The dynamic generalized Hough transform: its relationship to the probabilistic Hough transforms and an application to the concurrent detection of circles and ellipses
TL;DR: The dynamic generalized Hough transform (DGHT) provides an efficient feedback mechanism linking the accumulated boundary point evidence and the contributing boundary point data and introduces the opportunity for parallel calculation and accumulation of parameters.
Abstract: Parametric transformation is a powerful tool in shape analysis. Major shortcomings of the technique are excessive storage requirements and computational complexity. Using a standard Hough transform (SHT) algorithm, each image point votes independently for all instances of the shape under detection on which it may lie. In this way a great redundancy of evidence concerning the image is generated. A new type of Hough-like techniques have evolved, the probabilistic Hough transforms (PHTs). These attempt to reduce the generation of redundant information by sampling the image data in various ways. The dynamic generalized Hough transform (DGHT) belongs to this family. It differs from other SHT and PHT algorithms in two fundamental ways. The first difference is that the algorithm selects a single connected point, ( x c , y c ), and uses this point to seed the transformation. The n parameters associated with the shape under detection are calculated using ( x c , y c ) together with sets of ( n − 1) randomly sampled image points. In this way voting is constrained to be on the hypersurface in transform space which would be generated by the standard transformation of ( x c , y c ). The algorithm maps each set of n image points to a single point on this surface. Voting is further restricted by appropriate segmentation of the image data. The second fundamental difference exploits the production of a sparse transform space by projecting the results of the transformation onto the axes of the n -dimensional transform space. Hence if T is the resolution in transform space and n is the number of parameters under detection then use of the DGHT reduces memory requirements from T n to nT and introduces the opportunity for parallel calculation and accumulation of parameters. Essential to the efficient use of the probabilistic techniques are stopping criteria which ensure adequate sampling with respect to the desired detection result and which also give optimum computational savings. A robust stopping criterion is deduced for the DGHT. This is applied to the concurrent detection of circles and ellipses using real image data over a range and variety of noise conditions. It is shown that the DGHT copes well with both occlusion and the effects of correlated noise. In addition, the method provides an efficient feedback mechanism linking the accumulated boundary point evidence and the contributing boundary point data. It achieves this goal automatically with an intelligent monitoring of the transformation.
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20 Apr 2009
TL;DR: This book and the accompanying website, focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications.
Abstract: The detection and recognition of objects in images is a key research topic in the computer vision community Within this area, face recognition and interpretation has attracted increasing attention owing to the possibility of unveiling human perception mechanisms, and for the development of practical biometric systems This book and the accompanying website, focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications Using examples from face processing tasks throughout the book to illustrate more general object recognition approaches, Roberto Brunelli: examines the basics of digital image formation, highlighting points critical to the task of template matching; presents basic and advanced template matching techniques, targeting grey-level images, shapes and point sets; discusses recent pattern classification paradigms from a template matching perspective; illustrates the development of a real face recognition system; explores the use of advanced computer graphics techniques in the development of computer vision algorithms Template Matching Techniques in Computer Vision is primarily aimed at practitioners working on the development of systems for effective object recognition such as biometrics, robot navigation, multimedia retrieval and landmark detection It is also of interest to graduate students undertaking studies in these areas
721 citations
TL;DR: The progressive probabilistic Hough transform minimizes the amount of computation needed to detect lines by exploiting the difference in the fraction of votes needed to reliably detect lines with different numbers of supporting points.
604 citations
Cites background from "The dynamic generalized Hough trans..."
...The PPHT does not rely on particular aspects of the input data structure such as connectivity (unlike [9]), though it borrows the idea of removing labelled structures from the input data....
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01 Jan 1997
TL;DR: An algorithm for the detection of ellipse shapes in images, using the Randomized Hough Transform is described, found to give improvements in accuracy, and a reduction in computation time and the number of false alarms detected.
Abstract: We describe an algorithm for the detection of ellipse shapes in images, using the Randomized Hough Transform. The algorithm is compared to a standard implementation of the Hough Transform, and the Probabilistic Hough Transform. Tests are performed using both noise-free and noisy images, and several real-world images. The algorithm was found to give improvements in accuracy, and a reduction in computation time, memory requirements and the number of false alarms detected.
266 citations
TL;DR: In this paper, the authors describe an algorithm for the detection of ellipse shapes in images, using the Randomized Hough Transform (RHT) and compare it with three other Hough-based algorithms.
252 citations
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TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
Abstract: This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.
28,073 citations
TL;DR: It is shown how the boundaries of an arbitrary non-analytic shape can be used to construct a mapping between image space and Hough transform space, which makes the generalized Houghtransform a kind of universal transform which can beused to find arbitrarily complex shapes.
4,310 citations
TL;DR: This work proposes a new method for curve detection that has the advantages of small storage, high speed, infinite parameter space and arbitrarily high resolution, and the preliminary experiments have shown that the new method is quite effective.
1,080 citations
01 May 1979
TL;DR: Theoretical and experimental comparisons of edge detectors are presented and quantitative design and performance evaluation techniques developed are used to optimally design a variety of small and large mask edge detectors.
Abstract: Quantitative design and performance evaluation techniques are developed for the enhancement/thresholding class of image edge detectors. The design techniques are based on statistical detection theory and deterministic pattern-recognition classification procedures. The performance evaluation methods developed include: a)deterministic measurement of the edge gradient amplitude; b)comparison of the probabilities of correct and false edge detection; and c) figure of merit computation. The design techniques developed are used to optimally design a variety of small and large mask edge detectors. Theoretical and experimental comparisons of edge detectors are presented.
799 citations