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In this paper we describe a new technique for general purpose interactive segmentation of N-dimensional images. The user marks certain pixels as “object” or “background” to provide hard constraints for segmentation. Additional soft constraints incorporate both boundary and region information. Graph

This paper addresses the problem of segmenting an image into regions. We define a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image. We then develop an efficient segmentation algorithm based on this predicate, and show

This paper describes a segment (e.g. phoneme) boundary estimation method based on recurrent neural networks (RNNs). The proposed method only requires acoustic observations to accurately estimate segment boundaries. Experimental results show that the proposed method can estimate segment boundaries

Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech tagging, text segmentation and information extraction. In these cases, the observations are usually modeled

We present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition. This algorithm is derived by minimizing a generalized Bayes/MDL criterion using the variational principle. The algorithm is guaranteed to converge to a local minimum

, natural images, ground truth segmentation data set, boundary detection, boundary localization. 1

We propose a new multiphase level set framework for image segmentation using the Mumford and Shah model, for piecewise constant and piecewise smooth optimal approximations. The proposed method is also a generalization of an active contour model without edges based 2-phase segmentation, developed

Several methods have been previously used to approximate free boundaries in tinitedifference numerical simulations. A simple, but powerful, method is described that is based on the concept of a fractional volume of fluid (VOF). This method is shown to be more flexible and efftcient than other

exist to measure an algorithm’s accuracy against these rich structural annotations. In this work, we propose a method to evaluate structural boundary detec-tion with hierarchical annotations. The proposed method transforms boundary detection into a ranking problem, and facilitates the comparison of both