Hirokazu Madokoro

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networks. We specifically examine the dynamic diversity of facial expressions in time-series facial images after conversion using Gabor wavelet filters. The proposed method consists of three steps: the first step is to extract topological features from time-series facial image datasets using SOMs; the second step is to integrate weights of SOM into(More)
This paper presents a method for classification and recognition of behavior patterns based on interest from human trajectories at an event site. Our method creates models using Hidden Markov Models (HMMs) for each human trajectory quantized using One-Dimensional Self-Organizing Maps (1D-SOMs). Subsequently, we apply Two-Dimensional SOMs (2D-SOMs) for(More)
This paper presents a method for representation of facial expression changes using orientation selectivity of Gabor wavelets on Adaptive Resonance Theory (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 parameter called the(More)
—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 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)
This paper presents an unsupervised category classification method for time-series images that combines incremental learning of Adaptive Resonance Theory-2 (ART-2) and self-mapping characteristic of Counter Propagation Networks (CPNs). Our method comprises the following procedures: 1) generating visual words using Self-Organizing Maps (SOM) from(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 a segmentation method of multiple object regions based on visual saliency. Our method comprises three steps. First, attentional points are detected using saliency maps (SMs). Subsequently, regions of interest (RoIs) are extracted using scale-invariant feature transform (SIFT). Finally, foreground regions are extracted as object regions(More)