The style of a gesture provides significant information for communication, and thus understanding the style is of great importance in improving gestural interfaces using hand gestures. We present a novel method to estimate temporal and spatial scale—which are considered principal elements of the style—of hand gestures. Gesture synchronization is proposed for matching progression between spatio-temporally varying gestures, and scales are estimated based on the progression matching. For comparing gestures of various sizes and speeds, gesture representation is defined by adopting turning angle representation. Also, LCSS is used as a similarity measure for reliability and robustness to noise and outliers. Performance of our algorithm is evaluated with synthesized data to show the accuracy and robustness to noise and experiments are carried out using recorded hand gestures to analyze applicability under real-world situations.

This paper presents a block-based motion vector search algorithm for video coding based on an interpolation scheme of search blocks. The basic idea of motion vector estimation between frames is to select a block in the previous frame that best matches a block in the current frame by minimizing the difference between them. In most of the search algorithms, however, the best-match block can only be on a pre-defined grid pattern. Although using a pre-defined pattern increases the search efficiency, it may also reduce the search accuracy. To balance the two aspects and to fully utilize the block information, we propose a strategy, which, instead of selecting from pre-defined blocks, searches for a best match interpolated from the pre-defined blocks. Experiment results demonstrate a better accuracy and efficiency of this search method than some commonly-used methods for different kinds of motion.