• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.28 no.10C
• /
• pp.1023-1032
• /
• 2003

Object-based coding, such as MPEG-4, enables various content-based functionalities for multimedia applications. In order to support such functionalities, as well as to improve coding efficiency, each frame of video sequences should be segmented into video objects. In this paper. we propose an effective video object segmentation method using nonlinear multiscale filtering and spatio-temporal information. Proposed method performs a spatial segmentation using a nonlinear multiscale filtering based on the stabilized inverse diffusion equation(SIDE). And, the segmented regions are merged using region adjacency graph(RAG). In this paper, we use a statistical significance test and a time-variant memory as temporal segmentation methods. By combining of extracted spatial and temporal segmentations, we can segment the video objects effectively. Proposed method is more robust to noise than the existing watershed algorithm. Experimental result shows that the proposed method improves a boundary accuracy ratio by 43% on "Akiyo" and by 29% on "Claire" than A. Neri's Method does.

• Journal of Biomedical Engineering Research
• /
• v.22 no.3
• /
• pp.249-257
• /
• 2001

The voluntary contracted EMG signal of spinal cord injured patients is very small because the information from central nervous system is not sufficiently transmitted to $\alpha$ motor neuron or muscle fiber. Therefore the acquisited EMG signal from needle or surface electrodes can not be identified obvious voluntary contraction pattern by muscle movement. In this paper we propose the extraction technique of voluntary muscle contraction and relaxation pattern from EMG signal of spinal cord injured patient whose EMG signal is composed of the linear sum of mo색 unit action potentials with two noise sources, additive noise assumed to be white Gaussian noise and high frequency discharge assumed to be not motor unit action potential but impulsive noise. In order to eliminate impulsive noise and additive noise from voluntary contracted EMG signal, we use the FatBear filter which is a nonarithmetic piecewise constant filter, and multiscale nonlinear wavelet denoising processing, respectively. The proposed technique is applied to the EMG signal acquisited from transverse myelitis patients to extract voluntary muscle contraction pattern.