Keypoint Detection in RGB-D Images Using Binary Patterns

Detection of keypoints in an image is a crucial step in most registration and recognition tasks. The information encoded in RGB-D images can be redundant and, usually, only specific areas in the image are useful for the classification process. The process of identifying those relevant areas is known as keypoint detection. The use of keypoints can facilitate the following stages in the image processing process by reducing the search space. To properly represent an image by means of a set of keypoints, properties like repeatability and distinctiveness have to be fullfilled. In this work, we propose a keypoint detection technique based on the Shape Binary Pattern (SBP) descriptor that can be computed from RGB-D images. Next, we rely on this method to identify the most discriminative patterns that are used to detect the most relevant keypoint. Experiments on a well-know benchmark for 3D keypoint detection have been performed to assess our proposal.