Learning color receptive fields and color differential structure

\u3cp\u3eIn this paper we study the role of brain plasticity, and investigate the emergence and self-emergence of receptive fields from scalar and color natural images by principal component analysis of image patches. We describe the classical experiment on localized PCA on center-surround weighted patches of natural scalar images. The resulting set turns out to show great similarity to Gaussian spatial derivatives, and exhibits steerability behavior. We then relate the famous experiment by Blakemore of training a cat with only visual horizontal bar information with PCA analysis of images with primarily unidirectional structure. PCA is performed for patches of RGB natural color images. The resulting profiles resemble spatio-spectral operators extracting color differential structure and shape. We discuss how spatio-spectral Gaussian derivative operators along the wavelength dimension can be modeled, originally proposed by Koenderink, and based on Hering's opponent color theory. The discussion puts the PCA findings in the perspective of multi-scale Gaussian differential geometry, multi-orientation sub-Riemannian geometry, and PCA on affinity matrices for contextual models.\u3c/p\u3

The differential structure of images

The chapter focuses on modern computer vision algorithms, used to extract robust multi-scale differential features from discrete images, like corners, T-junctions, edges. The algorithms have an axiomatic basis, and are inspired by modern insights in possible functional circuits in the visual system in the human brain. Applications focus on computer-aided diagnosis and industrial vision tasks

Image Processing on Diagnostic Workstations

Medical workstations have developed into the super-assistants of radiologists. The overwhelming production of images, hardware that rapidly became cheaper and powerful 3D visualization and quantitative analysis software have all pushed the developments from simple PACS viewing into a really versatile viewing environment. This chapter gives an overview of these developments, aimed at radiologists’ readership. Many references and internet are given which discuss the topics in more depth than is possible in this short paper. This paper is necessarily incomplete

Multi-scale texture classification from generalized locally orderless images

Locally orderless images are families of three intertwined scale spaces that describe local histograms. We generalize locally orderless images by considering local histograms of a collection of filtered versions of the image, and by extending them to joint probability distributions. These constructions can be used to derive texture features and are shown to be a more general description of two established texture classification methods, viz., filter bank methods and cooccurrence matrices. Because all scale parameters are stated explicitly in this formulation, multi-resolution feature sets can be extracted in a systematic way. This includes new types of multi-resolution analysis, not only based on the spatial scale, but on the window size and intensity scale as well. Each multi-resolution approach improves texture classification performance, the best result being obtained if a multi-resolution approach for all scale parameters is used. This is demonstrated in experiments on a large data set of 1152 images for 72 texture classes