We present a method that estimates graspability measures on a single depth map for grasping objects randomly placed in a bin. Our method represents a gripper model by using two mask images, one describing a contact region that should be filled by a target object for stable grasping, and the other describing a collision region that should not be filled by other objects to avoid collisions during grasping. The graspability measure is computed by convolving the mask images with binarized depth maps, which are thresholded differently in each region according to the minimum height of the 3D points in the region and the length of the gripper. Our method does not assume any 3-D model of objects, thus applicable to general objects. Our representation of the gripper model using the two mask images is also applicable to general grippers, such as multi-finger and vacuum grippers. We apply our method to bin picking of piled objects using a robot arm and demonstrate fast pick-and-place operations for various industrial objects.

We propose an algorithm to detect human intruders by means of Leaky Coaxial cables (LCX) for wide area surveillance systems. When a human intrudes into an area between two LCX, the radiated waves will suffer considerable changes with specific fluctuations. The former version of our detection algorithm had many false detections because the algorithm used only amplitudes of in-phase/quadrature-phase (I/Q) detector signals. To develop a sophisticated detection algorithm, we analyzes I/Q detector signals in detail and introduce some critical signal features appeared in I/Q detector signals to discriminate human intruders from background noises and other irrelevant objects. We successfully reduced many of the false detections compared to the performance of the former algorithm. In this paper we introduce our LCX-based surveillance system, and describe a human intruder detection algorithm based on I/Q detector signals with typical simulation results. Current problems and remaining tasks are also discussed.

Deals with a method of detecting small moving objects in an image sequence. Although it is difficult to recognize small objects in a single image, if they are moving in an image sequence, it is easy to detect them, no matter how small they are. In order to detect small objects with robotic eyes, The authors propose a new interpretation of optical flow. If a small moving object exists in the scene, a pair of feature points appear. The authors call these points the contraction point and the diffusion point. The paper proposes an extraction method for these points.>