Many computer vision problems can be considered to consist of two main tasks: the extraction of image content descriptions and their subsequent matching. The appropriate choice of type and level of description is of course task dependent, yet it is generally accepted that the low-level or so called early vision layers in the Human Visual System are context independent. This paper concentrates on the use of low-level approaches for solving computer vision problems and discusses three inter-related aspects of this: saliency; scale selection and content description. In contrast to many previous approaches which separate these tasks, we argue that these three aspects are intrinsically related. Based on this observation, a multiscale algorithm for the selection of salient regions of an image is introduced and its application to matching type problems such as tracking, object recognition and image retrieval is demonstrated.

Figure 1: These four volume renderings utilize a fully opaque transfer function, but are segmented using the method discussed in this paper. The segmented volumes show: (a) abdominal aortic branch vessels, (b) an aortic aneurysm, (c) an aorta, and (d) peripheral blood vessels in the lung. The yellow arrows indicate the location of the user’s initial seeds that were evolved to form the presented segmentations. Segmentation of structures from measured volume data, such as anatomy in medical imaging, is a challenging data-dependent task. In this paper, we present a segmentation method that leverages the parallel processing capabilities of modern programmable graphics hardware in order to run significantly faster than previous methods. In addition, collocating the algorithm computation with the visualization on the graphics hardware circumvents the need to transfer data across the system bus, allowing for faster visualization and interaction. This algorithm is unique in that it utilizes sophisticated graphics hardware functionality (i.e., floating point precision, render to texture, computational masking, and fragment programs) to enable fast segmentation and interactive visualization.

Locally parallel dense patterns- sometimes called texture flows- define a perceptually coherent structure of particular significance to perceptual organization. We argue that with applications ranging from image segmentation and edge classification to shading analysis and shape interpretation, texture flows deserve attention equal to edge segment grouping and curve completion. This paper develops the notion of texture flow from a geometrical point of view to argue that local measurements of such structures must incorporate two curvatures. We show how basic theoretical considerations lead to a unique model for the local behavior of the flow and to a notion of texture flow “good continuation”. This, in turn, translates to a specification of consistency constraints between nearby flow measurements which we use for the computation of globally (piecewise) coherent struc-ture through the contextual framework of relaxation labeling. We demonstrate the results on synthetic and natural images.

In this paper we address the topics of scale-space and phase-based image processing in a unifying framework. In contrast to the common opinion, the Gaussian kernel is not the unique choice for a linear scale-space. Instead, we chose the Poisson kernel since it is closely related to the monogenic signal, a 2D generalization of the analytic signal, where the Riesz transform replaces the Hilbert transform. The Riesz transform itself yields the flux of the Poisson scalespace and the combination of flux and scale-space, the monogenic scale-space, provides the local features attenuation and phase-vector in scale-space. Under certain assumptions, the latter two again form a monogenic scale-space which gives deeper insight to low-level image processing. In particular, we discuss edge detection by a new approach to phase congruency and its relation to amplitude based methods, reconstruction from local amplitude and local phase, and the evaluation of the local frequency.

Multi-subject non-rigid registration algorithms using dense deformation fields often encounter cases where the transformation to be estimated has a large spatial variability. In these cases, linear stationary regularization methods are not su#cient. In this paper, we present an algorithm that uses a priori information about the nature of imaged objects in order to adapt the regularization of the deformations. We also present a robustness improvement that gives higher weight to those points in images that contain more information. Finally, a fast parallel implementation using networked personal computers is presented. In order to improve the usability of the parallel software by a clinical user, we have implemented it as a grid service that can be controlled by a graphics workstation embedded in the clinical environment. Results on inter-subject pairs of images show that our method can take into account the large variability of most brain structures. The registration time for images 124 is 5 minutes on 15 standard PCs. A comparison of our non-stationary visco-elastic smoothing versus solely elastic or fluid regularizations shows that our algorithm converges faster towards a more optimal solution in terms of accuracy and transformation regularity.

This paper presents an automatic approach for segmentation of the liver from computer tomography (CT) images based on a 3D statistical shape model. Segmentation of the liver is an important prerequisite in liver surgery planning. One of the major challenges in building a 3D shape model from a training set of segmented instances of an object is the determination of the correspondence between different surfaces. We propose to use a geometric approach that is based on minimizing the distortion of the correspondence mapping between two different surfaces. For the adaption of the shape model to the image data a profile model based on the grey value appearance of the liver and its surrounding tissues in contrast enhanced CT data was developed. The robustness of this method results from a previous nonlinear diffusion filtering of the image data. Special focus is turned to the quantitative evaluation of the segmentation process. Several

In magnetic resonance diffusion tensor imaging (DTI), the direction and magnitude of diffusion of water molecules is characterized by a diffusion tensor. In the central nervous system, the highly organized fibre structure of white matter fibre tracts causes the diffusion to be anisotropic. From the DTI data, one can calculate a vector field representing the preferred direction of diffusion at each imaging voxel, which corresponds to the orientation of white matter fibres. However, the reconstruction of continuous fibre tracts from such data remains a challenge because the measurements are dense and typically quite noisy. In this paper we introduce a geometric flow to address this problem. The key ideas are: 1) to locally extend the vector field in its orthogonal plane and 2) to model the fibres as very thin tubes, by introducing a constraint on the minimum cross-sectional curvature. We illustrate the approach with reconstructions of both simulated and real diffusion tensor images. 1.

Optical Flow estimation in noisy image sequences requires a special denoising strategy. Towards this end we introduce a new tensor-driven anisotropic diffusion scheme which is designed to enhance optical-flow-like spatiotemporal structures. This is achieved by selecting diffusivities in a special manner depending on the eigenvalues of the well known structure tensor. We illustrate how the proposed choice differs from edge- and coherence-enhancing anisotropic diffusion. Furthermore we extend a recently discovered discretization scheme for anisotropic diffusion to 3D data. An automatic stop criterion to terminate the diffusion after a suitable time is given. The performance of the introduced method is examined quantitatively using image sequences with a substantial amount of noise added. 1.

. Image dnoising and segmentation are fundamental problems in the field of image processing and computer vision with numerous applications. In this paper we present a novel nonlinear diffusion model augmented with reactive terms that yields quality denoising and segmentation results on a variety of images. We present a proof for the existence, uniqueness and stability of the viscosity solution of this PDE-based model. To achieve a faster implementation, we embed the the model in a scale space and the solution is achieved via a dynamic system governed by a coupled system of first order differential equations. The dynamic system finds the solution at a coarse scale and tracks it continuously to a desired fine scale. We implement this scale-space tracking using a multigrid technique and demonstrate the smoothing and segmentation results on several images. Key words. Nonlinear Diffusion, Image Processing, Segmentation, PDEs, Scale-space 1. Introduction. Image denoising and segmentation ...

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