Title :
A common set of perceptual observables for grouping, figure-ground discrimination, and texture classification
Author :
Hoogs, Anthony ; Collins, Roderic ; Kaucic, Robert ; Mundy, Joseph
Author_Institution :
GE Corporate Res. & Dev., Niskayuna, NY, USA
fDate :
4/1/2003 12:00:00 AM
Abstract :
We present a complete set of perceptual observables that provides a unified image description for grouping, figure-ground separation, and texture analysis. Although much progress has been made recently in treating contours and texture simultaneously for image segmentation and grouping, current approaches rely on different models for contours, regions, and texture such as one-dimensional intensity discontinuities for contours and filter bank responses for texture. This results in expensive computation that arbitrates between these disparate representations at each pixel. In our approach, salient image content such as contours, regions, and texture are represented in a common, low-level framework of image observables. We model the image as a partition of surfaces bounded by intensity discontinuities and derive perceptual measures as relations between neighboring surfaces. This enables us to extend the traditional Gestalt measures based on local edge geometry and contrast to region-based measures that jointly exploit large scale image topology, photometry, and geometry. These measures provide a natural basis for grouping on multidimensional similarity criteria and texture is directly derived as relational properties on local region neighborhoods. The viability of our model is demonstrated by applying the common observables to texture recognition, figure-ground separation, and generic image segmentation.
Keywords :
computer vision; feature extraction; image classification; image segmentation; image texture; object recognition; Gestalt measures; figure-ground discrimination; geometry; grouping; image description; image segmentation; image topology; local edge geometry; perceptual observables; photometry; region-based measures; salient image content; texture classification; texture recognition; Filter bank; Geometry; Image analysis; Image recognition; Image segmentation; Image texture analysis; Large-scale systems; Multidimensional systems; Photometry; Topology;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2003.1190572