Fast algorithm for region snake-based segmentation adapted to physical noise models and application to object tracking

Christophe Chesnaud; Philippe Refregier

doi:10.1117/12.351302

25 June 1999 Fast algorithm for region snake-based segmentation adapted to physical noise models and application to object tracking

Christophe Chesnaud, Philippe Refregier

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Proceedings Volume 3816, Mathematical Modeling, Bayesian Estimation, and Inverse Problems; (1999) https://doi.org/10.1117/12.351302
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

Abstract

Algorithms for object segmentation are crucial in many image processing applications. During past years, active contour models have been widely used for finding the contours of objects. This segmentation strategy is classically edge based in the sense that the snake is driven to fit the maximum of an edge map of the scene. We have recently proposed a region-based snake approach, that can be implemented using a fast algorithm , to segment an object in an image. The algorithms, optimal in the Maximum Likelihood sense, are based on the calculus of the statistics of the inner and the outer regions and can thus be adapted to different kinds of random fields which can describe the input image. In this paper out aim is to study this approach for tracking application in optronic images. We first show the relevance of using a priori information on the statistical laws of the input image in the case of Gaussian statistics which are well adapted to describe optronic images when a whitening preprocessing is used. We will then characterize the performance of the fast algorithm implementation of the used approach and we will apply it to tracking applications. The efficiency of the proposed method will be shown on real image sequences.

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Christophe Chesnaud and Philippe Refregier "Fast algorithm for region snake-based segmentation adapted to physical noise models and application to object tracking", Proc. SPIE 3816, Mathematical Modeling, Bayesian Estimation, and Inverse Problems, (25 June 1999); https://doi.org/10.1117/12.351302

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KEYWORDS

Image segmentation

Image processing algorithms and systems

Detection and tracking algorithms

Calculus

Optoelectronics

Sun

Image processing

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