Scale spaces on Lie groups

In the standard scale space approach one obtains a scale space representation u:R of an image by means of an evolution equation on the additive group (R d ,¿+¿). However, it is common to apply a wavelet transform (constructed via a representation of a Lie-group G and admissible wavelet ¿) to an image which provides a detailed overview of the group structure in an image. The result of such a wavelet transform provides a function on a group G (rather than (R d ,¿+¿)), which we call a score. Since the wavelet transform is unitary we have stable reconstruction by its adjoint. This allows us to link operators on images to operators on scores in a robust way. To ensure -invariance of the corresponding operator on the image the operator on the wavelet transform must be left-invariant. Therefore we focus on left-invariant evolution equations (and their resolvents) on the Lie-group G generated by a quadratic form Q on left invariant vector fields. These evolution equations correspond to stochastic processes on G and their solution is given by a group convolution with the corresponding Green’s function, for which we present an explicit derivation in two particular image analysis applications. In this article we describe a general approach how the concept of scale space can be extended by replacing the additive group R d by a Lie-group with more structure. The Dutch Organization for Scientific Research is gratefully acknowledged for financial support This article provides the theory and general framework we applied in [9],[5],[8]

Amoeba Techniques for Shape and Texture Analysis

Morphological amoebas are image-adaptive structuring elements for morphological and other local image filters introduced by Lerallut et al. Their construction is based on combining spatial distance with contrast information into an image-dependent metric. Amoeba filters show interesting parallels to image filtering methods based on partial differential equations (PDEs), which can be confirmed by asymptotic equivalence results. In computing amoebas, graph structures are generated that hold information about local image texture. This paper reviews and summarises the work of the author and his coauthors on morphological amoebas, particularly their relations to PDE filters and texture analysis. It presents some extensions and points out directions for future investigation on the subject.Comment: 38 pages, 19 figures v2: minor corrections and rephrasing, Section 5 (pre-smoothing) extende

Mathematical morphology on tensor data using the Loewner ordering

The notions of maximum and minimum are the key to the powerful tools of greyscale morphology. Unfortunately these notions do not carry over directly to tensor-valued data. Based upon the Loewner ordering for symmetric matrices this paper extends the maximum and minimum operation to the tensor-valued setting. This provides the ground to establish matrix-valued analogues of the basic morphological operations ranging from erosion/dilation to top hats. In contrast to former attempts to develop a morphological machinery for matrices, the novel definitions of maximal/minimal matrices depend continuously on the input data, a property crucial for the construction of morphological derivatives such as the Beucher gradient or a morphological Laplacian. These definitions are rotationally invariant and preserve positive semidefiniteness of matrix fields as they are encountered in DT-MRI data. The morphological operations resulting from a component-wise maximum/minimum of the matrix channels disregarding their strong correlation fail to be rotational invariant. Experiments on DT-MRI images as well as on indefinite matrix data illustrate the properties and performance of our morphological operators

A Unified Approach to the Processing of Hyperspectral Images

Since vector fields, such as RGB-color, multispectral or hyperspectral images, possess only limited algebraic and ordering structures they do not lend themselves easily to image processing methods. However, for fields of symmetric matrices a sufficiently elaborate calculus, that includes, for example, suitable notions of multiplication, supremum/infimum and concatenation with real functions, is available. In this article a vector field is coded as a matrix field, which is then processed by means of the matrix valued counterparts of image processing methods. An approximate decoding step transforms a processed matrix field back into a vector field. Here we focus on proposing suitable notions of a pseudo-supremum/infimum of two vectors/colors and a PDE-based dilation/erosion process of color images as a proof-of-concept. In principle there is no restriction on the dimension of the vectors considered. Experiments, mainly on RGB-images for presentation reasons, will reveal the merits and the shortcomings of the proposed methods

Flexible Segmentation and Smoothing of DT-MRI Fields Through a Customizable Structure Tensor

We present a novel structure tensor for matrix-valued images. It allows for user defined parameters that add flexibility to a number of image processing algorithms for the segmentation and smoothing of tensor fields. We provide a thorough theoretical derivation of the new structure tensor, including a proof of the equivalence of its unweighted version to the existing structure tensor from the literature. Finally, we demonstrate its advantages for segmentation and smoothing, both on synthetic tensor fields and on real DT-MRI data