Epipolar based Light Field Key-Location Detector

Nowadays, visual features play a key role, as they can provide a concise representation of visual data that is efficient for multiple tasks, notably content retrieval and object recognition. In parallel, visual sensors have been improving, targeting richer acquisitions of the light in a visual scene. In this context, the so-called light field cameras, which have recently emerged, are able to go beyond the standard acquisition models, by enriching the visual representation with directional light measures for each pixel position, e.g. by using a so-called lenslet light field camera. At this stage, not much research has been made in the field of feature detection and description for the emerging lenslet light field format. In this context, this paper proposes a feature detector suitable for lenslet light field images based on the exploitation of an alternative visual parametrization of the light field, called the Epipolar Planar Image (EPI). The proposed detector is heavily based on line detection in the EPI representation, since 3D points in the visual scene are mapped to line segments in an EPI, and the detector output is referred as key-locations. The proposed light field key-location detector is assessed with a solid evaluation framework using a large light field dataset. In comparison to the 2D SIFT detector, up to 10% improvements were achieved for the widely used repeatability metric.