the global properties of the shape, from high frequency harmonics, that encode the details of the object’s shape. We learn the regression model using Locally Weighted Projection Regression (LWPR) which expedites online, incremental learning. After sufficient observation of a set of unoccluded shapes

to certain intermediate local minima than traditional active contours, and it is based on a well-structured Riemannian metric, which is important for shape analysis and shape priors. In this paper, we analyze Sobolev active contours using scale-space analysis in order to understand their evolution across

into the global minimum of an image-derived “energy ” function. The nonparametric energy function was derived from the statistical properties of previously segmented images, thereby incorporating prior experience. Since the method was based on a state space search for the contour with the best global properties

intermediate local minima than traditional active contours, and it is based on a well-structured Riemannian metric, which is important for shape analysis and shape priors. In this paper, we analyze Sobolev active contours using scale-space analysis in order to understand their evolution across different scales

assembly of segments of mul-tiple parts that are arranged under a hierarchy of tailored spatio-temporal constraints. In this hierarchy, segments can rescale independently while their elasticity is controlled with global orientations and local distances. While the trend in tracking is to design complex

Abstract: Online learning initia-tives over the past decade have become increasingly comprehen-sive in their selection of courses and sophisticated in their presen-tation, culminating in the recent announcement of a number of consortium and startup activities that promise to make a university

of knowledge and a book in Her lotus hands. Dedicated to the Lotus Feet of the revered Sadguru. This thesis presents a new active mask (AM) framework and an algorithm for segmenta-tion of digital images, particularly those of punctate patterns from fluorescence microscopy. Fluorescence microscopy has greatly

In this paper, we present an approach for an unsupervised learning of probabilistic deformation modes of 2D moving objects from image sequences. The object representation relies on a statistical description of global and local deformations applied to an a priori prototype shape. The optimal

Fields and global shape priors to estimate segmentations and pose simultaneously is also proposed. Some utilized pose-specific conditional random and stick figures for segmentation, as well as pose estimation of humans within a Bayesian framework, which has been success-fully used in 3-D human pose

The role of segmentation and investor recognition through the lens of cross-listing activity∗ Francesca Carrieri†, Xavier Mouchette‡, Aline Muller§ We focus on the price effects occurring around cross-listing and research the impact of the sequencing of cross-listing, defined as the cumulative