A Mid-Level Approach to Contour-based Categorical Object Recognition


This paper proposes a method for detecting generic classes of objects from their representative contours that can be used by a robot with vision to find objects in cluttered environments. The approach uses a mid-level image operator to group edges into contours which likely correspond to object boundaries. This mid-level operator is used in two ways, bottom-up on simple edges and top-down incorporating object shape information, thus acting as the intermediary between low-level and high-level information. First, the mid-level operator, called the image torque, is applied to simple edges to extract likely fixation locations of objects. Using the operator’s output, a novel contour-based descriptor is created that extends the shape context descriptor to include boundary ownership information and accounts for rotation. This descriptor is then used in a multi-scale matching approach to modulate the torque operator towards the target, so it indicates its location and size. Unlike other approaches that use edges directly to guide the independent edge grouping and matching processes for recognition, both of these steps are effectively combined using the proposed method. We evaluate the performance of our approach using four diverse datasets containing a variety of object categories in clutter, occlusion and viewpoint changes. Compared with current state of the art approaches, our approach is able to detect the target with less false alarms in most object categories. The performance is further improved when we exploit depth information available from the Kinect RGB-Depth sensor by imposing depth consistency when applying the image torque.

Cite this paper

@inproceedings{Teo2013AMA, title={A Mid-Level Approach to Contour-based Categorical Object Recognition}, author={Ching Lik Teo and Cornelia Ferm{\"{u}ller and Yiannis Aloimonos}, year={2013} }