An Improved Method for Object Instance Detection Based on Object Center Estimation and Convex Quadrilateral Verification
This paper proposes a new frame which combines kernel density estimation with convex quadrilateral verification to achieve identification and localization of object instance in zooming image.Reference object centers are first calculated based on scales,orientations,and reference vectors for all matched key points.Then the valid object center with density peak value is held.A region of interest with evaluated radius is confirmed based on the scale of the center point and row and column number of the corresponding training image.Homography matrix is computed using the matched key points within the region.Finally,four edges of the candidate training image are mapped as a quadrilateral.Only ff the quadrilateral is convex that the object instance is detected.The experimental results prove that the proposed approach provides high efficiency for real-time applications with robustness.
Object instance detection Zooming image density estimation region of interest convex quadrilateral verification
Qiang Zhang Daokui Qu Fang Xu Kai Jia
State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenya University of Chinese Academy of Sciences,Beijing,China Shenyang SIASUN Robot & Automation Co.,LTD.,China,Shenyang,China State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenya
国际会议
重庆
英文
174-177
2016-03-20（万方平台首次上网日期，不代表论文的发表时间）