Efficient object recognition using sampling of keypoint triples and keygraph structure

Author(s)

Dazzi, Estephan ; de Campos, Teofilo ; Hilton, Adrian ; Cesar-, Roberto M., Jr. ; IEEE

• Document type: Journal article
• Source: 2016 29TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI); v. N/A, p. 8-pg., 2016-01-01.

Abstract

We present an object matching method that employs matches of local graphs of keypoints, called keygraphs, instead of simple keypoint matches. For a keygraph match to be valid, vertex (keypoint) descriptors must be similar and both keygraphs must satisfy structural properties concerning keypoints orientation, scale, relative position and cyclic ordering; as a result, the large majority of initial incorrect keypoint matches is correctly filtered out. We introduce a novel approach to sample keypoint triples (i.e. keygraphs) in a query image, based on complementary Delaunay triangulations; this generates a linear number of triples with relation to the number of keypoints. Query keygraphs are then matched against the indexed model keypoints; each established keygraph match is used to evaluate a candidate pose (an affine transformation). The proposed method has been evaluated for object recognition and pose estimation, achieving a better performance in comparison to state-of-the-art methods. (AU)

• FAPESP's process: 14/50769-1 - Hand tracking for occupational therapy
• Grantee: Roberto Marcondes Cesar Junior
• Support Opportunities: Regular Research Grants
• FAPESP's process: 11/50761-2 - Models and methods of e-Science for life and agricultural sciences
• Grantee: Roberto Marcondes Cesar Junior
• Support Opportunities: Research Projects - Thematic Grants