2D object recognition on a reconfigurable mesh

doi:10.1016/S0031-3203(97)00032-0

Pattern Recognition

Volume 31, Issue 1, January 1998, Pages 83-88

https://doi.org/10.1016/S0031-3203(97)00032-0 Get rights and content

Abstract

This paper presents an approach to recognizing two-dimensional multiscale objects on a reconfigurable mesh architecture with horizontal and vertical broadcasting. The object models are described in terms of a convex/concave multiscale boundary decomposition that is represented by a tree structure. The problem of matching an observed object against a model is formulated as a tree matching problem. A parallel dynamic programming solution to this problem is presented that requires O(max(n,m)) time on n × m reconfigurable mesh, where n and m are the sizes of the two trees.

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Citation Excerpt :
These representations are acquiring increasing importance in recent years [30–32]. Systems have been developed to represent contours, divided in segments that are progressively joined when the scale parameter is increased [33,34]. Wavelets have also been used to generate a tree representation, as in [35,36], where the wavelet tree is proposed as a way to identify textures in an image by recursively extracting feature values, and segmentation is then carried out using a multichannel classification algorithm.
A new general image segmentation system is presented, based on the calculation of a tree representation of the original image in which image regions are assigned to tree nodes, followed by a correspondence process with a model tree, which embeds the a priori knowledge about the images. For this correspondence, an original algorithm is proposed, which performs the minimization of an error function that quantifies the difference between the input image tree and the model tree. We also present a new algorithm for automatically calculating the model tree from a set of manually segmented images. Results on synthetic and MR brain images are presented.
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This paper presents a bibliography of over 2250 references related to computer vision and image analysis, arranged by subject matter. The topics covered include computational techniques; feature detection and segmentation; image and scene analysis; two-dimensional shape; pattern; color and texture; matching and stereo; three-dimensional recovery and analysis; three-dimensional shape; and motion. A few references are also given on related topics, including geometry and graphics, compression and processing, sensors and optics, visual perception, neural networks, artificial intelligence and pattern recognition, as well as on applications.
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^☆: A preliminary version of this paper was presented at the Tenth Int. Parallel Processing Symp. (1996).

²: Partially supported by the National Research Council of Italy and by the ESPRIT III Basic Research Programme of the EC under contract No. 9072 (Project GEPPCOM).

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2D object recognition on a reconfigurable mesh☆

Abstract

J. Parallel Distributed Comput.

Recognizing 2D objects by a multiresolution approach

Model-based object recognition in robot vision

ACM Comp. Surveys

An algorithm for matching distorted waveforms using a scale-based description

Learning visual models from shape contours using multi-scale convex/concave structure matching

IEEE Trans. Pattern Analysis Mach. Intell.

A survey of parallel algorithms for structural pattern matching

Constant-time parallel algorithms for image labeling on a reconfigurable network of processors

IEEE Trans. Pattern Analysis Mach. Intell.