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

Robust ellipse detection with Gaussian mixture models

The Euclidian distance between Gaussian Mixtures has been shown to be robust to perform point set registration (Jian and Vemuri, 2011). We propose to extend this idea for robustly matching a family of shapes (ellipses). Optimisation is performed with an annealing strategy, and the search for occurrences is repeated several times to detect multiple instances of the shape of interest. We compare experimentally our approach to other state-of-the-art techniques on a benchmark database for ellipses, and demonstrate the good performance of our approach

Contributions to Medical Image Segmentation and Signal Analysis Utilizing Model Selection Methods

This thesis presents contributions to model selection techniques, especially based on information theoretic criteria, with the goal of solving problems appearing in signal analysis and in medical image representation, segmentation, and compression.The field of medical image segmentation is wide and is quickly developing to make use of higher available computational power. This thesis concentrates on several applications that allow the utilization of parametric models for image and signal representation. One important application is cell nuclei segmentation from histological images. We model nuclei contours by ellipses and thus the complicated problem of separating overlapping nuclei can be rephrased as a model selection problem, where the number of nuclei, their shapes, and their locations define one segmentation. In this thesis, we present methods for model selection in this parametric setting, where the intuitive algorithms are combined with more principled ones, namely those based on the minimum description length (MDL) principle. The results of the introduced unsupervised segmentation algorithm are compared with human subject segmentations, and are also evaluated with the help of a pathology expert.Another considered medical image application is lossless compression. The objective has been to add the task of image segmentation to that of image compression such that the image regions can be transmitted separately, depending on the region of interest for diagnosis. The experiments performed on retinal color images show that our modeling, in which the MDL criterion selects the structure of the linear predictive models, outperforms publicly available image compressors such as the lossless version of JPEG 2000.For time series modeling, the thesis presents an algorithm which allows detection of changes in time series signals. The algorithm is based on one of the most recent implementations of the MDL principle, the sequentially normalized maximum likelihood (SNML) models.This thesis produces contributions in the form of new methods and algorithms, where the simplicity of information theoretic principles are combined with a rather complex and problem dependent modeling formulation, resulting in both heuristically motivated and principled algorithmic solutions

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