RISAS: A Novel Rotation, Illumination, Scale Invariant Appearance and Shape Feature
This work addresses the need for invariant features in computer vision applications like object recognition, though it appears incremental as it builds on existing methods like Harris corner detection.
The paper tackles the problem of robust feature detection and description for RGB-D images under variations in rotation, illumination, scale, and viewpoint, resulting in the RISAS feature that outperforms CSHOT and LOIND in experiments.
This paper presents a novel appearance and shape feature, RISAS, which is robust to viewpoint, illumination, scale and rotation variations. RISAS consists of a keypoint detector and a feature descriptor both of which utilise texture and geometric information present in the appearance and shape channels. A novel response function based on the surface normals is used in combination with the Harris corner detector for selecting keypoints in the scene. A strategy that uses the depth information for scale estimation and background elimination is proposed to select the neighbourhood around the keypoints in order to build precise invariant descriptors. Proposed descriptor relies on the ordering of both grayscale intensity and shape information in the neighbourhood. Comprehensive experiments which confirm the effectiveness of the proposed RGB-D feature when compared with CSHOT and LOIND are presented. Furthermore, we highlight the utility of incorporating texture and shape information in the design of both the detector and the descriptor by demonstrating the enhanced performance of CSHOT and LOIND when combined with RISAS detector.