Cut-sky orthogonal mode analyses of the COBE-DMR 53 and 90 GHz sky maps are used to determine the normalization of a variety of open cosmogonical models based on the cold dark matter scenario. To constrain the allowed cosmological-parameter range for these open cosmogonies, the predictions of the DMR-normalized models are compared to various observational measures of cosmography and large-scale...

We present a novel approach for solving the text segmentation problem in natural scene images. The proposed algorithm uses the normalized graph cut(Ncut) as the measure for spectral clustering, and the weighted matrices used in evaluating the graph cuts are based on the gray levels of an image, rather than the commonly used image pixels. Thus, the proposed algorithm requires much smaller spatia...

In this paper, we introduce a novel algorithm for segmentation of imperfect boundary probability maps (BPM) in connectomics. Our algorithm can be a considered as an extension of spectral clustering. Instead of clustering the diffusion maps with traditional clustering algorithms, we learn the manifold and compute an estimate of the minimum normalized cut. We proceed by divide and conquer. We als...

A novel algorithm for automatic foreground extraction based on difference of Gaussian (DoG) is presented. In our algorithm, DoG is employed to find the candidate keypoints of an input image in different color layers. Then, a keypoints filter algorithm is proposed to get the keypoints by removing the pseudo-keypoints and rebuilding the important keypoints. Finally, Normalized cut (Ncut) is used ...

We built a graph clustering system to analyze the different resulting clustering from Amazon’s product reviews from the dataset on SNAP. There are over 53MB of reviews, but we started from the graph constructed from the Jewelry set containing 60,000 nodes. Our focus for this graph clustering was the clustering product to ascertain whether the clusters were characterized by individual traits. We...

We consider three distinct and well studied problems concerning network structure: community detection by modularity maximization, community detection by statistical inference, and normalized-cut graph partitioning. Each of these problems can be tackled using spectral algorithms that make use of the eigenvectors of matrix representations of the network. We show that with certain choices of the ...

We present a Markov part-of-speech tagger for which the P (w|t) emission probabilities of word w given tag t are replaced by a linear interpolation of tag emission probabilities given a list of representations of w. As word representations, string suffixes of w are cut off at the local maxima of the Normalized Backward Successor Variety. This procedure allows for the derivation of linguisticall...

We regard histogram density estimation as a model selection problem. Our approach is based on the information-theoretic minimum description length (MDL) principle. MDLbased model selection is formalized via the normalized maximum likelihood (NML) distribution, which has several desirable optimality properties. We show how this approach can be applied for learning generic, irregular (variable-wi...

Region-based image segmentation techniques make use of similarity in intensity, color and texture to determine the partitioning of an image. The powerful cue of contour continuity is not exploited at all. In this paper, we provide a way of incorporating curvilinear grouping into region-based image segmentation. Soft contour information is obtained through orientation energy. Weak contrast gaps ...

In this paper we propose an image segmentation algorithm that combines region merging with spectral-based techniques. An initial partitioning of the image into primitive regions is produced by applying a region merging approach which produces a chunk graph that takes in attention the image gradient magnitude. This initial partition is the input to a computationally efficient region segmentation...