Wednesday, April 14, 2021


Feature matching, or feature correspondence serves as a core technique for image analysis and understanding. There is a wide range of applications that are closely related to it, such as object recognition, image retrieval, 3D reconstruction, image enhancement, and so on. The problems of correspondence involves clutter background, significant amount of outliers and occlusion. Moreoever, multiple translations, orientations and deformations also negatively affect the matching of features in terms of precision, recall and efficiency. To this end, we look into these problems and propose robust frameworks to resolve them.


What is the problem in feature matching?

  • Motivation: For image matching, the initial feature correspondence set
    • Can not be too large: Low recall
    • Contains corrupt matches: Low precision
    • Requires geometric checking: Time consuming

  • Proposed method: Alternate Hough and inverted Hough voting
    • Correspondence mutual checking in homography space
  • Main advantages:
    • High precision: Hough voting
    • High recall: Inverted Hough voting
    • Low computational cost: BPLR for voter filtering


Core Ideas

  • Precision:
    • The correct matches are biased to a dense cluster in the transformation space
    • We cast the task of feature matching problem into a density estimation problem
  • Recall:
    • Grouped features with high probability undergo similar transformations in matching
    • We utilize the nature of BPLR to locate non-crossboundary regions which correspond to groups of similar transformations

Core Techniques

  • Hough voting
    • The tentative correspondences are found via nearest-neighbor search in descriptor space and use to generate votes in the transformation space.
    • We use density of each correspondence in the transformation space to verify its correctness
  • Inverted hough voting
    • Recommend each feature additional transformations by investigating density distribution of nearby features covered by the same BPLR.




Robust Feature Matching with Alternate Hough and Inverted Hough Transforms

Hsin-Yi Chen, Yen-Yu Lin and Bing-Yu Chen

IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June 2013