Author:

Keywords:

affine region detectors, invariant image description, local features, performance evaluation, widely separated views, invariant regions, scale, PSI_VISICS, Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, INVARIANT, SCALE, 0801 Artificial Intelligence and Image Processing, Artificial Intelligence & Image Processing, 4603 Computer vision and multimedia computation, 4607 Graphics, augmented reality and games, 4611 Machine learning

Abstract:

The paper gives a snapshot of the state of the art in affine covariant region detectors, and compares their performance on a set of test images under varying imaging conditions. Six types of detectors are included: detectors based on affine normalization around Harris (Mikolajczyk and Schmid, 2002; Schaffalitzky and Zisserman, 2002) and Hessian points (Mikolajczyk and Schmid, 2002), a detector of 'maximally stable extremal regions', proposed by Matas et al. (2002); an edge-based region detector (Tuytelaars and Van Gool, 1999) and a detector based on intensity extrema (Tuytelaars and Van Gool, 2000), and a detector of 'salient regions', proposed by Kadir, Zisserman and Brady (2004). The performance is measured against changes in viewpoint, scale, illumination, defocus and image compression.