2-D edge feature extraction to subpixel accuracy using the generalized energy approach

Precision edge feature extraction is a very important step in vision, Researchers mainly use step edges to model an edge at subpixel level. In this paper we describe a new technique for two dimensional edge feature extraction to subpixel accuracy using a general edge model. Using six basic edge types to model edges, the edge parameters at subpixel level are extracted by fitting a model to the image signal using least-.squared error fitting technique.<br /

Edge Detection and 3D Reconstruction Based on the Shape-from-Focus

Má práce vychází z průmyslového projektu, jehož cílem je postavit stroj pro přesnou manipulaci mikrokomponenty. Zmíněné mikrokomponenty jsou sledovány na základě hledání hran v obraze. Má práce popisuje přehled postupů používaných pro detekci hran v obraze a zároveň návrh algoritmu pro rekonstrukci povrchu mikrokomponent pomocí Shape-From-Focus v mikroskopickém prostředí. Použité obrázky byly pořízeny kamerou s telecentrickým objektivem s malou hloubkou ostrosti. Vyvinul jsem Shape-From-Focus algoritmus, který používá 3D konvoluční masku pro detekci hran a je schopný aproximovat povrchy bez struktury. Vyvinutá 3D konvoluční maska je založena na druhé derivaci obrazové funkce. V pokusech popisujících kalibraci kamery a pro opětovné zaostření optické soustavy byly použity rozličné metody pro detekci hran v obraze. V pokusech se také prezentují výsledky rekonstrukce povrchu pomocí navrženého Shape-From-Focus algoritmu.The work stems from the industrial project which aims to build the highly precise micro components assembly machine. The components are positioned via locating the edges in the image. The overview of the edge detection techniques and the design of the Shape-From-Focus algorithm in the microscopic environment are presented. The images used were captured with telecentric optics with a shallow Depth-of-Field. The Shape-From-Focus algorithm is developed together with the 3D convolutional mask and approximation of the surface in the textureless areas. The developed 3D convolutional filter is based on the seconds derivative of the image function. Various edge detection techniques are used in experiments to calibrate the camera and to refocus the optics. The experiments also show the surface reconstruction obtained by the Shape-From-Focus algorithm

Image analysis using multiscale boundary extraction algorithm

The complete analysis and interpretation of the information in image data is a complex process. This dissertation presents 3 major contributions to image analysis, namely, global multiscale detection, local scale analysis, and boundary extraction. Global scale analysis is related to identification of the various scales presented in the image. A new approach for global scale analysis is developed based on the differential power spectrum normalized variance ratio (DPSNVR). The DPSNVR is the ratio of the second order normalized central moment of the power spectrum of the image to that of the multiscale differential mask. Local maxima in DPSNVR graph directly indicate the global scales in the image. Local scale analysis performs a more detailed analysis of the edges to eliminate effects of blurring. A method based on mutilscale feature matching has been proposed. Details obtained at all scales are treated using a scale invariant normalization scheme. Besides local scale analysis, a multiscale data fusion algorithm has been implemented which leads to the new concept of multiple scale differential masks. The multiple scale differential mask generated using a range of scale values possesses the remarkable shape preservation property which makes it superior to traditional multiscale masks. Finally the complete sequential boundary extraction algorithm based on particle motion in a velocity field is presented. The boundary extraction algorithm incorporates edge localization, boundary representation, and automated selection of boundary extraction parameters. The global scale analysis techniques in conjunction with the boundary extraction algorithm provide a multiscale image segmentation algorithm

Evaluation of collision properties of spheres using high-speed video analysis

Experimental evaluation of the collision properties of spheres is performed using video image analysis techniques. A high-speed Kodak EktaPro1000 video camera is utilized to record a collision sequence between two spheres at 1000 frames/sec, and then the images are analyzed to calculate three dimensional translation and rotation before and after the collision. These quantities are used to compute the collision properties for a pair of one inch nylon spheres, i.e. the coefficient of friction, and the coefficients of normal and tangential restitution. The focus of the thesis is on image analysis techniques that provide high accuracy results even though the image resolution is very low, i.e. 240x192 pixels. The procedure developed here can be extended to smaller size spheres and can also be applicable to other motion analysis expenments involving low resolution images

Machine vision techniques for inspection of dry-fibre composite preforms in the aerospace industry

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis presents the results of a three year investigation into machine vision techniques for in-process automated inspection of dry-fibre composite preforms. Efficient texture analysis based techniques have been developed, tested, and implemented in a prototype robotic assembly cell. Industrial constraints have been considered in the development of all the algorithms described. A single channel texture analysis model is described which can successfully segment images containing only a few textures. The model is based on convolution of the image with small kernels optimised for the task, and is elegant in the sense that it is computationally simple and easily realisable in low cost hardware. A new convolution kernel optimisation algorithm is described. It is demonstrated that convolution kernels can also be optimised to perform as edge operators in simple textured images. A novel boundary refinement algorithm is described which reduces the inspection errors inherent in texture based boundary estimates. The algorithm takes the form of a local search, using the texture estimate as a guiding template, and selects edge points by maximising a merit function. Optimum parameters for the merit function are obtained using multiple training images in conjunction with simple function optimisation algorithms.This study is funded by the Engineering and Physical Sciences Research Council (EPSRC) and Dowty Aerospace Propellers Ltd

Divergence Model for Measurement of Goos-Hanchen Shift

In this effort a new measurement technique for the lateral Goos-Hanchen shift is developed, analyzed, and demonstrated. The new technique uses classical image formation methods fused with modern detection and analysis methods to achieve higher levels of sensitivity than obtained with prior practice. Central to the effort is a new mathematical model of the dispersion seen at a step shadow when the Goos-Hanchen effect occurs near critical angle for total internal reflection. Image processing techniques are applied to measure the intensity distribution transfer function of a new divergence model of the Goos-Hanchen phenomena providing verification of the model. This effort includes mathematical modeling techniques, analytical derivations of governing equations, numerical verification of models and sensitivities, optical design of apparatus, image processin

A robust high-sensitivity algorithm for automated detection of proteins in two-dimensional electrophoresis gels

The automated interpretation of two-dimensional gel electrophoresis images used in protein separation and analysis presents a formidable problem in the detection and characterization of ill-defined spatial objects. We describe in this paper a hierarchical algorithm that provides a robust, high-sensitivity solution to this problem, which can be easily adapted to a variety of experimental situations. The software implementation of this algorithm functions as part of a complete package designed for general protein gel analysis applications

Modeling edges at subpixel accuracy using the local energy approach

In this paper we described new technique for 1-D and 2-D edge feature extraction to subpixel accuracy using edge models and the local energy approach. A candidate edge is modeled as one of a number of parametric edge models, and the fit is refined by a least-squared error fitting technique

Biomimetic Design for Efficient Robotic Performance in Dynamic Aquatic Environments - Survey

This manuscript is a review over the published articles on edge detection. At first, it provides theoretical background, and then reviews wide range of methods of edge detection in different categorizes. The review also studies the relationship between categories, and presents evaluations regarding to their application, performance, and implementation. It was stated that the edge detection methods structurally are a combination of image smoothing and image differentiation plus a post-processing for edge labelling. The image smoothing involves filters that reduce the noise, regularize the numerical computation, and provide a parametric representation of the image that works as a mathematical microscope to analyze it in different scales and increase the accuracy and reliability of edge detection. The image differentiation provides information of intensity transition in the image that is necessary to represent the position and strength of the edges and their orientation. The edge labelling calls for post-processing to suppress the false edges, link the dispread ones, and produce a uniform contour of objects

A new Edge Detector Based on Parametric Surface Model: Regression Surface Descriptor

In this paper we present a new methodology for edge detection in digital images. The first originality of the proposed method is to consider image content as a parametric surface. Then, an original parametric local model of this surface representing image content is proposed. The few parameters involved in the proposed model are shown to be very sensitive to discontinuities in surface which correspond to edges in image content. This naturally leads to the design of an efficient edge detector. Moreover, a thorough analysis of the proposed model also allows us to explain how these parameters can be used to obtain edge descriptors such as orientations and curvatures. In practice, the proposed methodology offers two main advantages. First, it has high customization possibilities in order to be adjusted to a wide range of different problems, from coarse to fine scale edge detection. Second, it is very robust to blurring process and additive noise. Numerical results are presented to emphasis these properties and to confirm efficiency of the proposed method through a comparative study with other edge detectors.Comment: 21 pages, 13 figures and 2 table