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A method is described for detecting salient regions in remote-sensed images, based on scale and contrast interaction. Salient objects are modeled as spatially localized and contrasted structures, and their detection exploits a probabilistic mixture model that combines inputs from contrast and scale information to classify each pixel in salient/nonsalient class, giving a binary segment of the image, also giving rise to a learning scheme (EM algorithm).

An ultrasonic processing arrangement is discussed that can compute three-dimensional (3-D) echo location more efficiently than can be accomplished using one-dimensional (1-D) methods.

Research into a generic approach to predict the output accuracy of an algorithm without running it, by a careful study of the local context, is presented. This technique can be used for a chain of several software modules.

Quantification of cell proliferation and monitoring its kinetics are essential in fields of research such as developmental biology, oncology, etc. Although several proliferation assays exist, monitoring cell proliferation kinetics remains challenging. We present a novel cell proliferation assay based on real-time monitoring of cell culture inside a standard incubator using a lens-free video-microscope, combined with automated detection of single cell divisions over a population of several thousand cells. Since the method is based on direct visualization of dividing cells, it is label-free, continuous, and not sample destructive. Kinetics of cell proliferation can be monitored from a few hours to several days. We compare our method to a standard assay, the EdU proliferation assay, and as proof of principle, we demonstrate concentration-dependent and time-dependent effect of actinomycin D--a cell proliferation inhibitor. [ABSTRACT FROM AUTHOR]

We are given a set of points in a space of high dimension. For instance, this set may represent many visual appearances of an object, a face, or a hand. We address the problem of approximating this set by a manifold in order to have a compact representation of the object appearance. When the scattering of this set is approximately an ellipsoid, then the problem has a well-known solution given by Principal Components Analysis (PCA). However, in some situations like object displacement learning or face learning, this linear technique may be ill-adapted and nonlinear approximation has to be introduced. The method we propose can be seen as a Non Linear PCA (NLPCA), the main difficulty being that the data are not ordered. We propose an index which favors the choice of axes preserving the closest point neighborhoods. These axes determine an order for visiting all the points when smoothing. Finally, a new criterion, called "generalization error," is introduced to determine the smoothing rate, that is, the knot number for the spline fitting. Experimental results conclude this paper: The method is tested on artificial data and on two data bases used in visual learning. Index Terms - Data analysis, example-based analysis and synthesis, visual learning, face representation, principal components analysis, nonlinear PCA models, dimensionality reduction, multidimensional scaling, projection pursuit, eigenfeatures.