Evaluating the Stability of Spatial Keypoints via Cluster Core Correspondence Index

• Published in: IEEE transactions on image processing Vol. 30; pp. 386 - 401
• Main Authors: Mukherjee, Suvadip, Lagache, Thibault, Olivo-Marin, Jean-Christophe
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; benchmarking; Clusters; Computer Science; Computer vision; density estimation; Detectors; Estimation; Feature extraction; Hyperspaces; Image analysis; Indexes; keypoint detection; level set; Parameter estimation; Pattern analysis; Quality assessment; Robustness (mathematics); Sensors; Spatial analysis; Spatial distribution; Stability analysis; Stability criteria; statistics; Task analysis; Three-dimensional displays; Visual tasks; Spatial analysis; keypoint detection; benchmarking; density estimation; level set; statistics
• ISSN: 1057-7149; 1941-0042; 1941-0042
• DOI: 10.1109/TIP.2020.3036759

Detection and analysis of informative keypoints is a fundamental problem in image analysis and computer vision. Keypoint detectors are omnipresent in visual automation tasks, and recent years have witnessed a significant surge in the number of such techniques. Evaluating the quality of keypoint detectors remains a challenging task owing to the inherent ambiguity over what constitutes a good keypoint. In this context, we introduce a reference based keypoint quality index which is based on the theory of spatial pattern analysis. Unlike traditional correspondence-based quality evaluation which counts the number of feature matches within a specified neighborhood, we present a rigorous mathematical framework to compute the statistical correspondence of the detections inside a set of salient zones (cluster cores) defined by the spatial distribution of a reference set of keypoints. We leverage the versatility of the level sets to handle hypersurfaces of arbitrary geometry, and develop a mathematical framework to estimate the model parameters analytically to reflect the robustness of a feature detection algorithm. Extensive experimental studies involving several keypoint detectors tested under different imaging scenarios demonstrate efficacy of our method to evaluate keypoint quality for generic applications in computer vision and image analysis.