Showing papers on "Canny edge detector published in 1988"

A statistical and geometrical edge detector for SAR images

Abstract: A constant-false-alarm-rate (CFAR) edge detector based on the ratio between pixel values is described. The probability distribution of the image obtained by applying the edge detector is derived. Hence, the decision threshold can be theoretically determined for a given probability of false alarm as a function of the number of looks of the image under study and the size of the processing neighborhood. For a better and finer detection, the edge detector operates along the four usual directions over windows of increasing sizes. A test performed, for a given direction, on a radar image of an agricultural scene shows good agreement with the theoretical study. The operator is compared with the CFAR edge detectors suitable for radar images. >

Subpixel edge detection and estimation with a microprocessor-controlled line scan camera

Abstract: A microprocessor-controlled line scan camera system for measuring edges and lengths of steel strips is described, and the problem of subpixel edge detection and estimation in a line image is considered. The edge image is assumed to change gradually in its intensity, and the true edge location may be between pixels. Detection and estimation of edges are based on measurement of gray values of the line images at a limited number of pixels. A two-stage approach is presented. At the first stage, a computationally simple discrete-template-matching method is used to place the estimated edge point to the nearest pixel value. Three second-stage methods designed for subpixel estimation are examined. The modified Chebyshev polynomial and the three-point interpolation method do not require much knowledge on the shape of the edge intensity. If the functional form of the edge is known, a least-square estimation method may be used for better accuracy. In the case of nonstationary Poisson noise, a recursive maximum-likelihood method for the first-stage edge detection, followed by subpixel estimation, is proposed. >

Edge detection through residual analysis

Abstract: The authors provide theoretical justification for the use of zero crossings of residuals (between a filtered image and the original) for edge detection. The smoothed version is obtained by bilinear interpolation as a result of two-dimensional discrete regularization of subsampled images. The method is also applicable to smoothed images obtained by convolution with a Gaussian. Examples of applications of the method are shown for three kinds of pictures: aerial photographs, low-quality pictures of tools, and a high-quality picture of a face. The same parameters are used in all the examples. In addition they show examples of the results of one of the Canny edge detectors on the same pictures. >

Refining edges detected by a LoG operator

Abstract: The Laplacian of Gaussian (LoG) operator is one of the most popular operators used in edge detection. This operator, however, has some problems: zero-crossings do not always correspond to edges, and edges with an asymmetric profile introduce a symmetric bias between edge and zero-crossing locations. The authors offer solutions to these two problems. First, for one-dimensional signals, such as slices from images, they propose a simple test to detect true edges, and, for the problem of bias, they propose different techniques: the first one combines the results of the convolution of two LoG operators of different deviations, whereas the others sample the convolution with a single LoG filter at two points besides the zero-crossing. In addition to localization, these methods allow them to further characterize the shape of the edge. The authors present an implementation of these techniques for edges in 2-D images. >

Context dependent edge detection

Abstract: To simulate the edge perception ability of human eyes and detect scene edges from an image, context information must be used in the edge detection process. To accomplish the optimal use of the context, the authors introduce an edge detection scheme which uses the context of the whole image. The edge context for each pixel is the set of all row monotonically increasing paths through the pixel. The edge detector assigns a pixel that edge state having highest edge probability among all the paths. Experiments indicate the validity of the edge detector. Upon comparing the performance of the context dependent edge detector with the context free second directional derivative zero-crossing edge operator, the authors find that the context dependent edge detector is superior. >

Edge Detection Based On Multi-Edge Models

Abstract: The DRF method for edge detection is developed in more general cases, which can be easily implemented by parallel line processors for real time image processing.

Image edge detection and segmentation based on the Hilbert transform

Abstract: An efficient edge detection model based on a two-stage procedure is presented. The first stage consists of a linear transformation of an artificially created signal that has the property of detecting step edges irrespective of the distribution of the intervals between two consecutive edges. It is demonstrated that the Hilbert transform possesses this property and also outperforms the derivative operation in the detection of step edges in the presence of noise. The second stage consists of an appropriate mapping of the filtered results into edge detection primitives. Practical confirmation of the results is given by means of examples. >

Pyramidal Edge Detection And Image Representation

Abstract: We present a scheme of extracting edge information from parallel spatial frequency bands. From these we create an integrated image of most significant edges of different scales. The frequency bands are realized using the formalism of a Gaussian pyramid in which the levels represent a bank of spatial lowpass filters. The integrated edge image is created in a top-down algorithm, starting from the smallest version of the image. The sequential algorithm uses mutual edge information of two consecutive levels to control the processing in the lower one. This edge detection algorithm constitutes an image-dependent nonuniform processing scheme. Computational results show that only 20%-50% of the operations are needed to create an edge pyramid, compared to the number required in the regular scheme. The proposed generic scheme of image-dependent processing can be also implemented with operators other than edge detectors to exploit the advantages inherent in biological processing of images.

An edge detection model based on non-linear Laplace filtering

Abstract: We have developed and evaluated an edge detection scheme using a non-linear Laplace operator and the Marr-Hildreth model of edge detection. The technique is extremely effective and flexible in detecting one-pixel thick edges in images whose signal-to-noise ratios (SNR) range from 40 dB down to 0 dB. We have compared our results with those in the literature. For the test images we considered, our configuration performs at least as well - and in most cases far better - than other edge detectors. For these comparisons we have used Pratt's figure-of-merit as a quantitative performance measure. At very low signal-to-noise ratios ( Specific characterizations of the non-linear Laplacian are its adaptive orientation to the direction of the gradient, its inherent masks which permit the development of approximately circular (isotropic) filters, and its easy and fast implementation in software.

Edge And Surface Searching In Medical Images

Abstract: Several edge detection algorithms are examined for their utilities in edge or surface searching in medical images. Algorithms that are under study include those methods developed by Sobel, Roberts, Kirsch, Marr-Hildreth, Haralick, Shen-Castan, and Nalwa-Binford. An example of boundaries detected by applying these algorithms to brain images acquired by various imaging modalities is presented.

Regularized edge detection

Abstract: It has been observed that the edge detection problem in image processing is ill-posed. The ill-posed aspect of this problem is the differentiation step, which is an explicit operation in many schemes for finding edges in images. Differentiation is very sensitive to noise and its discrete version is an ill-conditioned operation. The authors concentrate on regularizing the differentiation operation and incorporating it in an edge detection scheme. They consider both a direct implementation of Miller regularization and one involving projections onto convex sets. >

A new type of effective morphologic edge detectors

Abstract: A type of edge detector based on the concept of gray-scale morphology is proposed. Edge detection can be divided into two phases; the first is noise removal, and the second is ideal edge detection. By using an iterative averaged closing-opening operation, impulse noise as well as Gaussian noise is eliminated from the image. Then, the resulting ideal edges can be extracted, either by using one of the classical spatial operators or by a simple morphologic operator. Results obtained from test images show that the proposed morphologic edge detectors are very effective. >

Optimising edge detection

Abstract: A feature detector is described that bases its research on a set of probability distributions that describe the situations in which the feature can be found The result is a measure of the probability of the feature occurring at a particular point The probabilities required to operate this detector can be automatically derived from test images, although it can be altered to detect any kind of feature The output from this detector is a set of probabilities that represent the likelihood of and edge occurring at each point in the image These need only to be summed to find the probability of an edge over an interval, and, in a similar fashion, images can be subsampled in order to find probable edges to subpixel accuracy >

An AI-structural approach to edge detection

Abstract: Edge detection is an important issue in computer vision and many works have been done in this area[l, 2, 3]. A new approach to edge detection is presented in this paper. It is based on the intuition of edge: edge should be at the place where the gray level of picture has great change and which has a long but narrow shape, i.e. it uses both gray level and structural information in picture. The key contribution of this paper is the finding of a special data structure of tree to represent both gray level and structural information in picture conveniently. The approach is reliable and of general purpose. An overview of the approach is given in §2. The details about the various aspects of the approach are presented in §3-6. Five examples including both artificial ones and natural ones are given in §7. The paper is concluded with the discussion about the advantages and potential improvements in §8.

Detection and smoothing of edge contours in images by one dimensional Kalman techniques

Abstract: Develops an edge model in images and the associated edge detection and contour determination algorithms based on statistical signal processing approaches. An edge point along any scan direction is defined as a sufficient jump in the mean of a time series which is assumed to be white Gaussian around the edge point with the same variance on either side. An edge contour is defined as a sequence of such edge points, each point denoted by the two coordinates of the image plane. Each coordinate sequence is modeled as first order auto regressive Gaussian over and above a straight line sequence of arbitrary, finite slope and intercept. The first stage of our overall algorithm examines the time series along each row and each column, forms a window around each potential edge point, detects the location and estimates the variance of the error in the location of the edge point, by pattern recognition techniques. The second stage forms noisy edge contours by graph searching techniques. The third stage smoothing of an edge contour is formulated as a well defined Kalman smoothing problem. It is shown that the parameters of the system and noise can all be estimated from the data itself. Experimental results on a simple image are discussed. >

An Efficient Filter Structure for Edge Detection

Abstract: A new efficient filter structure is proposed to implement the difference of Gaussian function, suitable for edge detection. The filter structure is based on a new discrete time operator with a separable infinite impulse response approach. The efficiency of the filter structure is demonstrated with results which compare favourably with the Laplacian of a Gaussian filter.

Context dependent edge detection

Abstract: To obtain the optimal use of context, an edge detection scheme is introduced which uses the context of the whole image. The edge context for each pixel is the set of all row-monotonically-increasing paths through the pixel. The edge detector assigns to a pixel the edge state having the highest edge probability among all the paths. Experiments indicate the validity of the edge detector. A comparison with the performance of the context-free second-directional derivative zero-crossing edge operator shows that the context-dependent edge detector is superior. >

M6.19 detection

Abstract: This paper develops an edge model in images and the associated edge detection and contour determination algorithms based on statistical signal processing approaches. An edge point along any scan direction is defined as a sufficient jump in the mean of a time series which is assumed to be white Gaussian around the edge point with the same variance on either side. An edge contour is defined as a sequence of such edge points, each point denoted by the two coordinates of the image plane. Each coordinate sequence is modeled as first order auto regressive Gaussian over and above a straight line sequence of arbitrary, finite slope and intercept. The first stage of our overall algorithm examines the time series along each row and each column, forms a window around each potential edge point, detects the location and estimates the variance of the error in the location of the edge point, by pattern recognition techniques. The second stage forms noisy edge contours by graph searching techniques. The third stage smoothing of an edge contour is formulated as a well defined ICalman smoothing problem. It is shown that the parameters of the system and noise can all be estimated from the data itself. Experimental results on a simple image are discussed.