Efficient representation of visual information lies at the foundation of many image processing tasks, including compression, filtering, and feature extraction. Efficiency of a representation refers to the ability to capture significant information of an object of interest in a small description. For practical applications, this representation has to be realized by structured transforms and fast algorithms. Recently, it has become evident that commonly used separable transforms (such as wavelets) are not necessarily best suited for images. Thus, there is a strong motivation to search for more powerful schemes that can capture the intrinsic geometrical structure of pictorial information. This thesis focuses on the development of new “true ” two-dimensional representations for images. The emphasis is on the discrete framework that can lead to algorithmic implementations. The first method constructs multiresolution, local and directional image expansions by using non-separable filter banks. This discrete transform is developed in connection with the continuous-space

Motivated by Chernoff's bound on asymptotic probability of error we propose the alpha-divergence measure and a surrogate, the alpha-Jensen difference, for feature classification, indexing and retrieval in image and other databases. The alpha-

A new statistical model for characterizing texture images based on wavelet-domain hidden Markov models and steerable pyramids is presented. The new model is shown to capture well both the subband marginal distributions and the dependencies across scales and orientations of the wavelet descriptors. Once it is trained for an input texture image, the model can be easily steered to characterize that texture at any other orientation. After a diagonalization operation, one obtains a rotation-invariant model of the texture image. The effectiveness of the new texture models are demonstrated in retrieval experiments with large image databases, where significant performance gains are shown. Keywords texture characterization, image retrieval, rotation invariance, wavelets, hidden Markov models, steerable pyramids. Corresponding author. Address: see above; Phone: +41 21 693 7663; Fax: +41 21 693 4312. y Also with Department of EECS, UC Berkeley, Berkeley CA 94720, USA. April 23, 2001 DRAFT I.

In this paper we present a new perspective on the problem of complexity in software, using sound mathematical concepts from information theory such as Shannon's Entropy [31]. We study the complexity of the development process by examining the logs of the source control repository for large software projects. We hypothesize that the process of developing code is a good indicator of the current and future problems in the code and the project. A complex process will have negative affects on its outcome, such as producing a complex system or delaying releases. We validate our work by studying the evolution of six large open source projects (three operating systems, a window manager, an office productivity suite, and a database).

As large software systems evolve, controlling their complexity is a major challenge for many companies, as they strive to deliver future releases on time and within budget. Several source code based metrics have been proposed to assist in determining the complexity of code to help control development costs and outcome. In this

breast images

The description of 3D shapes with features that possess descriptive power is one of the most challenging issues in content based 3D model retrieval. In this paper we propose the usage of spherical wavelet transform as a tool for the analysis of 3D shapes represented by functions on the unit sphere. We introduce three new shape descriptors extracted from the spherical wavelet coefficients, namely: (1) a subset of the spherical wavelet coefficients, (2) the L1 and, (3) the L2 energies of the spherical wavelet sub-bands. The advantage of this tool is three fold: First, it filters out small shape details which hamper the retrieval performance. Second, it takes into account feature localization and local orientations. Third, it allows shape matching at different resolutions. Spherical wavelet descriptors are natural extension of 3D Zernike moments and spherical harmonics. We evaluate, on the Princeton Shape Benchmark, the proposed descriptors regarding computational aspects and shape retrieval performance.

A new statistical model for characterizing texture images based on wavelet-domain hidden Markov models and steerable pyramids is presented. The new model is shown to capture well both the subband marginal distributions and the dependencies across scales and orientations of the wavelet descriptors. Once it is trained for an input texture image, the model can be easily steered to characterize that texture at any other orientations. After a diagonalization operation, one obtains a rotation-invariant description of the texture image. The effectiveness of the new model is demonstrated in large test image databases where significant gains in retrieval performance are shown. 1. INTRODUCTION With the explosive growth of multimedia databases and digital libraries, there is high demand for efficient tools which allow users to search and browse through such collections. The focus of this paper is on the use of texture information for image retrieval applications. Some of the most well-known te...

Abstract. Automatic plagiarism detection considering a reference corpus compares a suspicious text to a set of original documents in order to relate the plagiarised fragments to their potential source. Publications on this task often assume that the search space (the set of reference documents) is a narrow set where any search strategy will produce a good output in a short time. However, this is not always true. Reference corpora are often composed of a big set of original documents where a simple exhaustive search strategy becomes practically impossible. Before carrying out an exhaustive search, it is necessary to reduce the search space, represented by the documents in the reference corpus, as much as possible. Our experiments with the METER corpus show that a previous search space reduction stage, based on the Kullback-Leibler symmetric distance, reduces the search process time dramatically. Additionally, it improves the Precision and Recall obtained by a search strategy based on the exhaustive comparison of word n-grams. 1

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