Itsuo Kumazawa

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In this paper, an experiment is conducted which proves that multi layer feed forward neural networks are capable of compressing 3D polygon meshes. Our compression method not only preserves the initial accuracy of the represented object but also enhances it. The neural network employed includes the vertex coordinates, the connectivity and normal information(More)
In this paper, a study of assistive devices with multi-modal feedback is conducted to evaluate the efficiency of haptic and auditory information towards the users' mouse operations. Haptic feedback, generated by a combination of wheels driven by motors, is provided through the use of the haptic mouse. Meanwhile, audio feedback either in the form of(More)
In this paper, we present a new neural network (NN) for three-dimensional (3D) shape reconstruction. This NN provides an analytic mapping of an initial 3D polyhedral model into its projection depth images. Through this analytic mapping, the NN can analytically refine vertices position of the model using error back-propagation learning. This learning is(More)
In this paper, we present a neural-network learning scheme for face reconstruction. This scheme, which we called as Smooth Projected Polygon Representation Neural Network (SPPRNN), is able to successively refine the polygon’s vertices parameter of an initial 3D shape based on depth-maps of several calibrated images taken from multiple views. The depth-maps,(More)
This paper presents a new neural network (NN) scheme for recovering three dimensional (3D) transparent surface. We view the transparent surface modeling, not as a separate problem, but as an extension of opaque surface modeling. The main insight of this work is we simulate transparency not only for generating visually realistic images, but for recovering(More)
This paper presents a combinatorial (decision tree induction) technique for transparent surface modeling from polarization images. This technique simultaneously uses the object’s symmetry, brewster angle, and degree of polarization to select accurate reference points. The reference points contain information about surface’s normals position and direction at(More)
Rice cultivation date estimation based on remote sensing data is critical information to evaluate the damages in rice fields from natural disasters. In this study, the 8-day composite normalized difference vegetation index (NDVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data was modeled as a triply modulated cosine function,(More)