Decomposition of a curve into arcs and line segments based on dominant point detection

International audienceA new solution is proposed to decompose a curve into arcs and straight line segments in $O(n\log n)$ time. It is a combined solution based on arc detection \cite{Nguyen10a_} and dominant point detection \cite{Nguyen10f} to strengthen the quality of the segmentation results. Experimental results show the fastness of the proposed method

Décomposition d'une courbe discrète en arcs de cercle et segments de droite

Session "Posters"National audienceUne nouvelle solution est proposée afin de décomposer une courbe en arcs et segments de droite en temps $O(n\log n)$. Il s'agit d'une combinaison entre une technique de segmentation en arcs et une technique de détection de points dominants pour renforcer la qualité des résultats obtenus. Elle est basée sur la notion de point isolé d'une courbe de points centraux dans espace particulier, dit espace des tangentes [Latecki00]. Un processus linéaire de balayage, appliqué pour détecter des points isolés, permet d'obtenir une représentation d'une courbe par des arcs de cercle et des segments de droite. De plus, la méthode proposée peut être combinée avec une technique non-paramétrique de polygonalisation afin de traiter des courbes bruitées sans utiliser des paramètres. Les résultats expérimentaux démontrent la robustesse de la méthode proposé

Computing global shape measures

Global shape measures are a convenient way to describe regions. They are generally simple and efficient to extract, and provide an easy means for high level tasks such as classification as well as helping direct low-level computer vision processes such as segmentation. In this chapter a large selection of global shape measures (some from the standard literature as well as other newer methods) are described and demonstrated

Computational Strategies for Object Recognition

This article reviews the available methods forautomated identification of objects in digital images. The techniques are classified into groups according to the nature of the computational strategy used. Four classes are proposed: (1) the s~mplest strategies, which work on data appropriate for feature vector classification, (2) methods that match models to symbolic data structures for situations involving reliable data and complex models, (3) approaches that fit models to the photometry and are appropriate for noisy data and simple models, and (4) combinations of these strategies, which must be adopted in complex situations Representative examples of various methods are summarized, and the classes of strategies are evaluated with respect to their appropriateness for particular applications

On Computer Stereo Vision with Wire Frame Models

Coordinated Science Laboratory changed its name from Control Systems LaboratoryShould have been numbered UILU-ENG 77-2252, and that number may have been distributed on some copies.Joint Services Electronics Program / DAAB-07-72-C-0259Ope

Automatic visual recognition using parallel machines

Invariant features and quick matching algorithms are two major concerns in the area of automatic visual recognition. The former reduces the size of an established model database, and the latter shortens the computation time. This dissertation, will discussed both line invariants under perspective projection and parallel implementation of a dynamic programming technique for shape recognition. The feasibility of using parallel machines can be demonstrated through the dramatically reduced time complexity. In this dissertation, our algorithms are implemented on the AP1000 MIMD parallel machines. For processing an object with a features, the time complexity of the proposed parallel algorithm is O(n), while that of a uniprocessor is O(n2). The two applications, one for shape matching and the other for chain-code extraction, are used in order to demonstrate the usefulness of our methods. Invariants from four general lines under perspective projection are also discussed in here. In contrast to the approach which uses the epipolar geometry, we investigate the invariants under isotropy subgroups. Theoretically speaking, two independent invariants can be found for four general lines in 3D space. In practice, we show how to obtain these two invariants from the projective images of four general lines without the need of camera calibration. A projective invariant recognition system based on a hypothesis-generation-testing scheme is run on the hypercube parallel architecture. Object recognition is achieved by matching the scene projective invariants to the model projective invariants, called transfer. Then a hypothesis-generation-testing scheme is implemented on the hypercube parallel architecture

Procedural Historic Building Information Modelling (HBIM) For Recording and Documenting European Classical Architecture

Procedural Historic Building Information Modelling (HBIM) is a new approach for modelling historic buildings which develops full building information models from remotely sensed data. HBIM consists of a novel library of reusable parametric objects, based on historic architectural data and a system for mapping these library objects to survey data. Using concepts from procedural modelling, a new set of rules and algorithms have been developed to automatically combine HBIM library objects and generate different building arrangements by altering parameters. This is a semi-automatic process where the required building structure and objects are first automatically generated and then refined to match survey data. The encoding of architectural rules and proportions into procedural modelling rules helps to reduce the amount of further manual editing that is required. The ability to transfer survey data such as building footprints or cut-sections directly into a procedural modelling rule also greatly reduces the amount of further editing required. These capabilities of procedural modelling enable a more automated and efficient overall workflow for reconstructing BIM geometry from point cloud data. This document outlines the research carried out to evaluate the suitability of a procedural modelling approach for improving the process of reconstructing building geometry from point clouds. To test this hypothesis, three procedural modelling prototypes were designed and implemented for BIM software. Quantitative accuracy testing and qualitative end-user scenario testing methods were used to evaluate the research hypothesis. The results obtained indicate that procedural modelling has potential for achieving more accurate, automated and easier generation of BIM geometry from point clouds

Coronal loop detection from solar images and extraction of salient contour groups from cluttered images.

This dissertation addresses two different problems: 1) coronal loop detection from solar images: and 2) salient contour group extraction from cluttered images. In the first part, we propose two different solutions to the coronal loop detection problem. The first solution is a block-based coronal loop mining method that detects coronal loops from solar images by dividing the solar image into fixed sized blocks, labeling the blocks as Loop or Non-Loop , extracting features from the labeled blocks, and finally training classifiers to generate learning models that can classify new image blocks. The block-based approach achieves 64% accuracy in IO-fold cross validation experiments. To improve the accuracy and scalability, we propose a contour-based coronal loop detection method that extracts contours from cluttered regions, then labels the contours as Loop and Non-Loop , and extracts geometric features from the labeled contours. The contour-based approach achieves 85% accuracy in IO-fold cross validation experiments, which is a 20% increase compared to the block-based approach. In the second part, we propose a method to extract semi-elliptical open curves from cluttered regions. Our method consists of the following steps: obtaining individual smooth contours along with their saliency measures; then starting from the most salient contour, searching for possible grouping options for each contour; and continuing the grouping until an optimum solution is reached. Our work involved the design and development of a complete system for coronal loop mining in solar images, which required the formulation of new Gestalt perceptual rules and a systematic methodology to select and combine them in a fully automated judicious manner using machine learning techniques that eliminate the need to manually set various weight and threshold values to define an effective cost function. After finding salient contour groups, we close the gaps within the contours in each group and perform B-spline fitting to obtain smooth curves. Our methods were successfully applied on cluttered solar images from TRACE and STEREO/SECCHI to discern coronal loops. Aerial road images were also used to demonstrate the applicability of our grouping techniques to other contour-types in other real applications

AutoGraff: towards a computational understanding of graffiti writing and related art forms.

The aim of this thesis is to develop a system that generates letters and pictures with a style that is immediately recognizable as graffiti art or calligraphy. The proposed system can be used similarly to, and in tight integration with, conventional computer-aided geometric design tools and can be used to generate synthetic graffiti content for urban environments in games and in movies, and to guide robotic or fabrication systems that can materialise the output of the system with physical drawing media. The thesis is divided into two main parts. The first part describes a set of stroke primitives, building blocks that can be combined to generate different designs that resemble graffiti or calligraphy. These primitives mimic the process typically used to design graffiti letters and exploit well known principles of motor control to model the way in which an artist moves when incrementally tracing stylised letter forms. The second part demonstrates how these stroke primitives can be automatically recovered from input geometry defined in vector form, such as the digitised traces of writing made by a user, or the glyph outlines in a font. This procedure converts the input geometry into a seed that can be transformed into a variety of calligraphic and graffiti stylisations, which depend on parametric variations of the strokes

Multiresolution analysis as an approach for tool path planning in NC machining

Wavelets permit multiresolution analysis of curves and surfaces. A complex curve can be decomposed using wavelet theory into lower resolution curves. The low-resolution (coarse) curves are similar to rough-cuts and high-resolution (fine) curves to finish-cuts in numerical controlled (NC) machining.;In this project, we investigate the applicability of multiresolution analysis using B-spline wavelets to NC machining of contoured 2D objects. High-resolution curves are used close to the object boundary similar to conventional offsetting, while lower resolution curves, straight lines and circular arcs are used farther away from the object boundary.;Experimental results indicate that wavelet-based multiresolution tool path planning improves machining efficiency. Tool path length is reduced, sharp corners are smoothed out thereby reducing uncut areas and larger tools can be selected for rough-cuts