Hirokazu Madokoro

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—This paper presents a new framework to describe individual facial expression spaces, particularly addressing the dynamic diversity of facial expressions that appear as an exclamation or emotion, to create a unique space for each person. We name this framework Facial Expression Spatial Charts (FESCs). The FESCs are created using Self–Organizing Maps (SOMs)(More)
— This paper presents a digital hardware Back-Propagation (BP) model for real-time learning in the field of video image processing. The model is a layer parallel architecture with a 16-bit fixed point specialized for video image processing. We have compared our model with a standard BP model that used a double-precision floating point. Simulation results(More)
— This paper presents a representation method of facial expression changes using Adaptive Resonance Theory (ART) networks. Our method extracts orientation selectivity of Gabor wavelets on ART networks, which are unsupervised and self-organizing neural networks that contain a stability-plasticity tradeoff. The classification ability of ART is controlled by a(More)
— This paper presents an unsupervised segmentation method using hybridized Self-Organizing Maps (SOMs) and Fuzzy Adaptive Resonance Theory (ART) based only on the brightness distribution and characteristics of head MR images. We specifically examine the features of mapping while maintaining topological relations of weights with SOMs and while integrating a(More)
— We propose an objective segmentation method for Magnetic Resonance (MR) images of the brain using self-mapping characteristics of one-dimensional Self-Organizing Maps (SOM). The proposed method requires no operators to specify the representative points, but can segment tissues (such as cerebrospinal fluid, gray matter and white matter) needed for(More)