Invertible Orientation Scores as an Application of Generalized Wavelet Theory,”

Abstract -Inspired by the visual system of many mammals, we consider the construction of-and reconstruction from-an orientation score of an image, via a wavelet transform corresponding to the left-regular representation of the Euclidean motion group in ‫ތ‬ 2 ( ‫ޒ‬ 2 ) and oriented wavelet ψ ∈ ‫ތ‬ 2 ( ‫ޒ‬ 2 ). Because this representation is reducible, the general wavelet reconstruction theorem does not apply. By means of reproducing kernel theory, we formulate a new and more general wavelet theory, which is applied to our specific case. As a result we can quantify the well-posedness of the reconstruction given the wavelet ψ and deal with the question of which oriented wavelet ψ is practically desirable in the sense that it both allows a stable reconstruction and a proper detection of local elongated structures. This enables image enhancement by means of left-invariant operators on orientation scores

Stability of Top-Points in Scale Space

Abstract. This paper presents an algorithm for computing stability of top-points in scale-space. The potential usefulness of top-points in scalespace has already been shown for a number of applications, such as image reconstruction and image retrieval. In order to improve results only reliable top-points should be used. The algorithm is based on perturbation theory and noise propagation

DIGITALLY RECONSTRUCTED RADIOGRAPHS USING THE GRAPHICS HARDWARE

Abstract A Digitally Reconstructed Radiograph (DRR) is an Xray -like representation of a CT dataset. Since the Hounsfield values in a CT dataset are obtained using X-ray radiation, it is possible to reconstruct such a DRR, from any arbitrary viewing angle, including angles that were not included in the original CT scans. DRRs are being used in many intensity based 2D-3D registration algorithms, as well as in the planning phase of radiotherapy. We present an approach that is both very fast and accurate, harvesting the parallel processing power of today's mainstream graphics hardware. Especially we use the high precision z-buffer as intermediate buffer, in order to produce accurate results

Conservative Image Transformations With Restoration And Scale-Space Properties

Many image processing applications require to solve problems such as denoising with edge enhancement, preprocessing for segmentation, or the completion of interrupted lines. This may be accomplished by applying a suitable nonlinear anisotropic diffusion process to the image. Its diffusion tensor is adapted to the differential structure of the underlying image. Although being image enhancement tools, filters of this class are well-posed. The temporal evolution of the diffusion process creates a scale-space whose causality properties can be understood in a deterministic, stochastic, information theory based and Fourier based way. The well-posedness and scale-space results carry over to the discrete setting, which gives rise to reliable algorithms preserving all properties of the continuous framework. c fl1997 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective work..

Enkelte spørsmål om merverdiavgift ved eksport av varer til Norge

Textures show multi-scale properties and hence multiresolution techniques are considered appropriate for texture classification. Recently, the authors proposed a multiresolution texture classification system based on scale space theory and combined classifiers. However, the use of multiresolution techniques increases the computational load and memory space required. Sub-sampling can help to reduce these side effects of multiresolution techniques. However, it may degrade the overall performance of the classification system. In this paper the effect of sub-sampling is investigated in scale space texture classification using combined classifiers. It is shown that sub-sampling can help to reduce both computational load and memory space required without compromising the performance of the system

Template matching via densities on the roto-translation group

We propose a template matching method for the detection of 2D image objects that are characterized by orientation patterns. Our method is based on data representations via orientation scores, which are functions on the space of positions and orientations, and which are obtained via a wavelet-type transform. This new representation allows us to detect orientation patterns in an intuitive and direct way, namely via cross-correlations. Additionally, we propose a generalized linear regression framework for the construction of suitable templates using smoothing splines. Here, it is important to recognize a curved geometry on the position-orientation domain, which we identify with the Lie group SE(2): The roto-translation group. Templates are then optimized in a B-spline basis, and smoothness is defined with respect to the curved geometry. We achieve state-of-the-art results on three different applications: Detection of the optic nerve head in the retina (99.83 percent success rate on 1,737 images), of the fovea in the retina (99.32 percent success rate on 1,616 images), and of the pupil in regular camera images (95.86 percent on 1,521 images). The high performance is due to inclusion of both intensity and orientation features with effective geometric priors in the template matching. Moreover, our method is fast due to a cross-correlation based matching approach

Feature coding with a statistically independent cortical representation

Current models of primary visual cortex (V1) include a linear filtering stage followed by a gain control mechanism that explains some of the nonlinear behavior of neurons. The nonlinear stage has been modeled as a divisive normalization in which each input linear response is squared and then divided by a weighted sum of squared linear responses in a certain neighborhood. In this communication, we show that such a scheme permits an efficient coding of natural image features. In our case, the linear stage is implemented as a four-level Daubechies decomposition, and the nonlinear normalization parameters are determined from the statistics of natural images under the hypothesis that sensory systems are adapted to signals to which they are exposed. In particular, we fix the weights of the divisive normalization to the mutual information of the corresponding pair of linear coefficients. This nonlinear process extracts significant, statistically independent, visual events in the image. © Springer-Verlag Berlin Heidelberg 2003.This research was supported by the Spanish Commission for Research and Technology (CICYT) under grant DPI2002-04370-C02-02.Peer Reviewe