Akihiro Mori

Early Recognition and Prediction of Gestures

This paper is concerned with an early recognition and prediction algorithm of gestures. Early recognition is the algorithm to provide recognition results before input gestures are completed. Motion prediction is the algorithm to predict the subsequent posture of the performer by using early recognition. In addition to them, this paper considers a gesture network for improving the performance of these algorithms. The performance of the proposed algorithm was evaluated by experiments of real-time control of a humanoid by gestures

Logical DP matching for detecting similar subsequence

A logical dynamic programming (DP) matching algorithm is proposed for extracting similar subpatterns from two sequential patterns. In the proposed algorithm, local similarity between two patterns is measured by a logical function, called support. The DP matching with the support can extract all similar subpatterns simultaneously while compensating nonlinear fluctuation. The performance of the proposed algorithm was evaluated qualitatively and quantitatively via an experiment of extracting motion primitives, i.e., common subpatterns in gesture patterns of different classes.

Automatic construction of gesture network for gesture recognition

This paper is concerned with automatic construction algorithm for gesture network. Gesture network is a network model of gestures for gesture recognition, especially early recognition and motion prediction. Manual construction of gesture network is inefficient, and thus its automatic construction method is expected; this is because gesture network has to be constructed, whenever target gestures are changed. This paper proposes an automatic construction algorithm for gesture network by logical DP matching. The experiment was conducted for evaluating the performance of the gesture network constructed automatically. The experimental result indicated that the proposed automatic construction algorithm for gesture network can be alternative of manual construction.

Fast Image Mosaicing Based on Histograms

This paper introduces a fast image mosaicing technique that does not require costly search on image domain (e.g., pixel-to-pixel correspondence search on the image domain) and the iterative optimization (e.g., gradient-based optimization, iterative optimization, and random optimization) of geometric transformation parameter. The proposed technique is organized in a two-step manner. At both steps, histograms are fully utilized for high computational efficiency. At the first step, a histogram of pixel feature values is utilized to detect pairs of pixels with the same rare feature values as candidates of corresponding pixel pairs. At the second step, a histogram of transformation parameter values is utilized to determine the most reliable transformation parameter value. Experimental results showed that the proposed technique can provide reasonable mosaicing results in most cases with very feasible computations.