Gabor texture in active appearance models

Publisher:
Elsevier Science Bv
Publication Type:
Journal Article
Citation:
Neurocomputing, 2009, 72 (13-15), pp. 3174 - 3181
Issue Date:
2009-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2011000222OK.pdf624.81 kB
Adobe PDF
In computer vision applications, Active Appearance Models (AAMs) is usually used to model the shape and the gray-level appearance of an object of interest using statistical methods, such as PCA. However, intensity values used in standard AAMs cannot provide enough information for image alignment. In this paper, we firstly propose to utilize Gabor filters to represent the image texture. The benefit of Gabor-based representation is that it can express local structures of an image. As a result, this representation can lead to more accurate matching when condition changes. Given the problem of the excessive storage and computational complexity of the Gabor. three different Gabor-based image representations are used in AAMs: (1) GaborD is the sum of Gabor filter responses over directions, (2) GaborS is the sum of Gabor filter responses over scales, and (3) GaborSD is the sum of Gabor filter responses over scales and directions. Through a large number of experiments, we show that the proposed Gabor representations lead to more accurate and robust matching between model and images.
Please use this identifier to cite or link to this item: