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Registration of camera captured documents under non-rigid deformation

Registration of camera captured documents under non-rigid de...

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作者： Edupuganti, Venkata Gopal Agarwal, Vinayak A Kompalli, Suryaprakash Dept. of Computer Science New Jersey Inst. of Technology Newark NJ United States Stanford University Stanford CA United States Hewlett-Packard Labs Bangalore India

ISBN: (纸本)9781457703942

Document registration is a problem where the image of a template document whose layout is known is registered with a test document image. Given the registration parameters, layout of the template image is superimposed on the test document. Registration algorithms have been popular in applications, such as forms processing where the superimposed layout is used to extract relevant fields. Prior art has been designed to work with scanned documents under affine transformation. We find that the proliferation of camera captured images makes it necessary to address camera noise such as non-uniform lighting, clutter, and highly variable scale/resolution. The absence of a scan bed also leads to challenging non-rigid deformations being seen in paper images. Prior approaches in point pattern based registration like RANdom SAmple Consensus (RANSAC) [4], and Thin Plate Spline-Robust Point Matching (TPS-RPM) [5, 6] form the basis of our work. We propose enhancements to these methods to enable registration of cell phone and camera captured documents under non-rigid transformations. We embed three novel aspects into the framework: (i) histogram based uniformly transformed correspondence estimation, (ii) clustering of points located near the regions of interest (ROI) to select only close by regions for matching, (iii) validation of the registration in RANSAC and TPS-RPM algorithms for non-rigid registration. We consider Scale Invariant Feature Transform (SIFT) [8] and Speeded-Up Robust Features (SURF) [1] as our features. Results are reported as comparing prior art with our method on a dataset that will be made publicly available. © 2011 ieee.

关键词： Cameras

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Bootstrapping SVM active learning by incorporating unlabelled images for image retrieval

Bootstrapping SVM active learning by incorporating unlabelle...

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conference on computer vision and pattern recognition (cvpr)

作者： Lei Wang Kap Luk Chan Zhihua Zhang School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Computer Science Hong Kong University of Science and Technology Kowloon Hong Kong China

ISBN: (纸本)0769519008

The performance of image retrieval with SVM active learning is known to be poor when started with few labeled images only. In this paper, the problem is solved by incorporating the unlabelled images into the bootstrapping of the learning process. In this work, the initial SVM classifier is trained with the few labeled images and the unlabelled images randomly selected from the image database. Both theoretical analysis and experimental results show that by incorporating unlabelled images in the bootstrapping, the efficiency of SVM active learning can be improved, and thus improves the overall retrieval performance.

关键词： Support vector machines Image retrieval computer society computer vision pattern recognition

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Segmentation of textured images

Segmentation of textured images

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conference on computer vision and pattern recognition (cvpr)

作者： A. Perry D.G. Lowe Courant Institute of Mathematical Sciences Department of Computer Science New York University New York NY USA Department of Computer Science University of British Columbia Vancouver BC Canada

The authors present a method for texture segmentation that does not assume any prior knowledge about either the type of textures or the number of textured regions present in the image. Local orientation and spatial frequencies are used as the key parameters for classifying texture. The information is obtained by creating a local multifrequency multiorientation channel decomposition of the image, with the width of each frequency band constant on a logarithmic scale. This decomposition is implemented by applying a set of Gabor-like functions that were modified to have a decreased frequency selectivity when the filter's center frequency increases. The set of filter outputs is then used to create robust texture descriptors. The segmentation algorithm uses the similarity of the descriptors to determine the existence of texture regions and to outline their border rather than concentrating on segregating the textures. The method has been applied to image containing natural textures, resulting in a good segmentation of the texture regions.< >

关键词： Image segmentation Frequency Gabor filters Psychology computer science Wavelet transforms Humans Fourier transforms Brain modeling Robustness

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On contour texture

On contour texture

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conference on computer vision and pattern recognition (cvpr)

作者： B.J. Subirana-Vilanova Artificial Intelligence Laboratory MIT Cambridge MA USA

It is suggested that non-smooth or complex boundaries are processed by the visual system so as to obtain a simple abstract description, called contour texture, which contains much less information than that provided by the location of all points of the contour. A discussion is presented of what the nature of the contour texture of a curve is and its relation with two-dimensional texture, which the author contends should be thought of as a separate concept. Several applications are suggested, and an implemented filter-based scheme is presented.< >

关键词： Shape Artificial intelligence Laboratories Inspection Visual system Clouds Hair Image recognition Image segmentation Psychology

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Uncertainty update and dynamic search window for model-based object recognition

Uncertainty update and dynamic search window for model-based...

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conference on computer vision and pattern recognition (cvpr)

作者： C.-H. Chen P.G. Mulgaonkar SRI International Inc. Menlo Park CA USA

The notion of uncertainty manipulation in robot navigation is applied to the task of model-based object recognition. Based on the fact that every detected object feature in an image must be associated with some uncertainty, the authors show how to iteratively refine the estimate of pose uncertainty of a hypothesized object when a new feature is matched, and how to dynamically determine a proper search window to look for the next model feature. The advantage of this method over the conventional static search window is demonstrated in an actual object recognition task.< >

关键词： Uncertainty Object recognition computer vision Object detection Feature extraction Mobile robots Robot kinematics Robot vision systems Navigation Shape

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Invariant histograms and deformable template matching for SAR target recognition

Invariant histograms and deformable template matching for SA...

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conference on computer vision and pattern recognition (cvpr)

作者： K. Ikeuchi T. Shakunaga M.D. Wheeler T. Yamazaki School of Computer Science Carnegie Mellon University Pittsburgh PA USA

Recognizing a target in synthetic-aperture radar (SAR) images is an important, yet challenging, application of the model-based vision technique. This paper describes a model-based SAR recognition system based on invariant histograms and deformable template matching techniques. An invariant histogram is a histogram of invariant values defined by geometric features such as points and lines in SAR images. Although a few invariants are sufficient to recognize a target, we use a histogram of all invariant values given by all possible target feature pairs. This redundant histogram enables robust recognition under severe occlusions typical in SAR recognition scenarios. Multi-step deformable template matching examines the existence of an object by superimposing templates over potential energy field generated from images or primitive features. It determines the template configuration which has the minimum deformation and the best alignment of the template with features. The deformability of the template absorbs the instability of SAR features. We have implemented the system and evaluated the system performance using hybrid SAR images, generated from synthesized model signatures and real SAR background signatures.

关键词： Histograms Target recognition Image generation Image recognition Radar applications Radar imaging Synthetic aperture radar Deformable models Robustness Potential energy

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Determining correspondences and rigid motion of 3-D point sets with missing data

Determining correspondences and rigid motion of 3-D point se...

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conference on computer vision and pattern recognition (cvpr)

作者： Xiaoguang Wang Yong-Qing Cheng R.T. Collins A.R. Hanson Department of Computer Science University of Massachusetts Amherst MA USA

This paper addresses the general 3-D rigid motion problem, where the point correspondences and the motion parameters between two sets of 3-D points are to be recovered. The existence of missing points in the two sets is the most difficult problem. We first show a mathematical symmetry in the solutions of rotation parameters and point correspondences. A closed-form solution based on the correlation matrix eigenstructure decomposition is proposed for correspondence recovery with no missing points. Using a heuristic measure of point pair affinity derived from the eigenstructure, a weighted bipartite matching algorithm is developed to determine the correspondences in general cases where missing points occur. The use of the affinity heuristic also leads to a fast outlier removal algorithm, which can be run iteratively to refine the correspondence recovery. Simulation results and experiments on real images are shown in both ideal and general cases.

关键词： Closed-form solution Iterative algorithms computer science Matrix decomposition Contracts Solids Graph theory Motion estimation

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Higher order whitening of natural images

Higher order whitening of natural images

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conference on computer vision and pattern recognition (cvpr)

作者： J. Gluckman Department of Computer Science Polytechnic University Brooklyn NY USA

Natural images are approximately scale invariant resulting in long range statistical regularities that typically obey a power law. For example, images have considerable regularity in their second order spatial correlations as measured by the power spectrum. Processing images to remove these expected correlations is known as whitening an image. Because the expected value of the power spectrum has a regular form (a power law) linear processing such as convolution can be used to whiten an image. After whitening an image, higher order regularities that cannot be removed with linear processing still exist in the form of correlations in the magnitude. In this paper it is shown that these correlations also obey a power law and a non-linear method is used to remove them, a process referred to as higher order whitening. The method is invertible demonstrating that while redundancy is removed no information is lost. Experiments are given showing that after higher order whitening the coefficients can be severely quantized yet a good reconstruction is possible despite the nonlinearities.

关键词： Higher order statistics Power measurement computer science Convolution Image reconstruction Layout Autocorrelation Information theory Impedance matching Physics

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The HFB Face Database for Heterogeneous Face Biometrics Research

The HFB Face Database for Heterogeneous Face Biometrics Rese...

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ieee computer society conference on computer vision and pattern recognition Workshops

作者： Stan Z. Li Zhen Lei Meng Ao Center for Biometrics and Security Research Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781424439942

A face database, composed of visual (VIS), near infrared (NIR) and three-dimensional (3D) face images, is collected. Called the HFB Face Database, it is released now to promote research and development of Heterogeneous Face Biometrics (HFB). This release of Version 1 contains a total of 992 images from 100 subjects;there are 4 VIS, 4 NIR, and 1 or 2 3D face images per subject. In this paper, we describe the apparatuses, environments and procedure of the data collection and present baseline performances of the standard PCA and LDA methods on the database.

关键词： Biometrics Image databases Visual databases Linear discriminant analysis Infrared imaging Principal component analysis Face recognition Image matching Research and development Infrared spectra

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Probabilistic modeling based vessel enhancement in thoracic CT scans

Probabilistic modeling based vessel enhancement in thoracic ...

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conference on computer vision and pattern recognition (cvpr)

作者： G. Agam C. Wu Department of Computer Science Illinois Institute of Technology Chicago IL USA

Vessel enhancement in volumetric data is a necessary prerequisite in various medical imaging applications with particular importance for automated nodule detection. Ideally, vessel enhancement filters should enhance vessels and vessel junctions while suppressing nodules and other non-vessel elements. A distinction between vessels and nodules is normally obtained through eigenvalue analysis of the curvature tensor which is a second order differential quantity and so is sensitive to noise. Furthermore, by relying on principal curvatures alone, existing vessel enhancement filters are incapable of distinguishing between nodules and vessel junctions. In this paper we propose probabilistic vessel models from which novel vessel enhancement filters capable of enhancing junctions while suppressing nodules are derived. The proposed filters are based on eigenvalue analysis of the structure tensor which is a first order differential quantity and so are less sensitive to noise. The proposed filters are evaluated and compared to known techniques based on actual clinical data.

关键词： Computed tomography Nonlinear filters Biomedical imaging Eigenvalues and eigenfunctions Tensile stress Blood vessels Lungs Image segmentation computer science Application software

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