Cette thèse porte sur différentes problématiques liées à la détection, l'ajustement et l'identification de structures elliptiques en images. Nous plaçons la détection de primitives géométriques dans le cadre statistique des méthodes a contrario afin d'obtenir un détecteur de segments de droites et d'arcs circulaires/elliptiques sans paramètres et capable de contrôler le nombre de fausses détections. Pour améliorer la précision des primitives détectées, une technique analytique simple d'ajustement de coniques est proposée ; elle combine la distance algébrique et l'orientation du gradient. L'identification d'une configuration de cercles coplanaires en images par une signature discriminante demande normalement la rectification Euclidienne du plan contenant les cercles. Nous proposons une technique efficace de calcul de la signature qui s'affranchit de l'étape de rectification ; elle est fondée exclusivement sur des propriétés invariantes du plan projectif, devenant elle même projectivement invariante / This thesis deals with different aspects concerning the detection, fitting, and identification of elliptical features in digital images. We put the geometric feature detection in the a contrario statistical framework in order to obtain a combined parameter-free line segment, circular/elliptical arc detector, which controls the number of false detections. To improve the accuracy of the detected features, especially in cases of occluded circles/ellipses, a simple closed-form technique for conic fitting is introduced, which merges efficiently the algebraic distance with the gradient orientation. Identifying a configuration of coplanar circles in images through a discriminant signature usually requires the Euclidean reconstruction of the plane containing the circles. We propose an efficient signature computation method that bypasses the Euclidean reconstruction; it relies exclusively on invariant properties of the projective plane, being thus itself invariant under perspective

Steered Response Power based algorithms are widely used for finding sound source location using microphone array systems. SRCP-PHAT is one such algorithm that has a robust performance under noisy and reverberant conditions. The algorithm creates a likelihood function over the field of view. This thesis employs image processing methods on SRCP-PHAT images, to exploit the difference in power levels and pixel patterns to discriminate between sound source and background pixels. Hough Transform based ellipse detection is used to identify the sound source locations by finding the centers of elliptical edge pixel regions typical of source patterns. Monte Carlo simulations of an eight microphone perimeter array with single and multiple sound sources are used to simulate the test environment and area under receiver operating characteristic (ROCA) curve is used to analyze the algorithm performance. Performance was compared to a simpler algorithm involving Canny edge detection and image averaging and an algorithms based simply on the magnitude of local maxima in the SRCP image. Analysis shows that Canny edge detection based method performed better in the presence of coherent noise sources.

In this study, it is aimed to improve a set of image processing techniques used in a previously developed method for reconstructing 3D parameters of a secondary passive target using single camera images. This 3D reconstruction method was developed and implemented on a setup consisting of a digital camera, a computer, and a positioning unit. Some automatic target recognition techniques were also included in the method. The passive secondary target used is a circle with two internal spots. In order to achieve a real time target detection, the existing binarization, edge detection, and ellipse detection algorithms are debugged, modified, or replaced to increase the speed, to eliminate the run time errors, and to become compatible for target tracking. The overall speed of 20 Hz is achieved for 640x480 pixel resolution 8 bit grayscale images on a 2.8 GHz computer A novel target tracking method with various tracking strategies is introduced to reduce the search area for target detection and to achieve a detection and reconstruction speed at the maximum frame rate of the hardware. Based on the previously suggested lens distortion model, distortion measurement, distortion parameters determination, and distortion correction methods for both radial and tangential distortions are developed. By the implementation of this distortion correction method, the accuracy of the 3D reconstruction method is enhanced. The overall 3D reconstruction method is implemented in an integrated software and hardware environment as a combination of the methods with the best performance among their alternatives. This autonomous and real time system is able to detect the secondary passive target and reconstruct its 3D configuration parameters at a rate of 25 Hz. Even for extreme conditions, in which it is difficult or impossible to detect the target, no runtime failures are observed.

The thesis introduces methods used for an ellipse detection. Each method is theoretically described in current subsection. The description includes methods like Hough transform, Random Hough transform, RANSAC, Genetic Algorithm and improvements with optimalization. Further there are described modifications of current procedures in the thesis to reach better results. Next to the last chapter represents testing parameters of speed, quality and accuracy of implemented algorithms. There is a conclusion of testing and a result discussion at the end.