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model-based recognition OF CHARACTERS IN TRADEMARK ARTWORK

PATTERN recognition LETTERS 1990年第5期11卷 363-370页

作者： BROSSMAN, C CROSS, GR Department of Computer Science Washington State University Pullman WA 99164-1210 USA

We are building a legal expert system that contains a database of trademarks and reasons about their similarity to assess possible infringement. Our attention in this document is limited to the development of a character recognition component which determines the location and identity of alphabetic characters in the trademarks. The techniques include structural models, graph homeomorphisms, and thinning. Modifications and enhancements to these techniques were required along with changes to existing algorithms.

关键词： image databases model-based recognition multi-media databases Multifont character recognition similarity-based retrieval thinning of digital patterns

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model-based recognition of 3D Objects using Intersecting Lines

Model-based Recognition of 3D Objects using Intersecting Lin...

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IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems

作者： Truong, Hung Q. Lee, Sukhan Jang, Seok-Woo Sungkyunkwan Univ Sch Informat & Commun Engn Suwon South Korea

ISBN: (纸本)9781424421435

Exploiting geometric features, such as points, straight or curved lines and corners, plays an important role in object recognition. In this paper, we present a model-based recognition of 3D objects using intersecting lines. We concentrate on using perpendicular line pairs to test recognition of a parallelepiped model and represent the visible face of the object. From 2D images and point clouds, first, 3D line segments are extracted, and then intersecting lines are selected from them. By estimating the coverage ratio, we find the most accurate matching between detected perpendicular tine pairs and the model database. Finally, the position and the pose of the object are determined. The experimental results show the performance of the proposed algorithm.

关键词： Intersecting line Line matching model-based recognition

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model based object recognition - The role of affine invariants

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ARTIFICIAL INTELLIGENCE IN ENGINEERING 1996年第3期10卷 227-234页

作者： Bose, SK Biswas, KK Gupta, SK UNIV BONN INST PHOTOGRAMMETRIE D-53115 BONN GERMANY Indian Inst Technol DELHI DEPT COMP SCI & ENGN NEW DELHI 110016 INDIA

This paper proposes an efficient method to recognize rigid flat objects from its intensity images which are assumed to be arbitrarily positioned in space. The task of the recognition method is to find instances of known object models in affine images. Affine invariant shape descriptors of rigid flat objects are generated which are invariant to change in the point of view. In the proposed paradigm, the objects are described by sets of local and global features. Since we are also concerned with the recognition of partially occluded objects, the local features are given importance for obtaining descriptions of objects. The global features are useful for finding the exact match and are used for verification. The local features can be points, line segments, curve segments, etc. We restrict ourselves to points, which are referred to as interest points. The point set of the various model objects are matched simultaneously against the point set of the composite overlapping scene using a small number of corresponding points. Seven discrete moments are used here as global features which are also invariant under the affine transformation. Experiments show good performance and together with inherent parallelism of the recognition method makes the method a promising one.

关键词： model-based recognition affine invariants hashing indexing

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Robust hypothesis verification: application to model-based object recognition

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PATTERN recognition 1999年第6期32卷 1069-1081页

作者： Jurie, F Univ Blaise Pascal CNRS UMR 6602 LASMEA F-63177 Aubiere France

The use of hypothesis verification is recurrent in the model based recognition literature. Small sets of Features forming salient groups are paired with model features. Pose can be hypothesised from this small set of correspondences. Verification of the pose consists in measuring how much model features transformed by the computed pose coincide with image features. When data involved in the initial pairing are noisy the pose is inaccurate and verification is a difficult problem. In this paper we propose to use a robust hypothesis verification algorithm to perform object recognition. We explain how to integrate it in two different recognition schemes (2D and 3D recognition). After describing these applications we present numerous experimental results proving the robustness and the efficiency of these algorithms. (C) 1999 Pattern recognition Society. Published by Elsevier Science Ltd. All rights reserved.

关键词： model-based recognition pose verification image features

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model-based multi-source fusion for exploitation, classification and recognition

Model-based multi-source fusion for exploitation, classifica...

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Conference on Signal Processing, Sensor Fusion, and Target recognition XV

作者： Williams, Wayne Keydel, Eric McCarty, Sean SAIC Ann Arbor Res & Dev Ctr 3600 Green Ct Suite 650 Ann Arbor MI 48105 USA

ISBN: (纸本)0819462918

A model-based multi-sensor fusion framework has previously been developed that supports improved target recognition by fusing target signature information obtained from sensor imagery [1], [2]. Image- based signature features, however, are not the only source of information that may be exploited to advantage by a target recognition system. This paper presents a review of the key features of the model-based fusion framework and shows how it can be expanded to support information derived from imaging sensors as well as data from other non-imaging sources. The expanded model-based multi-source framework supports not only the combination of image data, such as Synthetic Aperture Radar (SAR) and electro-optical (EO), but also various types of non-image data that may be derived from those, or other sensor measurements. The paper illustrates the flexibility of the model-based framework by describing the combination of spatial information from an imaging sensor with scattering characteristics derived from polarimetric phase history data. The multi-source fusion is achieved by relating signature features to specific structural elements on the 3-D target geometry. The 3-D model is used as a sensor neutral, view independent, common reference for the combination of multi-source information.

关键词： multi-sensor fusion exploitation model-based recognition

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Solution of the simultaneous pose and correspondence problem using Gaussian error model

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COMPUTER VISION AND IMAGE UNDERSTANDING 1999年第3期73卷 357-373页

作者： Jurie, F Univ Clermont Ferrand LASMEA CNRS UMR 6602 F-63177 Aubiere France

The use of hypothesis verification is recurrent in the model-based recognition literature. Verification consists in measuring how many model features transformed by a pose coincide with some image features. When data involved in the computation of the pose are noisy, the pose is inaccurate and difficult to verify, especially when the objects are partially occluded. To address this problem, the noise in image features is modeled by a Gaussian distribution. A probabilistic framework allows the evaluation of the probability of a matching, knowing that the pose belongs to a rectangular volume of the pose space. It involves quadratic programming, if the transformation is affine. This matching probability is used in an algorithm computing the best pose. It consists in a recursive multiresolution exploration of the pose space, discarding outliers in the match data while the search is progressing. Numerous experimental results are described. They consist of 2D and 3D recognition experiments using the proposed algorithm. (C) 1999 Academic Press.

关键词： model-based recognition pose verification

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A PARALLEL ALGORITHM FOR DETERMINING TWO-DIMENSIONAL OBJECT POSITIONS USING INCOMPLETE INFORMATION ABOUT THEIR BOUNDARIES

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PATTERN recognition 1989年第1期22卷 21-28页

作者： AMINI, AA WEYMOUTH, TE ANDERSON, DJ UNIV MICHIGAN DEPT ELECT ENGN & COMP SCIANN ARBORMI 48109

Extraction of two-dimensional object locations using current techniques is a computationally intensive process. In this paper a parallel algorithm is presented that can specify the location of objects from edge streaks produced by an edge operator. Best-first searches are carried out in a number of non-interacting and localized edge streak spaces. The outcome of each search is a hypothesis. Each edge streak votes for a single hypothesis; it may also take part in the formation of other hypotheses. A poll of the votes determined the stronger hypotheses. The algorithm can be used as a ❉ end to a visual pattern recognition system where features are extracted from the hypothesized object boundary or from the area localized by the hypothesized boundary. Experimental results from a biomedical domain are presented.

关键词： Computer vision Two-dimensional object recognition Search Parallel algorithm Automated histopathology Edge-linking model-based recognition Feature-learning

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Recognizing handwritten digits using hierarchical products of experts

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2002年第2期24卷 189-197页

作者： Mayraz, G Hinton, GE UCL Gatsby Computat Neurosci Unit London WCIN 3AR England

The product of experts learning procedure [1] can discover a set of stochastic binary features that constitute a nonlinear generative model of handwritten images of digits. The quality of generative models learned in this way can be assessed by learning a separate model for each class of digit and then comparing the unnormalized probabilities of test images under the 10 different class-specific models. To improve discriminative performances a hierarchy of separate models can be learned for each digit class. Each model in the hierarchy learns a layer of binary feature detectors that model the probability distribution of vectors of activity of feature detectors in the layer below. The models in the hierarchy are trained sequentially and each model uses a layer of binary feature detectors to learn a generative model of the patterns of feature activities in the preceding layer. After training, each layer of feature dectectors produces a separate, unnormalized log probability score. With three layers of feature detectors for each of the 10 digit classes, a test image produces 30 scores which can be used as inputs to a supervised, logistic classification network that is trained on separate data. On the MNIST database, our system is comparable with current state-of-the-art discriminative methods, demonstrating that the product of experts learning procedure can produce effective hierarchies of generative models of high-dimensional data.

关键词： neural networks products of experts handwriting recognition feature extraction shape recognition Boltzmann machines model-based recognition generative models

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Recognizing target variants and articulations in synthetic aperture radar images

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OPTICAL ENGINEERING 2000年第3期39卷 712-723页

作者： Bhanu, B Jones, G Univ Calif Riverside Ctr Res Intelligent Syst Riverside CA 92521 USA

The focus of this paper is recognizing articulated vehicles acid actual vehicle configuration variants in real synthetic aperture radar (SAR) images. Using SAR scattering-center locations and magnitudes as features, the invariance of these features is shown with articulation (e.g., rotation of a tank turret), with configuration variants, and with a small change in depression angle. This scatterer-location and magnitude quasiinvariance is used as a basis for development of a SAR recognition system that successfully identifies real articulated and nonstandard-configuration vehicles based on nonarticulated, standard recognition models. Identification performance results are presented as vote-space scatterplots and receiver operating characteristic curves for configuration variants, for articulated objects, and for a small change in depression angle with the MSTAR public data. (C) 2000 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(00)03403-6].

关键词： automatic target recognition model-based recognition quasiinvariance of features geometric and magnitude constraints multiattribute hashing

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Recognizing 3-D objects by using a hopfield-style optimization algorithm for matching patch-based descriptions

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PATTERN recognition 1998年第6期31卷 727-741页

作者： Zha, HB Nanamegi, H Nagata, T Kyushu Univ Dept Intelligent Syst Higashi Ku Fukuoka 812 Japan Asahi Glass Co Ltd Chiba Japan Inst Syst & Informat Technol Fukuoka Japan

A new method is proposed for recognizing 3-D objects by using a Hopfield-style optimization algorithm based on matching patch-based image and model descriptions. To obtain the image descriptions, range images are employed to extract reliable high-level patch Features. In the optimization process, the objective function is a Liapunov function which encodes a set of geometric constraints on the descriptions. The optimization is implemented in a Hopfield network with its interconnections encoding the imposed unary, binary and bounding edge constraints. At first, the paper makes an explanation on a new pre-processing method for deriving the required image description. It then presents the structure of the used Hopfield network that is able to recognize multiple model objects all at the same time. Experimental results based on synthetic or real range images are also reported. (C) 1998 Pattern recognition Society. Published by Elsevier Science Ltd. All rights reserved.

关键词： 3-D object recognition model-based recognition optimal matching algorithms constraint satisfaction Hopfield networks patch-based descriptions

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