Contour-based object detection

The arrival of appearance-based image features has dramatically influenced the field of visual object recognition. Previous work has shown, however, that contour curvature and junctions are important for shape representation and detection. We investigate a local representation of contours for object detection that complements appearance-based information, such as texture. We present a non-parametric representation of contours, curvature, and junctions which enables their accurate localization. We combine contour and appearance information into a general, voting-based detection algorithm. Besides detecting objects, we demonstrate that this approach reveals the most relevant contours and junctions supporting each object hypothesis. The experiments confirm that our contourbased representation compliments appearance information and the performance of baseline voting methods is significantly improved.