2016/03/14 by Kanzhi Wu, Xiaoyang Li, Wu, Kanzhi +7 · 1 citation
Computer Science · Engineering · Mathematics · #Advanced Image and Video Retrieval Techniques #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Detector #FOS: Computer and information sciences #Feature (linguistics) #Grayscale #Image Retrieval and Classification Techniques #Invariant (physics) #Mathematics #Pattern recognition (psychology) #Pixel #RGB color model #Robotics (cs.RO) #Robotics and Sensor-Based Localization #Rotation (mathematics) #Scale invariance #cs.CV #cs.RO
paper · pdf · doi:10.48550/arxiv.1603.04134
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2016/03/14 · arxiv created 2016/09/19 · arxiv updated 2016/09/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.