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198
A computational approach to edge detection
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1986
"... AbstractThis 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 ..."
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Cited by 4624 (0 self)
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AbstractThis 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 Gaussiansmoothed image. We extend this simple detector using operators of several widths to cope with different signaltonoise ratios in the image. We present a general method, called feature synthesis, for the finetocoarse 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. This detection scheme uses several elongated operators at each point, and the directional operator outputs are integrated with the gradient maximum detector. Index TermsEdge detection, feature extraction, image processing, machine vision, multiscale image analysis. I.
Edge Detection
, 1985
"... For both biological systems and machines, vision begins with a large and unwieldy array of measurements of the amount of light reflected from surfaces in the environment. The goal of vision is to recover physical properties of objects in the scene, such as the location of object boundaries and the s ..."
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Cited by 1279 (1 self)
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For both biological systems and machines, vision begins with a large and unwieldy array of measurements of the amount of light reflected from surfaces in the environment. The goal of vision is to recover physical properties of objects in the scene, such as the location of object boundaries and the structure, color and texture of object surfaces, from the twodimensional image that is projected onto the eye or camera. This goal is not achieved in a single step; vision proceeds in stages, with each stage producing increasingly more useful descriptions of the image and then the scene. The first clue about the physical properties of the scene are provided by the changes of intensity in the image. The importance of intensity changes and edges in early visual processg has led to extensive research on their detection, description and .use, both in computer and biological vision systems. This article reviews some of the theory that underlies the detection of edges, and the methods used to carry out this analysis.
Using Canny’s criteria to derive a recursively implemented optimal edge detector
 J. OF COMP. VISION
, 1987
"... A highly efficient recursive algorithm for edge detection is presented. Using Canny's design [1], we show that a solution to his precise formulation of detection and localization for an infinite extent filter leads to an optimal operator in one dimension, which can be efficiently implemented by ..."
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Cited by 288 (14 self)
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A highly efficient recursive algorithm for edge detection is presented. Using Canny's design [1], we show that a solution to his precise formulation of detection and localization for an infinite extent filter leads to an optimal operator in one dimension, which can be efficiently implemented by two recursive filters moving in opposite directions. In addition to the noise truncature immunity which results, the recursive nature of the filtering operations leads, with sequential machines, to a substantial saving in computational effort (five multiplications and five additions for one pixel, independent of the size of the neighborhood). The extension to the twodimensional case is considered and the resulting filtering structures are implemented as twodimensional recursive filters. Hence, the filter size can be varied by simply changing the value of one parameter without affecting the time execution of the algorithm. Performance measures of this new edge detector are given and compared to Canny's filters. Various experimental results are shown.
SUSAN  A New Approach to Low Level Image Processing
 International Journal of Computer Vision
, 1995
"... This paper describes a new approach to low level image processing; in particular, edge and corner detection and structure preserving noise reduction. ..."
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Cited by 267 (3 self)
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This paper describes a new approach to low level image processing; in particular, edge and corner detection and structure preserving noise reduction.
A Review of Vessel Extraction Techniques and Algorithms
 ACM Computing Surveys
, 2000
"... Vessel segmentation algorithms are the critical components of circulatory blood vessel analysis systems. We present a survey of vessel extraction techniques and algorithms. We put the various vessel extraction approaches and techniques in perspective by means of a classification of the existing r ..."
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Cited by 183 (0 self)
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Vessel segmentation algorithms are the critical components of circulatory blood vessel analysis systems. We present a survey of vessel extraction techniques and algorithms. We put the various vessel extraction approaches and techniques in perspective by means of a classification of the existing research. While we have mainly targeted the extraction of blood vessels, neurosvascular structure in particular, we have also reviewed some of the segmentation methods for the tubular objects that show similar characteristics to vessels. We have divided vessel segmentation algorithms and techniques into six main categories: (1) pattern recognition techniques, (2) modelbased approaches, (3) trackingbased approaches, (4) artificial intelligencebased approaches, (5) neural networkbased approaches, and (6) miscellaneous tubelike object detection approaches. Some of these categories are further divided into sub categories. We have also created tables to compare the papers in each category against such criteria as dimensionality, input type, preprocessing, user interaction, and result type.
Local scale control for edge detection and blur estimation
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1998
"... Abstract—The standard approach to edge detection is based on a model of edges as large step changes in intensity. This approach fails to reliably detect and localize edges in natural images where blur scale and contrast can vary over a broad range. The main problem is that the appropriate spatial sc ..."
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Cited by 165 (20 self)
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Abstract—The standard approach to edge detection is based on a model of edges as large step changes in intensity. This approach fails to reliably detect and localize edges in natural images where blur scale and contrast can vary over a broad range. The main problem is that the appropriate spatial scale for local estimation depends upon the local structure of the edge, and thus varies unpredictably over the image. Here we show that knowledge of sensor properties and operator norms can be exploited to define a unique, locally computable minimum reliable scale for local estimation at each point in the image. This method for local scale control is applied to the problem of detecting and localizing edges in images with shallow depth of field and shadows. We show that edges spanning a broad range of blur scales and contrasts can be recovered accurately by a single system with no input parameters other than the second moment of the sensor noise. A natural dividend of this approach is a measure of the thickness of contours which can be used to estimate focal and penumbral blur. Local scale control is shown to be important for the estimation of blur in complex images, where the potential for interference between nearby edges of very different blur scale requires that estimates be made at the minimum reliable scale.
Edge Detection Techniques  An Overview
 INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND IMAGE ANALYSIS
, 1998
"... In computer vision and image processing, edge detection concerns the localization of significant variations of the grey level image and the identification of the physical phenomena that originated them. This information is very useful for applications in 3D reconstruction, motion, recognition, image ..."
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Cited by 130 (2 self)
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In computer vision and image processing, edge detection concerns the localization of significant variations of the grey level image and the identification of the physical phenomena that originated them. This information is very useful for applications in 3D reconstruction, motion, recognition, image enhancement and restoration, image registration, image compression, and so on. Usually, edge detection requires smoothing and differentiation of the image. Differentiation is an illconditioned problem and smoothing results in a loss of information. It is difficult to design a general edge detection algorithm which performs well in many contexts and captures the requirements of subsequent processing stages. Consequently, over the history of digital image processing a variety of edge detectors have been devised which differ in their mathematical and algorithmic properties. This paper is an account of the current state of our understanding of edge detection. We propose an overview of research...
Multiscale Detection of Curvilinear Structures in 2D and 3D Image Data
, 1995
"... This paper presents a novel, parameterfree technique for the segmentation and local description of line structures on multiple scales, both in 2D and 3D. The algorithm is based on a nonlinear combination of linear filters and searches for elongated, symmetric line structures, while suppressing th ..."
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Cited by 106 (3 self)
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This paper presents a novel, parameterfree technique for the segmentation and local description of line structures on multiple scales, both in 2D and 3D. The algorithm is based on a nonlinear combination of linear filters and searches for elongated, symmetric line structures, while suppressing the response to edges. The filtering process creates one sharp maximum across the linefeature profile and across scalespace. The multiscale response reflects local contrast and is independent of the local width.
A unifying framework for structure and motion recovery from image sequences
 In Proc. Int. Conference on Computer Vision
, 1995
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Image Features from Phase Congruency
, 1999
"... This paper presents a new measure of phase congruency and shows how it can be calculated through the use of wavelets. The existing theory that has been developed for 1D signals is extended to allow the calculation of phase congruency in 2D images. It is shown that, for good localization, ..."
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Cited by 75 (1 self)
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This paper presents a new measure of phase congruency and shows how it can be calculated through the use of wavelets. The existing theory that has been developed for 1D signals is extended to allow the calculation of phase congruency in 2D images. It is shown that, for good localization, it is important to consider the spread of frequencies present at a point of phase congruency. An effective method for identifying and compensating for the level of noise in an image is presented