A biologically inspired scale-space for illumination invariant feature detection

Abstract

This paper presents a new illumination invariant operator, combining the nonlinear characteristics of biological center-surround cells with the classic difference of Gaussians operator. It specifically targets the underexposed image regions, exhibiting increased sensitivity to low contrast, while not affecting performance in the correctly exposed ones. The proposed operator can be used to create a scale-space, which in turn can be a part of a SIFT-based detector module. The main advantage of this illumination invariant scale-space is that, using just one global threshold, keypoints can be detected in both dark and bright image regions. In order to evaluate the degree of illumination invariance that the proposed, as well as other, existing, operators exhibit, a new benchmark dataset is introduced. It features a greater variety of imaging conditions, compared to existing databases, containing real scenes under various degrees and combinations of uniform and non-uniform illumination. Experimental results show that the proposed detector extracts a greater number of features, with a high level of repeatability, compared to other approaches, for both uniform and non-uniform illumination. This, along with its simple implementation, renders the proposed feature detector particularly appropriate for outdoor vision systems, working in environments under uncontrolled illumination conditions.

An approach to vision-based localisation with binary features for partially sighted people

“…These popular benchmarks contain base images, as well as sequences of transformed images with known homographies between them. In datasets, there are [12], b Phos [36], c the AH, d the DC, and e the BR [29] images that exhibit a large amount of scaling, rotation, viewpoint change, blur, illumination changes, exposure, or compression. Figure 2a contains some images from these datasets.…”

“…Oxford and Heinly et. al datasets contain mostly rotated and scaled images, and in order to provide more thorough evaluation of the descriptors against various illumination conditions, Phos dataset [36] was used. Phos contains 15 scenes captured changing the strength of uniform and degrees of non-uniform illumination.…”

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“…They were rotated (90 • and 45 • ) and scaled (1/2 and 2/3). Then, 225 images from Phos dataset [29] were added to the resulting dataset. Phos contains 15 scenes captured under different illumination conditions (i.e., uniform and nonuniform).…”

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“…3). In particular, Wall and Graffiti datasets [3,4,25,29] are typical benchmarks to evaluate keypoint robustness against viewpoint changes while Book dataset [26,27] is employed to analyze keypoint robustness against lighting changes.…”