promising recognition rates. In addition to the performance of individual textural features, different combinations of texture measures were investigated which realized interesting results.

关键词： Text recognition Image recognition Histograms Optical character recognition software Fractals Character recognition Indexing

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A direct approach for word and character segmentation in run-length compressed documents with an application to word spotting

A direct approach for word and character segmentation in run...

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International Conference on Document Analysis and recognition

作者： Mohammed Javed P. Nagabhushan B.B. Chaudhuri Department of Studies in Computer Science University of Mysore Mysore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Segmentation of a text document into lines, words and characters is an important objective in application like OCR and related analytics. However in today's scenario, the documents are compressed for archival and transmission efficiency. Text segmentation in compressed documents warrants decompression, and needs additional computing resources. In this backdrop, the paper proposes a method for text segmentation directly in run-length compressed, printed English text documents. Line segmentation is done using the projection profile technique. Further segmentation into words and characters is accomplished by tracing the white runs along the base region of the text line. During the process, a run based region growing technique is applied in the spatial neighborhood of the white runs to trace the vertical space between the characters. After detecting the character spaces in the entire text line, the decision of word space and character space is made by computing the average character space. Subsequently based on the spatial position of the detected words and characters, their respective compressed segments are extracted. The proposed algorithm is tested with 1083 compressed text lines, and F-measure of 97.93% and 92.86% respectively for word and character segmentation are obtained. Finally an application of word spotting is also presented.

关键词： Optical character recognition software Image coding Adaptation models

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Automatic extraction of correlation-entropy features for text document analysis directly in run-length compressed domain

Automatic extraction of correlation-entropy features for tex...

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International Conference on Document Analysis and recognition

Automatic feature extraction plays a pivotal role in defining the overall performance of any Document Image Analysis system, which conventionally operates directly over uncompressed images, although most of the real time systems such as fax machines, digital libraries and e-governance applications accrue and archive the documents in the compressed form for the sake of storage and transfer efficiencies. However, this infers that the compressed documents need to be decompressed before carrying out any operation or analysis which warrants additional computing resources. This limitation in existing systems instigates motivation to explore for feature extraction techniques directly from the compressed documents and eventually design a document analysis system that works directly in compressed domain. Therefore, this research work proposes to extract novel correlation-entropy features directly from run-length compressed TIFF documents. Further, the research work also investigates different methods to demonstrate some of the straight forward application of the proposed features in carrying out compressed document image analysis such as text and non-text component detection, and subsequently performing compressed text line segmentation and characterization, all carried out in the compressed version of the printed text document without going through the stage of decompression. Finally, the experimental results reported validate the developed algorithms and also illustrate that the proposed features are quite powerful in distinguishing compressed text and non-text components.

关键词： Image coding Image segmentation

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