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Information Retrieval Based on OCR Errors in Scanned Documents

Information Retrieval Based on OCR Errors in Scanned Documen...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Y. Fataicha M. Cheriet J. Y. Nie C. Y. Suen CENPARMI Concordia University Montreal QUE Canada LIVIA Laboratory École de Technologie Supérieure QUE Canada RALI laboratory Université de Montréal QUE Canada

An important proportion of documents are document images, i.e. scanned documents. For their retrieval, it is important to recognize their contents. Current technologies for optical character recognition (OCR) and document analysis do not handle such documents adequately because of the recognition errors. In this paper, we describe an approach that integrates the detection of errors in scanned texts without relying on a lexicon, and this detection is integrated in the research process. The proposed algorithm consists of two basic steps. In the first step, we apply editing operations on OCR words that generate a collection of error-grams and correction rules. The second step uses query terms, error-grams, and correction rules to create searchable keywords, identify appropriate matching terms, and determine the degree of relevance of retrieved document images. Algorithms has been tested on 979 document images provided by Media-team databases from Washington University, and the experimental results obtained show the effectiveness of our method and indicate improvement in comparison with the standard methods such as exact or partial matching, N-gram overlaps, and Q-gram distance.

关键词： Optical character recognition software Distance measurement Text recognition Image recognition pattern recognition Character recognition Databases

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Statistical Search for Hierarchical Linear Optimal Representations of Images

Statistical Search for Hierarchical Linear Optimal Represent...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Qiang Zhang Xiuwen Liu Anuj Srivastava Department of Computer Science Florida State University Tallahassee FL USA Department of Statistics Florida State University Tallahassee FL USA

Although linear representations of images are widely used in appearance-based recognition of objects, the frequently used ideas, such as PCA, ICA, and FDA, are often found to be suboptimal. A stochastic search algorithm has been proposed recently [4] for finding representations that are optimal for specific tasks and datasets. However, this search algorithm is computationally efficient only when the image size is relatively small. Here we propose a hierarchical learning algorithm to speed up the search. The proposed approach decomposes the original optimization problem into several stages according to a hierarchical organization. In particular, the following idea is applied recursively: (i) reduce the image dimension using a shrinkage matrix, (ii) optimize the recognition performance in the reduced space, and (iii)expand the optimal subspace to the bigger space in a pre-specified way. We show that the optimal performance is maintained in the last step. By applying this decomposition procedure recursively, a hierarchy of layers is formed. This speeds up the original algorithm significantly since the search is performed mainly in reduced spaces. The effectiveness of hierarchical learning is illustrated on a popular database, where the computation time is reduced by a large factor compared to the original algorithm.

关键词： Training Complexity theory Image recognition Distance measurement Pixel Algorithm design and analysis Optimization

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Direct Least Square Fitting of Paracatadioptric Line Images

Direct Least Square Fitting of Paracatadioptric Line Images

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Joao P. Barreto Helder Araujo Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal

Paracatadioptric sensors combine a parabolic shaped mirror and a camera inducing an orthographic projection. Such a configuration provides a wide field of view while keeping a single effective viewpoint. In general the paracatadioptric image of a line is a conic curve. The estimation of line images is an important subject for applications such as reconstruction and visual control of motion. However the estimation of the conic curves where lines are mapped is hard to accomplish. In general only a small arc of the conic is visible in the image and conventional conic fitting techniques are unable to correctly estimate the curve. This paper shows that line images can be accurately estimated by constraining the search space. A conic curve is the paracatadioptric image of a line if, and only if, the image of the circular points lie on the curve and two certain points are conjugate with respect to the conic. Considering the space of all conic curves, the line images lie in a linear subspace which depends on the system calibration. The paracatadioptric projection of a line can estimated by fitting a conic in the subspace to the data points. The proposed approach is computationally efficient since the fitting problem can be solved by an eigensystem

关键词： Fitting Equations Cameras Noise Least squares approximation Mirrors Sensors

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Archaeological Fragment Reconstruction Using Curve-Matching

Archaeological Fragment Reconstruction Using Curve-Matching

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jonah C. McBride Benjamin B. Kimia Brown University Providence RI USA

We present a novel approach to the problem of puzzle solving as it relates to archaeological fragment reconstruction. We begin with a set of broken fragments. In the first stage, we compare every pair of fragments and use partial curve matching to find similar portions of their respective boundaries. Partial curve matching is typically a very difficult problem because the specification of the partial curves are highly unconstrained and curve matching is computationally expensive. To address the first problem, we only consider matches which begin at fragment corners and then use curve-matching with normalized energy to determine how far the match extends. We also reduce computational cost by employing a multi-scale approach. This allows us to quickly generate many possible matches at a coarse scale and only keep the best ones to be matched again at a finer scale. In the second stage, we take a rank-ordered list of pairwise matches to search for a globally optimal arrangement. The search is based on a best-first strategy which adds fragments with the highest pairwise affinity first, but then evaluates their confidence as part of the global solution by rewarding the formation of triple junctions which are dominant in archaeological puzzles. To prevent failure due to the inclusion of spurious matches, we employ a standard beam-search to simultaneously expand on multiple solutions. Results on several cases are demonstrated.

关键词： Junctions Length measurement computer vision Materials Complexity theory pattern recognition conferences

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4th International workshop on Energy Minimization Methods in computer vision and pattern recognition, EMMCVPR 2003

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4th International workshop on Energy Minimization Methods in computer vision and pattern recognition, EMMCVPR 2003

ISBN: (纸本)3540404988

The proceedings contain 39 papers. The special focus in this conference is on Unsupervised Learning, Matching, Probabilistic Modelling, Segmentation, Grouping, Shape Modelling and Reconstruction. The topics include: Stochastic search for optimal linear representations of images on spaces with orthogonality constraints;curve matching using the fast marching method;EM algorithm for clustering an ensemble of graphs with comb matching;information force clustering using directed trees;active sampling strategies for multihypothesis testing;likelihood based hierarchical clustering and network topology identification;learning mixtures of tree-unions by minimizing description length;hierarchical annealing for random image synthesis;semi-supervised image segmentation by parametric distributional clustering;path variation and image segmentation;a fast snake segmentation method applied to histopathological sections;a compositionality architecture for perceptual feature grouping;using prior shape and points in medical image segmentation;separating a texture from an arbitrary background using pair wise grey level cooccurrences;surface recovery from 3D point data using a combined parametric and geometric flow approach;curvature vector flow to assure convergent deformable models for shape modelling;a MAP estimation algorithm using IIR recursive filters;estimation of rank deficient matrices from partial observations;contextual and non-combinatorial approach to feature extraction;generalized multi-camera scene reconstruction using graph cuts and graph matching using spectral seriation.

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Application of Structured Illumination in Nano-Scale vision

Application of Structured Illumination in Nano-Scale Vision

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： J. Ryu B. K. P. Horn M. S. Mermelstein S. Hong D. M. Freeman Division of Health Science & Technology Harvard and MIT Cambridge MA USA Department of Electrical Engineering and Computer Science MIT Cambridge MA USA Lightwave Instruments L.L.C. Watertown MA USA Department of Electrical Engineering and Computer Science MIT Cambridge MA

We describe how structured illumination patterns can be used to increase the resolution of an imaging system for optical microscopy. A target is illuminated by a sequence of finely textured light patterns formed by the interference of multiple coherent beams. The sequence of brightness values reported from a single pixel of a CCD imager encodes the target contrast pattern with sub-pixel resolution. Fourier domain components at spatial frequencies contained in the probing illumination patterns can be recovered from the pixel brightness sequence by solving a set of over-determined linear equations. We show that uniform angular spacing of the beams generating the illumination patterns leads to less than ideal sampling of the transform space and we propose alternative geometric arrangements. We describe an image reconstruction algorithm based on the Voronoi diagram that applies when the transform domain is not sampled uniformly. Finally, the contrast patterns within individual pixels can be spliced together to forman image encompassing multiple pixels.

关键词： Lighting Laser beams Image resolution Optical imaging Pixel Lenses Brightness

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A Study on Bayes Feature Fusion for Image Classification

A Study on Bayes Feature Fusion for Image Classification

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： X. Shi R. Manduchi Department of Computer Engineering University of California Santa Cruz USA

We consider here the problem of image classification when more than one visual feature are available. In these cases, Bayes fusion offers an attractive solution by combining the results of different classifiers (one classifier per feature). This is a general form of the so-called "naive Bayes" approach. Analyzing the performance of Bayes fusion with respect to a Bayesian classifier over the joint feature distribution, however, is tricky. On the one hand, it is well-known that the latter has lower bias than the former, unless the features are conditionally independent, in which case the two coincide. On the other hand, as noted by Friedman, the low variance associated with naive Bayes estimation may dramatically mitigate the effect of its bias. In this paper, we attempt to assess the tradeoff between these two factors by means of experimental tests on two image data sets using color and texture features. Our results suggest that (1) the difference between the correct classification rates using Bayes fusion and using the joint feature distribution is a function of the conditional dependence of the features (measured in terms of mutual information), however: (2) for small training data size, Bayes fusion performs almost as well as the classifier on the joint distribution.

关键词： Image color analysis Bayesian methods Error analysis Mutual information Training data Correlation Joints

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Statistical Error Propagation in 3D Modeling From Monocular Video

Statistical Error Propagation in 3D Modeling From Monocular ...

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Amit Roy Chowdhury Rama Chellappa Center for Automation Research University of Maryland College Park MD USA Center for Automation Research University of Maryland College Park MD

A significant portion of recent research in computer vision has focused on issues related to sensitivity and robustness of existing techniques. In this paper, we study the classical structure from motion problem and analyze how the statistics representing the quality of the input video propagates through the reconstruction algorithm and affects the quality of the output reconstruction. Specifically, we show that it is possible to derive analytical expressions of the first and second order statistics (bias and error covariance) of the solution as a function of the statistics of the input. We concentrate on the case of reconstruction from a monocular video, where the small baseline makes any algorithm very susceptible to noise in the motion estimates from the video sequence. We derive an expression relating the error covariance of the reconstruction to the error covariance of the feature tracks in the input video. This is done using the implicit function theorem of real analysis and does not require strong statistical assumptions. Next, we prove that the 3D reconstruction is statistically biased, derive an expression for it and show that it is numerically significant. Combining these two results, we also establish a new bound on the minimum error in the depth reconstruction. We present the numerical significance of these analytical results on real video data.

关键词： Three dimensional displays Image reconstruction computer vision Cameras Uncertainty Equations Adaptive optics

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computer vision Algorithms for Quantifying the Growth and Behavior of Neurons Cultured on Nanofabricated Surfaces

Computer Vision Algorithms for Quantifying the Growth and Be...

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Muhammad-Amri Abdul Karim Khalid Al-Kofahi Badrinath Roysam Natalie Dowell-Mesfin Rifat J. Hussain William Shain James N. Turner Electrical Computer & Systems Engineering Dept. Rensselaer Polytechnic Institute Troy NY Electrical Computer & Systems Engineering Dept. Rensselaer Polytechnic Institute Troy NY USA The Wadsworth Center Department of Health Albany NY The Wadsworth Center Department of Health Albany NY USA

Neuron growth on nanofabricated silicon surfaces, once understood, is fundamental to engineer circuits involving neurons and electronic components, leading to applications such as next-generation brain implants, and high-throughput neurologic drug screening assay systems. Precise quantification of neuronal growth and behavior from image samples requires computer vision algorithms for automatic tracing of neurons, and flexible algorithms for reliable registration and analysis of multi-fluorophore imagery. The tracing algorithms must be robust to high levels of clutter and common imaging artifacts, process discontinuities, and quantum noise, especially when live neurons are imaged. Finally, the large numbers of images that must be processed for a hypothesis test or assay call for computationally efficient algorithms. A fully automated neuron tracing algorithm based on the use of robust detection and estimation principles is described here that meets the above needs. It takes 2 seconds to trace and extract morphological statistics from a typical 1280 × 1024 image on a Pentium III, 1 GHz personal computer. It was validated against manually generated traces and the errors were in the range of 1-5% per image.

关键词： Neurons Surface topography Algorithm design and analysis Robustness computer vision Surface morphology Nerve fibers

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A Web-based System for Retrieving Document Images from Digital Library

A Web-based System for Retrieving Document Images from Digit...

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Li Zhang Yue Lu Chew Lim Tan Department of Computer Science School of Computing National University of Singapore Singapore

A web-based system for retrieving imaged documents from a digital library is described in this paper. First, some image preprocessing is performed off-line on the underlying imaged document to extract its word objects. Then, each word object is represented by a string known as its feature code, based on which a feature code file of the corresponding document is constructed. On the web interface side, the system allows the user to input a set of query words and indicate either to perform "AND" or "OR" operation on them. Once receiving user's request, the system will process each query word and combine the results based on the "AND" or "OR" operation the user has chosen. As for each query word, it is first looked up in an index table that stores words being queried before. If matches are found, results will be retrieved from the index table directly and stored temporarily for subsequent merging. This speeds up searching and makes the system an incremental intelligence system. Otherwise, the system will convert the query word to a feature code string and employ a partial word matching approach to perform search on the pre-generated feature code files. Preliminary experimental results with the imaged documents of students' theses provided by our digital library show that the proposed system is efficient and promising for document image retrieval, and thus has potential applications to digital libraries.

关键词： Feature extraction Indexes Servers Libraries Shape Probability density function Image retrieval

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