Masahide Naemura

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Although it has been observed that motion-compensated frame differences increase toward block boundaries and overlapped block motion compensation (OBMC) has been shown to provide reduced blocking artifacts as well as improved prediction accuracy, there is almost no satisfactory theoretical basis that clearly interprets the space-dependent characteristics of(More)
This paper presents a technique for integrating multiple visual features for tracking moving objects. Our proposed method consists of observation (pattern-matching) units and prediction units, which form a ladder structure. The major feature of our proposed method is that each of the observation units with different pattern matching algorithms is executed(More)
This paper proposes a distributed state estimation architecture for multi-sensor fusion. The system consists of networked subsystems that cooperatively estimate the state of a common target from their own observations. Each subsystem is equipped with a self-contained particle filter that can operate in stand-alone as well as in network mode with a particle(More)
This paper presents a robust and reconfigurable object tracker that integrates multiple visual features from multiple views. The tandem modular architecture stepwise refines the estimate of trajectories of the objects in the world coordinates using many plug-ins that observe various features such as texture, color, region and motion in 2D images acquired by(More)
We developed a new device-free user interface for TV viewing that uses a human gesture recognition technique. Although many motion recognition technologies have been reported, no man–machine interface that recognizes a large enough variety of gestures has been developed. The difficulty was the lack of spatial information that could be acquired from normal(More)
We propose a method for estimating dancing skills based on rhythmical factors extracted by video processing. The proposed method consists of motion analysis to obtain motion parameters of human body parts and data analysis to transform the obtained complex motion parameters into compact forms that represent the dance actions. We use a Kalman filter to(More)
We present a new human motion recognition technique for a hands-free user interface. Although many motion recognition technologies for video sequences have been reported, no man-machine interface that recognizes enough variety of motions has been developed. The difficulty was the lack of spatial information that could be acquired from video sequences(More)