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Minoru ASADA Masahiro KIMURA Yoshiaki SHIRAI
Integration of 2
Haisong GU Yoshiaki SHIRAI Minoru ASADA
This paper presents a method for spatial and temporal segmentation of long image sequences which include multiple independently moving objects, based on the Minimum Description Length (MDL) principle. By obtaining an optimal motion description, we extract spatiotemporal (ST) segments in the image sequence, each of which consists of edge segments with similar motions. First, we construct a family of 2D motion models, each of which is completely determined by its specified set of equations. Then, based on these sets of equations we formulate the motion description length in a long sequence. The motion state of one object at one moment is determined by finding the model with shortest description length. Temporal segmentation is carried out when the motion state is found to have changed. At the same time, the spatial segmentation is globally optimized in such a way that the motion description of the entire scene reaches a minimum.
Hsiao-Jing CHEN Yoshiaki SHIRAI Minoru ASADA
A method for detecting multiple rigid motions in images from an optical flow field obtained with multi-scale, multi-orientation filters is proposed. Convolving consecutive gray scale images with a set of eight orientation-selective spatial Gaussian filters yields eight gradient constraint equations for the two components of a flow vector at every location. The flow vector and an uncertainty measure are obtained from these equations. In the neighborhood of motion boundary, the uncertainty of the flow vectors increase. By using multiple sets of filters of different scales, multiple flow vectors are obtained at every location, from which the one with minimal uncertainty measure is selected. The obtained flow field is then segmented in order to solve the aperture problem and to remove noise without blurring discontinuity in the flow field. Discontinuities are first detected as those locations where flow vectors have relatively larger uncertainty measures. Then similar flow vectors are gouped into regions. By modeling flow vectors, regions are merged to form segments each of which belongs to a planar patch of a rigid object in the scene.
Akira OKAMOTO Yoshiaki SHIRAI Minoru ASADA
This paper describes a method for describing a three-dimensional (3-D) scene by integrating color and range data. Range data is obtained by a feature-based stereo method developed in our laboratory. A color image is segmented into uniform color regions. A plane is fitted to the range data inside a segmented region. Regions are classified into three types based on the range data. A certain types of regions are merged and the others remain unless the region type is modified. The region type is modified if the range data on a plane are selected by removing of the some range data. As a result, the scene is represented by planar surfaces with homogeneous colors. Experimental results for real scenes are shown.
Ryo IWAKI Hiroki YOKOYAMA Minoru ASADA
The step size is a parameter of fundamental importance in learning algorithms, particularly for the natural policy gradient (NPG) methods. We derive an upper bound for the step size in an incremental NPG estimation, and propose an adaptive step size to implement the derived upper bound. The proposed adaptive step size guarantees that an updated parameter does not overshoot the target, which is achieved by weighting the learning samples according to their relative importances. We also provide tight upper and lower bounds for the step size, though they are not suitable for the incremental learning. We confirm the usefulness of the proposed step size using the classical benchmarks. To the best of our knowledge, this is the first adaptive step size method for NPG estimation.