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检索条件"机构=Computer Vision and Pattern"

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OCR from Video Stream of Book Flipping

OCR from Video Stream of Book Flipping

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Asian Conference on pattern Recognition (ACPR)

作者： Dibyayan Chakraborty Partha Pratim Roy Jose M. Alvarez Umapada Pal Department of computer Science Ramakrishna Mission Vivekananda University Howrah India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India NICTA Australia

Optical Character Recognition (OCR) in video stream of flipping pages is a challenging task because flipping at random speed cause difficulties to identify frames that contain the open page image (OPI) for better readability. Also, low resolution, blurring effect shadows add significant noise in selection of proper frames for OCR. In this work, we focus on the problem of identifying the set of optimal representative frames for the OPI from a video stream of flipping pages and then perform OCR without using any explicit hardware. To the best of our knowledge this is the first work in this area. We present an algorithm that exploits cues from edge information of flipping pages. These cues, extracted from the region of interest (ROI) of the frame, determine the flipping or open state of a page. Then a SVM classifier is trained with the edge cue information for this determination. For each OPI we obtain a set of frames. Next we choose the central frame from that set of frames as the representative frame of the corresponding OPI and perform OCR. Experiments are performed on video documents recorded using a standard resolution camera to validate the frame selection algorithm and we have obtained 88% accuracy. Also, we have obtained character recognition accuracy of 82% and word recognition accuracy of 77% from such book flipping OCR.

关键词： Optical character recognition software Streaming media Accuracy Cameras Image edge detection Feature extraction Noise

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Duplicate open page removal from video stream of book flipping

Duplicate open page removal from video stream of book flippi...

引用

National Conference on computer vision, pattern Recognition, Image Processing and Graphics (NCVPRIPG)

Book flipping scanning refers to the process of recording a book while the user performs the flipping action of its pages. In recent years it has gained much attention as it reduces the workload of book digitization significantly. It is a challenging task because flipping at random speed and direction causes difficulties to identify distinct open page images (OPI) which represent each page of the book. In this paper, we propose a fast technique for removing duplicate open pages introduced in the video stream due to erroneous flipping. We present an algorithm that exploits cues from edge information of flipping pages. The nature of the cues extracted from the region of interest (ROI) of the frame, determines the flipping or an open state of a page whereas temporal position a flipping page determines the direction of the flipping. Combining these information we decide whether an open page image is a duplicate or not. Experiments are performed on video documents recorded using a standard resolution camera to validate the duplicate open page removal algorithm and we have obtained 95% accuracy.

关键词： Streaming media Feature extraction Cameras Optical character recognition software Image edge detection Accuracy Performance evaluation

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Semi-supervised Online Learning of Handwritten Characters Using a Bayesian Classifier

Semi-supervised Online Learning of Handwritten Characters Us...

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Asian Conference on pattern Recognition (ACPR)

作者： Rituraj Kunwar Umapada Pal Michael Blumenstein School of Information and Communication Technology Griffith University Queensland Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

This paper addresses the problem of creating a handwritten character recognizer, which makes use of both labelled and unlabelled data to learn continuously over time to make the recognisor adaptable. The proposed method makes learning possible from a continuous inflow of a potentially unlimited amount of data without the requirement for storage. It highlights the use of unlabelled data for better parameter estimation, especially when labelled data is scarce and expensive unlike unlabelled data. We introduce an algorithm for learning from labelled and unlabelled samples based on the combination of novel online ensemble of the Randomized Naive Bayes classifiers and a novel incremental variant of the Expectation Maximization (EM) algorithm. We make use of a weighting factor to modulate the contribution of unlabelled data. An empirical evaluation of the proposed method on Tamil handwritten base character recognition proves efficacy of the proposed method to carry out incremental semi-supervised learning and producing accuracy comparable to state-of-the-art batch learning method. Online handwritten Tamil characters from the IWFHR 2006 competition dataset was used for evaluating the proposed method.

关键词： Accuracy Training Handwriting recognition Character recognition Semisupervised learning Hidden Markov models

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Complex 3D General Object Reconstruction from Line Drawings

Complex 3D General Object Reconstruction from Line Drawings

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International Conference on computer vision (ICCV)

作者： Linjie Yang Jianzhuang Liu Xiaoou Tang Department of Information Engineering Chinese University of Hong Kong Shenzhen Key Lab of Computer Vision and Pattern Recognition Chinese Academy of Sciences China

ISBN: (纸本)9781479928415

An important topic in computer vision is 3D object reconstruction from line drawings. Previous algorithms either deal with simple general objects or are limited to only manifolds (a subset of solids). In this paper, we propose a novel approach to 3D reconstruction of complex general objects, including manifolds, non-manifold solids, and nonsolids. Through developing some 3D object properties, we use the degree of freedom of objects to decompose a complex line drawing into multiple simpler line drawings that represent meaningful building blocks of a complex object. After 3D objects are reconstructed from the decomposed line drawings, they are merged to form a complex object from their touching faces, edges, and vertices. Our experiments show a number of reconstruction examples from both complex line drawings and images with line drawings superimposed. Comparisons are also given to indicate that our algorithm can deal with much more complex line drawings of general objects than previous algorithms.

关键词： Solids Three-dimensional displays Manifolds Image reconstruction computer vision Search problems Image edge detection

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Dirichlet Process Mixtures of Multinomials for Data Mining in Mice Behaviour Analysis

Dirichlet Process Mixtures of Multinomials for Data Mining i...

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International Conference on computer vision Workshops (ICCV Workshops)

作者： Matteo Zanotto Diego Sona Vittorio Murino Francesco Papaleo Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genova Italy Neuroscience and Brain Technologies Istituto Italiano di Tecnologia Italy

Automatic analysis of rodents behaviour has received growing attention in recent years as rodents are the reference species for large scale pharmacological and genetic screenings. In this paper we propose a new method to identify prototypical high-level behavioural patterns which go beyond simple atomic actions. The method is embedded in a data mining pipeline thought to support behavioural scientists in exploratory data analysis and hypothesis formulation. A case study is presented where the method is capable of learning high-level behavioural prototypes which help discriminating between two strains of mouse having known differences in their behaviour.

关键词： Mice Prototypes Data mining Pipelines Strain computer vision Histograms

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Sclera Recognition - A Survey

Sclera Recognition - A Survey

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Asian Conference on pattern Recognition (ACPR)

作者： Abhijit Das Umapada Pal Michael Blumenstein Miguel Angel Ferrer Ballester Institute for Integrated and Intelligent Systems Griffith University Queensland Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

This paper presents a survey on sclera-based biometric recognition. Among the various biometric methods, sclera is one of the novel and promising biometric techniques. The sclera, a white region of connective tissue and blood vessels, surrounds the iris. A survey of the techniques available in the area of sclera biometrics will be of great assistance to researchers, and hence a comprehensive effort is made in this article to discuss the advancements reported in this regard during the past few decades. As a limited number of publications are found in the literature, an attempt is made in this paper to increase awareness of this area so that the topic gains popularity and interest among researchers. In this survey, a brief introduction is given initially about the sclera biometric, which is subsequently followed by background concepts, various pre-processing techniques, feature extraction and finally classification techniques associated with the sclera biometric. Benchmarking databases are very important for any pattern recognition related research, so the databases related with this work is also discussed. Finally, our observations, future scope and existing difficulties, which are unsolved in sclera biometrics, are discussed. We hope that this survey will serve to focus more researcher attention towards the emerging sclera biometric.

关键词： Iris recognition Biomedical imaging Image segmentation Feature extraction pattern recognition Image color analysis Image recognition

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Rapid disparity prediction for dynamic scenes

Rapid disparity prediction for dynamic scenes

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9th International Symposium on Advances in Visual Computing, ISVC 2013

作者： Jiang, Jun Cheng, Jun Chen, Baowen Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Chinese University of Hong Kong Hong Kong Hong Kong Shsenzhen Institute of Information Technology China Guangdong Provincial Key Laboratory of Robotics and Intelligent System China Shenzhen Key Laboratory of Computer Vision and Pattern Recognition China

ISBN: (纸本)9783642419133

Real-time 3D sensing plays a critical role in robotic navigation, video surveillance and human-computer interaction, etc. When computing 3D structures of dynamic scenes from stereo sequences, spatiotemporal stereo and scene flow methods can produce temporally coherent disparity. However, most existing methods do not utilize the previous disparity map sufficiently to compute the next disparity map, and the searching space of correspondences limits the speed of disparity computation for each image pair. This paper proposes an effective scheme to predict disparity maps from stereo sequences. In particular, we apply a robust 3D registration algorithm based on the angular-invariant feature to estimate the ego-motion of the stereo rig between consecutive frames, and present the transformation between consecutive disparity maps. The scheme can produce a sequence of temporally coherent disparity maps rapidly. We apply the new scheme to real outdoor scenes, and thorough empirical studies indicate the effectiveness of the new scheme for practical applications. © 2013 Springer-Verlag.

关键词： Human computer interaction

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Robust Superdirective End-Fire Arrays

Robust Superdirective End-Fire Arrays

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Oceans 2013 - Norway

作者： A. Trucco F. Traverso M. Crocco Dept. of Electrical Electronic Telecommunications Engineering and Naval Architecture (DITEN) University of Genoa Pattern Analysis & Computer Vision Istituto Italiano di Tecnologia (IIT)

ISBN: (纸本)9781479900022

Oversteering is a technique to achieving robust superdirective performance by end-fire arrays whose transducers are spaced less than half wavelength. To reduce the beamwidth, the main-lobe is pushed past end-fire by the introduction of additional delays. The reduction of the main-lobe sensitivity produces, in turn, an increase of the side-lobe level and a fall of the white noise gain. This paper proposes a method to compute the real-valued weight coefficients that yield the maximum directivity for a given oversteering amount and assure a white noise gain above a given threshold. If this optimization is carried out in parallel with the exhaustive search for the best oversteering amount, the optimum oversteering performance can be reached. Finally, it is shown that for a linear array composed of 8 transducers, the oversteering technique yields a directivity which is very close to that provided by the optimum complex-valued weight coefficients, requiring a simpler processing system and assuring the same robustness (i.e., the two solutions show the same white noise gain).

关键词： End-fire arrays Superdirective beamforming Oversteering Maximum directivity White noise gain

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A semantic model for video based face recognition

A semantic model for video based face recognition

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International Conference on Information and Automation (ICIA)

作者： Dihong Gong Kai Zhu Zhifeng Li Yu Qiao Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781479913329

Video-based face recognition has attracted a great deal of attention in recent years due to its wide applications. The challenge of video-based face recognition comes from several aspects. First, video data involves many frames, which increases data size and processing complexity. Second, key frames extracted from videos are usually of high intra-personal discrepancy due to variations in expressions, poses, and illuminations. In order to address these problems, we propose a novel semantic based subspace model to improve the performance of video based face recognition. The basic idea is to construct an appropriate low-dimensional subspace for each person, upon which a semantic model is built to classify the key frames of the person into specific class. After the semantic classification, the key frames belonging to the same classes, i.e. the same semantics, are used to train the linear classifiers for recognition. Extensive experiments on a large face video database (XM2VTS) clearly show that our approach obtains a significant performance improvement over the traditional approaches.

关键词： Face Training Face recognition Semantics Video sequences Principal component analysis Probes

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A Fast Word Retrieval Technique Based on Kernelized Locality Sensitive Hashing

A Fast Word Retrieval Technique Based on Kernelized Locality...

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International Conference on Document Analysis and Recognition

作者： Tanmoy Mondal Nicolas Ragot Jean-Yves Ramel Umapada Pal Laboratoire dInformatique Université François Rabelais de Tours Tours France Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781479901937

In this paper, we have presented a new and faster word retrieval approach, which is able to deal with heterogeneous document image collections. A certain amount of image features (statistical and Gabor Wavelet) are extracted, which inherently represent word's images. These features are used for generating hash table for fast retrieval of similar image from a very large image dataset. The decomposition and embedding of high-dimensional features and complex distance functions into a low-dimensional Hamming space helps to efficiently search items. However, existing methods do not apply for high-dimensional kernelized data when the underlying features' embedding for the kernel is unknown. The generalization of locality sensitive hashing (LSH) for arbitrary kernel is presented in the paper. The proposed algorithm provides sub-linear time similarity search and works for a wide class of similarity functions.

关键词： Feature extraction Kernel Databases Accuracy Vectors Training pattern recognition

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