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Building Local Features from Pattern-Based Approximations of Patches: Discussion on Moments and Hough Transform

Abstract

The paper overviews the concept of using circular patches as local features for image description, matching, and retrieval. The contents of scanning circular windows are approximated by predefined patterns. Characteristics of the approximations are used as feature descriptors. The main advantage of the approach is that the features are categorized at the detection level, and the subsequent matching or retrieval operations are, thus, tailored to the image contents and more efficient. Even though the method is not claimed to be scale invariant, it can handle (as explained in the paper) image rescaling within relatively wide ranges of scales. The paper summarizes and compares various aspects of results presented in previous publications. In particular, three issues are discussed in detail: visual accuracy, feature localization, and robustness against "visual intrusions." The compared methods are based on relatively simple tools, that is, area moments and modified Hough transform, so that the computational complexity is rather low.

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Correspondence to Andrzej Sluzek.

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Keywords

  • Computer Vision
  • Local Feature
  • Feature Descriptor
  • Image Content
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