Shape-based attention for identification and localization of cylindrical objects

In this paper, we propose a novel framework for detecting and identifying cylindrical shapes, commonly found in daily contexts, using multi-modal visual information provided by RBG-D cameras. The current state-of-the-art methods for cylinder detection are based on RANSAC and Hough transforms which estimate cylinder parameters using 3D point cloud information. However, the presence of distracting non-cylindrical shapes leads to time-consuming parametric fitting of wrong detections, compromising the efficiency of the whole method. We tackle the aforementioned problem by introducing a biologically plausible framework, incorporating a pre-attentive mechanism which learns in a supervised and data-efficient manner to selectively discard irrelevant shapes before further processing. A set of experiments with real data are conducted to assess the advantages of our framework. The results demonstrate that combining bottom-up 3D segmentation with top-down shape-based attention allows for large speedup and accuracy improvements on cylinder identification. The qualitative and quantitative results with real data acquired from a consumer RGB-D camera, confirm the advantages of the proposed framework.