In this paper, we propose a descriptor as a shape signature and the projective refinement as a verification method for recognizing 2D curved objects with occlusions from their partial views. For an extracted curve segment, we compute a series of the geometric invariance of equally spaced five co-planar points on the curve. Thus the resulting descriptor is invariant only under rotation, translation, and scale, but sufficient similarity is preserved even under large distortions. It is more stable and robust since it does not need derivatives. We use this transformation-invariant descriptor to index a hash table. We show the efficiency of the method through experiments using seriously distorted images of 2-D curved objects with occlusions.

A discrete dynamic model for defining contours in 2-D medical images is presented. An active contour in this objective is optimized by a dynamic programming algorithm, for which a new constraint that has fast and stable properties is introduced. The internal energy of the model depends on local behavior of the contour, while the external energy is derived from image features. The algorithm is able to rapidly detect convex and concave objects even when the image quality is poor.

We present a navigation system using ultrasonic sensors for unknown and dynamic indoor environments. To achieve the robustness and flexibility of the mobile robot, we develop a behavior-based system architecture, consisting of multi-layered behaviors. Basic behaviors required for the navigation of a mobile robot, such as, avoiding obstacles, moving towards free space, and following targets, are redundantly developed as agents and combined in a behavior-based system architecture. An extended potential filed method is developed to produce the appropriate velocity and steering commands for the behaviors of the robot. We demonstrate the capabilities of our system through real world experiments in unstructured dynamic office environments using an indoor mobile robot.