From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Simulation and the monte carlo method

Local Shannon entropy measure with statistical tests for image randomness

Contrast in image processing is usually defined as a ratio between the darkest and the brightest spots of an image. In this paper we introduce a different contrast definition. The newly introduced Global Contrast Factor (GCF) corresponds closer to the human perception of contrast. GCF uses contrasts at various resolution levels in order to compute overall contrast. Experiments were conducted in order to find weight factors needed to calculate GCF. GCF measures richness of detail as perceived by a human observer, and as such can be used in various application areas like rendering, tone mapping, volume visualization, and lighting design.

/pdf/global-contrast-factor-a-new-approach-to-image-contrast-4soayayfc0.pdf

Global contrast factor - a new approach to image contrast

http://people.hws.edu/graham/Graham_VisionRes10.pdf

Statistical regularities in art: Relations with visual coding and perception

The Birkhoff aesthetic measure of an object is the ratio between order and complexity. Informational aesthetics describes the interpretation of this measure from an information-theoretic perspective. From these ideas, the authors define a set of ratios based on information theory and Kolmogorov complexity that can help to quantify the aesthetic experience.

/pdf/informational-aesthetics-measures-3zymjrpka5.pdf

Informational Aesthetics Measures

Computational Aesthetics 2008: Categorizing art: Comparing humans and computers

In 1928, George D. Birkhoff introduced the Aesthetic Measure, defined as the ratio between order and complexity, and, in 1965, Max Bense analyzed Birkhoff's measure from an information theory point of view. In this paper, the concepts of order and complexity in an image (in our case, a painting) are analyzed in the light of Shannon entropy and Kolmogorov complexity. We also present a new vision of the creative process: the initial uncertainty, obtained from the Shannon entropy of the repertoire (palette), is transformed into algorithmic information content, defined by the Kolmogorov complexity of the image. From this perspective, the Birkhoff's Aesthetic Measure is presented as the ratio between the algorithmic reduction of uncertainty (order) and the initial uncertainty (complexity). The measures proposed are applied to several works of Mondrian, Pollock, and van Gogh.

Conceptualizing Birkhoff's aesthetic measure using Shannon entropy and Kolmogorov complexity

In this paper, we introduce a new information-theoretic approach to study the complexity of an image. The new framework we present here is based on considering the information channel that goes from the histogram to the regions of the partitioned image, maximizing the mutual information. Image complexity has been related to the entropy of the image intensity histogram. This disregards the spatial distribution of pixels, as well as the fact that a complexity measure must take into account at what level one wants to describe an object. We define the complexity by using two measures which take into account the level at which the image is considered. One is the number of partitioning regions needed to extract a given ratio of information from the image. The other is the compositional complexity given by the Jensen-Shannon divergence of the partitioned image.

/pdf/an-information-theoretic-framework-for-image-complexity-5dv1ljkv9h.pdf

An information-theoretic framework for image complexity

In image processing, segmentation algorithms constitute one of the main focuses of research. In this paper, new image segmentation algorithms based on a hard version of the information bottleneck method are presented. The objective of this method is to extract a compact representation of a variable, considered the input, with minimal loss of mutual information with respect to another variable, considered the output. First, we introduce a split-and-merge algorithm based on the definition of an information channel between a set of regions (input) of the image and the intensity histogram bins (output). From this channel, the maximization of the mutual information gain is used to optimize the image partitioning. Then, the merging process of the regions obtained in the previous phase is carried out by minimizing the loss of mutual information. From the inversion of the above channel, we also present a new histogram clustering algorithm based on the minimization of the mutual information loss, where now the input variable represents the histogram bins and the output is given by the set of regions obtained from the above split-and-merge algorithm. Finally, we introduce two new clustering algorithms which show how the information bottleneck method can be applied to the registration channel obtained when two multimodal images are correctly aligned. Different experiments on 2-D and 3-D images show the behavior of the proposed algorithms.

/pdf/image-segmentation-using-information-bottleneck-method-2fbce8bpan.pdf

Jaume Rigau

Papers

Informational Aesthetics Measures

Computational Aesthetics 2008: Categorizing art: Comparing humans and computers

Conceptualizing Birkhoff's aesthetic measure using Shannon entropy and Kolmogorov complexity

An information-theoretic framework for image complexity

Image Segmentation Using Information Bottleneck Method