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Texture‐based parametric active contour for target detection and tracking
Author(s) -
Vard Ali Reza,
Moallem Payman,
Nilchi Ahmad Reza Naghsh
Publication year - 2009
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.20194
Subject(s) - active contour model , artificial intelligence , computer vision , computer science , parametric statistics , tracking (education) , texture (cosmology) , segmentation , bidirectional texture function , pattern recognition (psychology) , object (grammar) , image texture , image segmentation , image (mathematics) , mathematics , psychology , pedagogy , statistics
Abstract In recent years, active contour models (ACM) have been considered as powerful tools for image segmentation and object tracking in computer vision and image processing applications. This article presents a new tracking method based on parametric active contour models. In the proposed method, a new pressure energy called “texture pressure energy” is added to the energy function of the parametric active contour model to detect and track a texture target object in a texture background. In this scheme, the texture features of the contour are calculated by a moment‐based method. Then, by comparing these features with texture features of the target object, the contour curve is expanded or contracted to be adapted to the object boundaries. Experimental results show that the proposed method is more efficient and accurate in the tracking of objects compare to the traditional ones, when both object and background are textures in nature. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 187–198, 2009