Quantization-free parameter space reduction in ellipse detection

Ellipse modeling and detection is an important task in many computer vision and pattern recognition applications. In this thesis, four Hough-based transform algorithms have been carefully selected, studied and analyzed. These techniques include the Standard Hough Transform, Probabilistic Hough Transform, Randomized Hough Transform and Directional Information for Parameter Space Decomposition. The four algorithms are analyzed and compared against each other in this study using synthetic ellipses. Objects such as noise have been introduced to distract ellipse detection in some of the synthetic ellipse images. To complete the analysis, real world images were used to test each algorithm resulting in the proposal of a new algorithm. The proposed algorithm uses the strengths from each of the analyzed algorithms. This new algorithm uses the same approach as the Directional Information for Parameter Space Decomposition to determine the ellipse center. However, in the process of collecting votes for the ellipse center, pairs of unique edge points voted for the center are also kept in an array. A minimum of two pairs of edge points are required to determine the ellipse. This significantly reduces the usual five dimensional array requirement needed in the Standard Hough Transform. We present results of the experiments with synthetic images demonstrating that the proposed method is more effective and robust to noise. Real world applications on complex real world images are also performed successfully in the experiment

On Shape-Mediated Enrolment in Ear Biometrics

Ears are a new biometric with major advantage in that they appear to maintain their shape with increased age. Any automatic biometric system needs enrolment to extract the target area from the background. In ear biometrics the inputs are often human head profile images. Furthermore ear biometrics is concerned with the effects of partial occlusion mostly caused by hair and earrings. We propose an ear enrolment algorithm based on finding the elliptical shape of the ear using a Hough Transform (HT) accruing tolerance to noise and occlusion. Robustness is improved further by enforcing some prior knowledge. We assess our enrolment on two face profile datasets; as well as synthetic occlusion

Medical image processing: applications in ophthalmology and total hip replacement

Medical imaging tools technologically supported by the recent advances in the areas of computer vision can provide systems that aid medical professionals to carry out their expert diagnostics and investigations more effectively and efficiently. Two medical application domains that can benefit by such tools are ophthalmology and Total Hip Replacement (THR). Although a literature review conducted within the research context of this thesis revealed a number of existing solutions these are either very much limited by their application scope, robustness or scope of the extensiveness of the functionality made available. Therefore this thesis focuses on initially investigating a number of requirements defined by leading experts in the respective specialisms and providing practical solutions, well supported by the theoretical advances of computer vision and pattern recognition. This thesis provides three novel algorithms/systems for use within image analysis in the areas of Ophthalmology and THR. The first approach uses Contourlet Transform to analyse and quantify corneal neovascularization. Experimental results are provided to prove that the proposed approach provides improved robustness in the presence of noise, non-uniform illumination and reflections, common problems that exist in captured corneal images. The second approach uses a colour based segmentation approach to segment, measure and analyse corneal ulcers using the HVS colour space. Literature review conducted within the research context of this thesis revealed that there is no such system available for analysis and measurement of corneal ulcers. Finally the thesis provides a robust approach towards detecting and analysing possible dislocations and misalignments in THR X-ray images. The algorithm uses localised histogram equalisation to enhance the quality of X-ray images first prior to using Hough Transforms and filtered back projections to locate and recognise key points of the THR x-ray images. These key points are then used to measure the possible presence of dislocations and misalignments. The thesis further highlights possible extensions and improvements to the proposed algorithms and systems

Ellipse detection through decomposition of circular arcs and line segments

International audienceIn this work we propose an efficient and original method for ellipse detection which relies on a recent contour representation based on arcs and line segments \cite{NguyenD11a}. The first step of such a detection is to locate ellipse candidate with a grouping process exploiting geometric properties of adjacent arcs and lines. Then, for each ellipse candidate we extract a compact and significant representation defined from the segment and arc extremities together with the arc middle points. This representation allows then a fast ellipse detection by using a simple least square technique. Finally some first comparisons with other robust approaches are proposed

Fundamental Limits of Wideband Localization - Part II: Cooperative Networks

The availability of positional information is of great importance in many commercial, governmental, and military applications. Localization is commonly accomplished through the use of radio communication between mobile devices (agents) and fixed infrastructure (anchors). However, precise determination of agent positions is a challenging task, especially in harsh environments due to radio blockage or limited anchor deployment. In these situations, cooperation among agents can significantly improve localization accuracy and reduce localization outage probabilities. A general framework of analyzing the fundamental limits of wideband localization has been developed in Part I of the paper. Here, we build on this framework and establish the fundamental limits of wideband cooperative location-aware networks. Our analysis is based on the waveforms received at the nodes, in conjunction with Fisher information inequality. We provide a geometrical interpretation of equivalent Fisher information for cooperative networks. This approach allows us to succinctly derive fundamental performance limits and their scaling behaviors, and to treat anchors and agents in a unified way from the perspective of localization accuracy. Our results yield important insights into how and when cooperation is beneficial.Comment: To appear in IEEE Transactions on Information Theor

Affine Subspace Representation for Feature Description

This paper proposes a novel Affine Subspace Representation (ASR) descriptor to deal with affine distortions induced by viewpoint changes. Unlike the traditional local descriptors such as SIFT, ASR inherently encodes local information of multi-view patches, making it robust to affine distortions while maintaining a high discriminative ability. To this end, PCA is used to represent affine-warped patches as PCA-patch vectors for its compactness and efficiency. Then according to the subspace assumption, which implies that the PCA-patch vectors of various affine-warped patches of the same keypoint can be represented by a low-dimensional linear subspace, the ASR descriptor is obtained by using a simple subspace-to-point mapping. Such a linear subspace representation could accurately capture the underlying information of a keypoint (local structure) under multiple views without sacrificing its distinctiveness. To accelerate the computation of ASR descriptor, a fast approximate algorithm is proposed by moving the most computational part (ie, warp patch under various affine transformations) to an offline training stage. Experimental results show that ASR is not only better than the state-of-the-art descriptors under various image transformations, but also performs well without a dedicated affine invariant detector when dealing with viewpoint changes.Comment: To Appear in the 2014 European Conference on Computer Visio

Complex data processing: fast wavelet analysis on the sphere

In the general context of complex data processing, this paper reviews a recent practical approach to the continuous wavelet formalism on the sphere. This formalism notably yields a correspondence principle which relates wavelets on the plane and on the sphere. Two fast algorithms are also presented for the analysis of signals on the sphere with steerable wavelets.Comment: 20 pages, 5 figures, JFAA style, paper invited to J. Fourier Anal. and Appli