2020/01/31 by Wenbo Dong, Pravakar Roy, Cheng Peng +1 · 88 citations
Computer Science · Earth and Planetary Sciences · Environmental Science · Mathematics · #3D Surveying and Cultural Heritage #Artificial intelligence #Bounding overwatch #Cluster analysis #Computer science #Computer vision #Ellipse #Feature (linguistics) #Geometry #Image and Object Detection Techniques #Mathematics #Object (grammar) #Object detection #Pattern recognition (psychology) #Remote Sensing and LiDAR Applications #cs.CV #cs.RO
paper · pdf · doi:10.1109/tip.2021.3050673
published in IEEE Transactions on Image Processing 30, 2193-2206 (Institute of Electrical and Electronics Engineers) · 18 pages, 20 figures, 7 tables
arxiv created 2020/11/14 · openalex publication_date 2021/01/01 · arxiv updated 2021/02/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Images of heavily occluded objects in cluttered scenes, such as fruit clusters in trees, are hard to segment. To further retrieve the 3D size and 6D pose of each individual object in such cases, bounding boxes are not reliable from multiple views since only a little portion of the object's geometry is captured. We introduce the first CNN-based ellipse detector, called Ellipse R-CNN, to represent and infer occluded objects as ellipses. We first propose a robust and compact ellipse regression based on the Mask R-CNN architecture for elliptical object detection. Our method can infer the parameters of multiple elliptical objects even they are occluded by other neighboring objects. For better occlusion handling, we exploit refined feature regions for the regression stage, and integrate the U-Net structure for learning different occlusion patterns to compute the final detection score. The correctness of ellipse regression is validated through experiments performed on synthetic data of clustered ellipses. We further quantitatively and qualitatively demonstrate that our approach outperforms the state-of-the-art model (i.e., Mask R-CNN followed by ellipse fitting) and its three variants on both synthetic and real datasets of occluded and clustered elliptical objects.