Detection and identification of elliptical structure arrangements in images: theory and algorithms

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. ABSTRACT : 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

Fast and Robust Normal Estimation for Point Clouds with Sharp Features

Proceedings of the 10th Symposium of on Geometry Processing (SGP 2012), Tallinn, Estonia, July 2012.International audienceThis paper presents a new method for estimating normals on unorganized point clouds that preserves sharp fea- tures. It is based on a robust version of the Randomized Hough Transform (RHT). We consider the filled Hough transform accumulator as an image of the discrete probability distribution of possible normals. The normals we estimate corresponds to the maximum of this distribution. We use a fixed-size accumulator for speed, statistical exploration bounds for robustness, and randomized accumulators to prevent discretization effects. We also propose various sampling strategies to deal with anisotropy, as produced by laser scans due to differences of incidence. Our experiments show that our approach offers an ideal compromise between precision, speed, and robustness: it is at least as precise and noise-resistant as state-of-the-art methods that preserve sharp features, while being almost an order of magnitude faster. Besides, it can handle anisotropy with minor speed and precision losses

Active shape models with focus on overlapping problems applied to plant detection and soil pore analysis

[no abstract

Real-time visual detection and correction of automatic screw operations in dimpled light-gauge steel framing with pre-drilled pilot holes

Modular and panelized construction have been promoted and recognized globally as advanced construction techniques for residential and commercial industries alike. Light-Gauge Steel (LGS) panels have become more popular for commercial buildings and high-rise residential buildings in the last decades. When constructing such panels, for ease of manufacturing and assembling, a common practice in the construction industry is the use of dimples and pre-drilled pilot holes. Current automatic LGS machinery, however, is not designed to operate with such constraints. In this study, a real-time vision-based approach is proposed to enable current machinery to use dimpled studs with pre-drilled pilot holes. An algorithm designed for hole detection inside the dimples on LGS steel studs, based on edge detection and ellipse fitting is proposed. Finally, an adaptive approach is proposed to adjust the screw driving manipulators to ensure that the drilling operation occurs accurately, avoiding any possible damage to the LGS studs or failure of the screwing operation. The proposed algorithm is validated on a real steel assembly and a comparison is provided with other well-known algorithms for ellipse detection to demonstrate the effectiveness of the proposed method. This real-time algorithm gives real-time results for pilot hole detection and screwing location estimation within 3 mm tolerance. When compared with other well-known approaches in the literature, the proposed approach is 59% more accurate than the fastest available algorithm

Robuste und genaue Erkennung von Mid-Level-Primitiven für die 3D-Rekonstruktion in von Menschen geschaffenen Umgebungen

The detection of geometric primitives such as points, lines and arcs is a fundamental step in computer vision techniques like image analysis, pattern recognition and 3D scene reconstruction. In this thesis, we present a framework that enables a reliable detection of geometric primitives in images. The focus is on application in man-made environments, although the process is not limited to this. The method provides robust and subpixel accurate detection of points, lines and arcs, and builds up a graph describing the topological relationships between the detected features. The detection method works directly on distorted perspective and fisheye images. The additional recognition of repetitive structures in images ensures the unambiguity of the features in their local environment. We can show that our approach achieves a high localization accuracy comparable to the state-of-the-art methods and at the same time is more robust against disturbances caused by noise. In addition, our approach allows extracting more fine details in the images. The detection accuracy achieved on the real-world scenes is constantly above that achieved by the other methods. Furthermore, our process can reliably distinguish between line and arc segments. The additional topological information extracted by our method is largely consistent over several images of a scene and can therefore be a support for subsequent processing steps, such as matching and correspondence search. We show how the detection method can be integrated into a complete feature-based 3D reconstruction pipeline and present a novel reconstruction method that uses the topological relationships of the features to create a highly abstract but semantically rich 3D model of the reconstructed scenes, in which certain geometric structures can easily be detected.Die Detektion von geometrischen Primitiven wie Punkten, Linien und Bögen ist ein elementarer Verarbeitungsschritt für viele Techniken des maschinellen Sehens wie Bildanalyse, Mustererkennung und 3D-Szenenrekonstruktion. In dieser Arbeit wird eine Methode vorgestellt, die eine zuverlässige Detektion von geometrischen Primitiven in Bildern ermöglicht. Der Fokus liegt auf der Anwendung in urbanen Umgebungen, wobei der Prozess nicht darauf beschränkt ist. Die Methode ermöglicht eine robuste und subpixelgenaue Detektion von Punkten, Linien und Bögen und erstellt einen Graphen, der die topologischen Beziehungen zwischen den detektierten Merkmalen beschreibt. Die Detektionsmethode kann direkt auf verzeichnete perspektivische Bilder und Fischaugenbilder angewendet werden. Die zusätzliche Erkennung sich wiederholender Strukturen in Bildern gewährleistet die Eindeutigkeit der Merkmale in ihrer lokalen Umgebung. Das neu entwickelte Verfahren erreicht eine hohe Lokalisierungsgenauigkeit, die dem Stand der Technik entspricht und gleichzeitig robuster gegenüber Störungen durch Rauschen ist. Darüber hinaus ermöglicht das Verfahren, mehr Details in den Bildern zu extrahieren. Die Detektionsrate ist bei dem neuen Verfahren auf den realen Datensätzen stets höher als bei dem aktuellen Stand der Technik. Darüber hinaus kann das neue Verfahren zuverlässig zwischen Linien- und Bogensegmenten unterscheiden. Die durch das neue Verfahren gewonnenen zusätzlichen topologischen Informationen sind weitgehend konsistent über mehrere Bilder einer Szene und können somit eine Unterstützung für nachfolgende Verarbeitungsschritte wie Matching und Korrespondenzsuche sein. Die Detektionsmethode wird in eine vollständige merkmalsbasierte 3D-Rekonstruktionspipeline integriert und es wird eine neuartige Rekonstruktionsmethode vorgestellt, die die topologischen Beziehungen der Merkmale nutzt, um ein abstraktes, aber zugleich semantisch reichhaltiges 3D-Modell der rekonstruierten Szenen zu erstellen, in dem komplexere geometrische Strukturen leicht detektiert werden können

Weighted Trimean as a Regressor in the Estimate of Theil-Sen Regression

The most used method in nonparametric regression analysis is the Theil-Sen approach. With this method, all coefficient estimations are made with the median parameter as opposed to parametric methods. The most important criticism in computations with the median parameter is that the impact of extreme values does not participate in calculations.  In this study, it was proposed to use the trimean parameter by weighting, which more effectively adds the effect of outliers to the average account in Theil-Sen regression analysis. In applications with 5 data sets, Theil-Sen calculations with weighted trimean were found to be more successful than calculations with the median parameter. Thus, in cases where the outliers are too high or directly affect the data, it can be said that the use of weighted trimean will yield more effective results

Mathematical Morphology for Quantification in Biological & Medical Image Analysis

Mathematical morphology is an established field of image processing first introduced as an application of set and lattice theories. Originally used to characterise particle distributions, mathematical morphology has gone on to be a core tool required for such important analysis methods as skeletonisation and the watershed transform. In this thesis, I introduce a selection of new image analysis techniques based on mathematical morphology. Utilising assumptions of shape, I propose a new approach for the enhancement of vessel-like objects in images: the bowler-hat transform. Built upon morphological operations, this approach is successful at challenges such as junctions and robust against noise. The bowler-hat transform is shown to give better results than competitor methods on challenging data such as retinal/fundus imagery. Building further on morphological operations, I introduce two novel methods for particle and blob detection. The first of which is developed in the context of colocalisation, a standard biological assay, and the second, which is based on Hilbert-Edge Detection And Ranging (HEDAR), with regard to nuclei detection and counting in fluorescent microscopy. These methods are shown to produce accurate and informative results for sub-pixel and supra-pixel object counting in complex and noisy biological scenarios. I propose a new approach for the automated extraction and measurement of object thickness for intricate and complicated vessels, such as brain vascular in medical images. This pipeline depends on two key technologies: semi-automated segmentation by advanced level-set methods and automatic thickness calculation based on morphological operations. This approach is validated and results demonstrating the broad range of challenges posed by these images and the possible limitations of this pipeline are shown. This thesis represents a significant contribution to the field of image processing using mathematical morphology and the methods within are transferable to a range of complex challenges present across biomedical image analysis