Feature extraction via heat flow analogy

Abstract

Feature extraction is an important field of image processing and computer vision. Features can be classified as low-level and high-level. Low-level features do not give shape information of the objects, where the popular low-level feature extraction
techniques are edge detection, corner detection, thresholding as a point operation and optical flow estimation. On the other hand, high-level features give shape information, where the popular techniques are active contours, region growing, template matching and the Hough transform.

In this thesis, we investigate the heat flow analogy, which is a physics based analogy, both for low-level and high-level feature extraction. Three different contributions to feature extraction, based on using the heat conduction analogy, are presented in this thesis. The solution of the heat conduction equation depends on properties of the material, the heat source as well as specified initial and boundary conditions. In our contributions, we consider and represent particular heat conduction problems, in the image and video domains, for feature extraction. The first contribution is moving-edge detection for motion analysis, which is a low-level feature extraction. The second contribution is shape extraction from images which is a high-level feature extraction. Finally, the third contribution is silhouette object feature extraction for recognition purpose and this can be considered as a combination of low-level and high-level feature
extraction.

Our evaluations and experimental results show that the heat analogy can be applied successfully both for low-level and for high-level feature extraction purposes in image processing and computer vision.

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Identifiers

ORCID for Mark Nixon: orcid.org/0000-0002-9174-5934

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