The possibility to provide an adequate task assistance using underactuated robots for human-robot tool comanipulation is investigated. This novel approach does not take into account any a priori knowledge about user depending parameters however optimizes the robot-user synergy, for instance during US breast examinations. Results show that the examination time can be reduced and a tendency for increasing scanning accuracy using underactuated robots.

Fuzzy connected filters were recently introduced as an extension of connected filters within the fuzzy set framework. They rely on the representation of the image gray levels by fuzzy quantities, which are suitable to represent imprecision usually contained in images. No robust construction method of these fuzzy images has been introduced so far. In this paper we propose a generic method to fuzzify a crisp image in order to explicitly take imprecision on grey levels into account. This method is based on the conversion of statistical noise present in an image, which cannot be directly represented by fuzzy sets, into a denoising imprecision. The detectability of constant gray level structures in these fuzzy images is also discussed.

A perceptual multiresolution filtering method (Moon and Spencer, 1945) based on human perception models is proposed. The main idea is to detect and remove the irrelevant structures at different scales using the just-noticeable contrast notion and luminance adaptation. The processing is done in the Laplacian and Gaussian pyramid decompositions of the image. The consistency of the method is demonstrated on a gray-level image.

An improvement of the well-known block truncation coding (BTC) method, introduced by Delp and Mitchell (1979), is proposed. The basic idea, derived from previous work of Beghdadi and Le Negrate (1989, 1991) on contrast enhancement and image filtering using a local contrast measure is to exploit edginess information in estimating both the quantization threshold and the reconstruction levels. The results obtained on actual images clearly show the efficiency of the method in preserving edge information compared to similar approaches.

This paper proposes a vector quantization (VQ) scheme that improves the quality of the reconstructed image by correcting quantization artifacts. The basic idea of the coding scheme is to treat separately the blocks containing edges (edge blocks), as they contain important perceptual information. The edge blocks are identified using an edge detection algorithm and are labeled by an escape index, and their reconstruction is done via an interpolation procedure that exploits the spatial correlation in the image. The remaining blocks (smooth blocks) are coded using standard VQ.

This paper generalizes trellis based near-lossless image compression schemes, in order to satisfy two constraints: a local one, which enforces an upper bound /spl epsiv/ for the reconstruction error at every pixel, and a global one, which penalizes the quantity R+/spl lambda/D (the rate and distortion over each line of the image). By changing /spl lambda/ we obtain gradual changes from lossless coding (/spl lambda//spl infin/) to DPCM near-lossless coding (/spl lambda/=0), resulting in a distinct rate-distortion curve for each tolerance /spl epsiv/. We propose here a trellis-based fixed-slope near-lossless compression approach, that fits the adaptive context coding scheme ASBOSC introduced in 1997. The experimental results over a set of test images in near-lossless mode are better than the results obtained with other near-lossless techniques.

Starting from the concepts introduced by L.O. Chua, and L.K. Chen (1976), the paper presents a generalized method of diakoptic analysis for large-scale piecewise-linear nonlinear electric circuits containing any type of independent or coupled lumped circuit elements. The generalization lies in the way the subcircuits are analyzed and in determining the currents and the voltages of the interconnected branches among subcircuits.>