We present a novel contour-based approach that recognizes object classes in real-world scenes using simple and generic shape primitives of line segments and ellipses. Compared to commonly used contour fragment features, these primitives support more efficient representation since their storage requirements are independent of object size. Additionally, these primitives are readily described by their geometrical properties and hence afford very efficient feature comparison. We pair these primitives as shape-tokens and learn discriminative combinations of shape-tokens. Here, we allow each combination to have a variable number of shape-tokens. This, coupled with the generic nature of primitives, enables a variety of class-specific shape structures to be learned. Building on the contour-based method, we propose a new hybrid recognition method that combines shape and appearance features. Each discriminative combination can vary in the number and the types of features, where these two degrees of variability empower the hybrid method with even more flexibility and discriminative potential. We evaluate our methods across a large number of challenging classes, and obtain very competitive results against other methods. These results show the proposed shape primitives are indeed sufficiently powerful to recognize object classes in complex real-world scenes.

A novel ellipse detector based upon edge following is proposed in this paper. The detector models edge connectivity by line segments and exploits these line segments to construct a set of elliptical-arcs. Disconnected elliptical-arcs which describe the same ellipse are identified and grouped together by incrementally finding optimal pairings of elliptical-arcs. We extract hypothetical ellipses of an image by fitting an ellipse to the elliptical-arcs of each group. Finally, a feedback loop is developed to sieve out low confidence hypothetical ellipses and to regenerate a better set of hypothetical ellipses. In this aspect, the proposed algorithm performs self-correction and homes in on "difficult" ellipses. Detailed evaluation on synthetic images shows that the algorithm outperforms existing methods substantially in terms of recall and precision scores under the scenarios of image cluttering, salt-and-pepper noise and partial occlusion. Additionally, we apply the detector on a set of challenging real-world images. Successful detection of ellipses present in these images is demonstrated. We are not aware of any other work that can detect ellipses from such difficult images. Therefore, this work presents a significant contribution towards ellipse detection.