Evaluating the Stability of Spatial Keypoints via Cluster Core Correspondence Index - Institut Pasteur Access content directly
Journal Articles IEEE Transactions on Image Processing Year : 2020

Evaluating the Stability of Spatial Keypoints via Cluster Core Correspondence Index

Abstract

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 correspondencebased 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.
Fichier principal
Vignette du fichier
suvadip-2019-TIP-R2-TL.pdf (9.33 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

pasteur-03699899 , version 1 (20-06-2022)

Licence

Attribution - NonCommercial

Identifiers

Cite

Suvadip Mukherjee, Thibault Lagache, Jean-Christophe Olivo-Marin. Evaluating the Stability of Spatial Keypoints via Cluster Core Correspondence Index. IEEE Transactions on Image Processing, 2020, 30, pp.386-401. ⟨10.1109/TIP.2020.3036759⟩. ⟨pasteur-03699899⟩
25 View
73 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More