Displacement field estimation and image segmentation using block matching enhanced by a neural network

Displacement field estimation and image segmentation using block matching enhanced by a neural... Displacement field estimation and image segmentation using block matching enhanced by a neural network YONG-SHENG CHEN and CHIOU-SHANN FUH* Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan Received 10 August 1995; accepted 30 October 1995 Abstract-The block-matching method plays an important role in displacement field estimation due to its simplicity, achievement of long-range motion, and robustness to noise. In this paper, a single-layer feedback neural network model is proposed that enhances block matching, estimates the displacement field, and simultaneously performs image segmentation from consecutive images. In this paper, image segmentation is defined as partitioning each image into a set of moving objects and the background. For any two consecutive images, a neural network is created that learns the connection relationship of the pixels in an object from the displacement field and stores the relationship in the network. A modified block matching is used to compute a more accurate displacement field by utilizing the segmentation information embedded in the neural network. The displacement vector at the edge of an object or occluding boundary is hard to estimate, but the proposed model performs satisfactorily because it learns and uses the connection information. Furthermore, a flood-fill algorithm http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Spatial Vision (continued as Seeing & Perceiving from 2010) Brill

Displacement field estimation and image segmentation using block matching enhanced by a neural network

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Publisher
Brill
Copyright
© 1996 Koninklijke Brill NV, Leiden, The Netherlands
ISSN
0169-1015
eISSN
1568-5683
D.O.I.
10.1163/156856896X00042
Publisher site
See Article on Publisher Site

Abstract

Displacement field estimation and image segmentation using block matching enhanced by a neural network YONG-SHENG CHEN and CHIOU-SHANN FUH* Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan Received 10 August 1995; accepted 30 October 1995 Abstract-The block-matching method plays an important role in displacement field estimation due to its simplicity, achievement of long-range motion, and robustness to noise. In this paper, a single-layer feedback neural network model is proposed that enhances block matching, estimates the displacement field, and simultaneously performs image segmentation from consecutive images. In this paper, image segmentation is defined as partitioning each image into a set of moving objects and the background. For any two consecutive images, a neural network is created that learns the connection relationship of the pixels in an object from the displacement field and stores the relationship in the network. A modified block matching is used to compute a more accurate displacement field by utilizing the segmentation information embedded in the neural network. The displacement vector at the edge of an object or occluding boundary is hard to estimate, but the proposed model performs satisfactorily because it learns and uses the connection information. Furthermore, a flood-fill algorithm

Journal

Spatial Vision (continued as Seeing & Perceiving from 2010)Brill

Published: Jan 1, 1996

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