This paper describes a method to localize objects which are partially buried in an unstructured environment. The objects are limited to surfaces of revolution in the present study. Candidate regions are extracted as convex surface regions from a range data that is obtained by area-based stereo matching. The surface type at each point is determined based on the signs of the maximum and minimum curvatures, which are calculated by fitting a quadric in the local window. For each candidate region, multiple hypotheses for the position and direction of the rotational axis are generated for each object model. Each hypothesis is verified and improved by an iterative method, and the most reliable hypothesis is adopted. The experimental results demonstrate the effectiveness of the proposed method for actual range data.

A retrieval method, called running time interval clustering (Rutic), is proposed. The method detects time sequence intervals similar to the query time sequence in a vast database of time sequences such as video and audio data. Conventional methods, such as RIFCDP, handled queries of any interval lengths, but they require a relatively high computational burden and are not suitable for real-time retrieval from a large database. The Rutic method allows retrieval results to be output for each input frame of the time sequence queries, thus enabling retrieval from a database without segmentation. The computational burden of the Rutic method is so small that it enables the so-called real-time spotting retrieval. This report describes the algorithm of the Rutic method, and verifies its validity through comparison with other methods based on image search experiments.

This paper proposes the concept of "agent networks" to realize flexible and unified integration of multi-agent intelligent robot systems. As an example, we developed a hand-to-hand robot on the agent network. Its robust and flexible behaviour proved the effectiveness of the method.

Describes a newly developed labeling board based on an algorithm in a combination of raster scanning and boundary tracking. This board employs the boundary tracking method and does not require a working memory for temporary labels, and thus it can process labels in size 512*512 images without restricting the maximum numbers of connected regions. It can also count holes within regions, and control labeling by using the perimeter of regions, at the same time of labelling. The processing time is 50 msec for size 512*512 real images.>

It is difficult to obtain sufficient accuracy in view of the outdoor environment to perform 3D stereo measurement. The authors propose a method which involves moving a single camera and prolonging the base line, then measuring the motion parameters of the camera by image correspondence. Primitives are used as units of correspondence to make the matching stable. This paper presents the method and some experimental results.>

The authors extend the algorithm for stereo vision on a mobile robot in a unstructured environment to generate the 3-D b-rep of the large environment by incrementally integrating local and incomplete stereo data. The stereo vision is based on the 2-D b-reps of stereo images. The 2-D b-rep is a 2-D version of the 3-D b-rep to describe an image; the image is represented in a hierarchy of regions, boundaries, and line-segments. The b-rep is obtained for each of stereo images and the two images are matched on this data structure. The direct output of stereo is 3-D wireframes, a set of 3-D loops (boundaries) composed of 3-D line-segments, of the scene. A unique surface is then defined for each loop to make the 3-D b-rep, the consistent geometrical model, of the scene. It is necessary for the mobile robot to change views of the scene to make uninterpretable loops interpretable by finding missing segments, and thus make the complete b-rep of the scene.>

This paper describes a method of planning grasps for parallel two-fingered grippers in order to pick up user-specified objects and place them at user-specified configurations. By characterizing local space around gripping positions, the method can plan grasps where the hand can move as freely as possible and then easily plan a path. It can plan regrasping where the grasps themselves and the regrasping placements are computed flexibly according to the environment. It can plan minimal sequences of regraspings for objects that have rotational symmetries.>

All stereo systems assume that the correct camera parameters are obtained. In fact, those parameters can be calibrated in advance using known test patterns. However, not only there is calibration errors but also some parameters are easy to change after the calibration. Even if the errors may be small, the effects on identifying corresponding points and computing distances are very large. It is infeasible to compute camera parameters every time befort? observation especially for stereo cameras which change its focus of attention by convergence or those on mobile robots which observe their environment and acquire depth information. The self-calibration of stereo cameras, which autonomously computes its current camera parameters from unknown observed data, is really necessary for real stereo systlxns. In this paper, we propose a self-calibration method based on boundary representations of a pair of stereo images.

The problem of detecting occluding and ridge edges is solved for objects with planar surfaces, using stereo vision that is based on directional boundary segments of regions in a pair of stereo images. Methods are proposed to detect occlusions in which occluding edges are explicitly identified: to classify the other unidentified edges as either occluding or ridge edges, using the explicitly occluding edges to classify ridge edges further as convex, concave, or flat (paint or shadow) edges, based on the surface normals of both sides of the edges; and to classify the space of the scene into free, occupied, or uninterpretable real surfaces.>

Stereo problems regarding range resolution, the output available for higher-level postprocessing, and the so-called correspondence problem are discussed. To solve the problems, it is proposed to use boundary segments of regions as units for correspondence between a pair of stereo images. Not only the features of regions and segments themselves, but also the high connectivity of segments can be used as strong constraints for correspondence. A planarity constraint is introduced for segments on planar surfaces. Since the output form is a boundary representation, the most general solid model, it can be used directly as the input to an object recognition system without any preprocessing.>