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Proceedings ArticleDOI

Comparative Study of Edge Detection Algorithms

01 Jan 2003-Seg Technical Program Expanded Abstracts (Society of Exploration Geophysicists)-
TL;DR: In this paper, the authors evaluate the sensitivity of classic derivative, Canny and Torreao and Amaral edge detection algorithms used in image processing in their ability to extract the full spectrum of geological features in the presence of random noise and acquisition footprint.
Abstract: Summary Important geological features like faults and channels often manifest themselves as edges in seismic data. Several classes of edge detection algorithms have been developed to extract the edges of seismic data. Two of the most basic and straightforward classes are that of the gradient edge detectors and Gaussian edge detectors. In this paper we evaluate the sensitivity of classic derivative, Canny and Torreao and Amaral edge detection algorithms used in image processing in their ability to extract the full spectrum of geological features in the presence of random noise and acquisition footprint. Geological features of interest may include linear but arbitrary oriented faults and fractures, sinuous channels, and highly irregular but organized karasting and dewatering features. Through application to real data we conclude that Torreao and Amaral, Canny, and derivative algorithms exhibit better performance respectively. We show that these algorithms perform better in detecting faults and channels when noise is suppressed.
Citations
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Journal ArticleDOI
TL;DR: An edge detection method of UAVRSI by combining Zernike moments with clustering algorithms is proposed in this study and shows that the proposed method effectively improves the accuracy of edge information extracted from remote sensing images.
Abstract: Due to the unmanned aerial vehicle remote sensing images (UAVRSI) within rich texture details of ground objects and obvious phenomenon, the same objects with different spectra, it is difficult to effectively acquire the edge information using traditional edge detection operator. To solve this problem, an edge detection method of UAVRSI by combining Zernike moments with clustering algorithms is proposed in this study. To begin with, two typical clustering algorithms, namely, fuzzy -means (FCM) and -means algorithms, are used to cluster the original remote sensing images so as to form homogeneous regions in ground objects. Then, Zernike moments are applied to carry out edge detection on the remote sensing images clustered. Finally, visual comparison and sensitivity methods are adopted to evaluate the accuracy of the edge information detected. Afterwards, two groups of experimental data are selected to verify the proposed method. Results show that the proposed method effectively improves the accuracy of edge information extracted from remote sensing images.

12 citations


Cites methods from "Comparative Study of Edge Detection..."

  • ...For instance, Vishwakarma and Katiyar (2011) conducted a comparative analysis into the detected edges of remote sensing images, respectively, using Canny operator, Sobel operator, and Prewitt operator [7]....

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  • ...For instance, Vishwakarma and Katiyar (2011) conducted a comparative analysis into the detected edges of remote sensing images, respectively, using Canny operator, Sobel operator, and Prewitt operator [7]....

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01 Jan 2011
TL;DR: This paper provides a basis for objectively comparing the performance of different techniques and quantifies relative noise tolerance, and results shown allow selection of the most optimum method for application to image.
Abstract: ------------------------------------------------------------------ABSTRACT-------------------------------------------------------Edge detection is the most important feature of image processing for object detection, it is crucial to have a good understanding of edge detection algorithms/operators. Computer vision is rapidly expanding field that depends on the capability to perform faster segments and thus to classify and infer images. Segmentation is central to the successful extraction of image features and their ensuing classification. Powerful segmentation techniques are available; however each technique is ad hoc. In this paper, the computer vision investigates the sub regions of the composite image, brings out commonly used and most important edge detection algorithms/operators with a wide-ranging comparative along with the statistical approach. This paper implements popular algorithms such as Sobel, Roberts, Prewitt, Laplacian of Gaussian and canny. A standard metric is used for evaluating the performance degradation of edge detection algorithms as a function of Peak Signal to Noise Ratio (PSNR) along with the elapsed time for generating the segmented output image. A statistical approach to evaluate the variance among the PSNR and the time elapsed in output image is also incorporated. This paper provides a basis for objectively comparing the performance of different techniques and quantifies relative noise tolerance. Results shown allow selection of the most optimum method for application to image.

8 citations


Cites methods from "Comparative Study of Edge Detection..."

  • ...The assessment of edge detection [14], [12] performance obeys the three important criterion....

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Journal ArticleDOI
TL;DR: This paper proposes a new technique for detecting faces in color images using color model and edge detection and shows that the proposed algorithm is good enough to detect the human face taken through video with accuracy.
Abstract: The increasing use of computer vision in security in place of humans led many to research the problem of face detection in images. The problem is not a petty one as the classification of a human face proves to challenging. Despite the many variations of a human face, features can still be found, given a certain context, which will uniquely identify a face. Early face-detection algorithms focused on the detection of frontal human faces, whereas this paper attempt to solve the more general and difficult problem of multi-view face detection. Face detection involves many research challenges such as scale, rotation, and pose and illumination variation. The techniques used for face detection have been researched for years and much progress has been suggested in literature. This paper proposes a new technique for detecting faces in color images using color model and edge detection. Face detection is used in as a part of a facial recognition system. It is also used in human computer interface, image database management and video surveillance. The results of this technique show that the proposed algorithm is good enough to detect the human face taken through video with accuracy. This paper is achieving high detection speed, high detection accuracy and reduces the false detecting rate.

3 citations

Journal ArticleDOI
TL;DR: A new edge detector based on swarm intelligence is proposed, which fairly detects the edges of all types of images with improved quality, and with a low failing probability in detecting edges.
Abstract: front end of most vision systems consists of edge detection as preprocessing. The vision of objects is easy for the human because of the natural intelligence of segmenting, pattern matching and recognizing very complex objects. But for the machine, everything needs to be artificially induced and it is not so easy to recognize and identify objects. Towards Computer vision, the Machine needs pattern recognition; extracting the important features so as to recognize the objects, where the boundary detection or the edge detection is very crucial. Edge detection is finding the points where there are sudden changes in the intensity values and linking them suitably. This paper aims, at presenting a comparative study on the Gradient based edge detectors with a swarm intelligence. Though, these detectors are applied on to the same image, they may not see the same edge pixels. Some detectors seems to be good only for simple transparent images which are less noise prone, and marks pseudo and congested edges in case of denser images. Hence it would be appreciated, to have an edge detector, which is sensitive in detecting edges in majority of the common types of edges. With this in mind, the authors propose a new edge detector based on swarm intelligence, which fairly detects the edges of all types of images with improved quality, and with a low failing probability in detecting edges.

2 citations


Cites background or methods from "Comparative Study of Edge Detection..."

  • ...The convolution mask of Prewitt [5], [6], [7] is as shown in Figure 2...

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  • ...The Sobel Detection The Sobel operator [5], [6], [7] performs a 2D spatial gradient measurement on an image, hence emphasizes regions of high spatial frequency that correspond to edges....

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  • ...Edge detection operators [5], [6], [7] examine each pixel neighborhood and quantify the slope....

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  • ...The mask value [5], [6], [7] is as shown in Figure 3....

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  • ...2 important quantities in edge detection are the gradient magnitude denoted by [6]...

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Journal ArticleDOI
TL;DR: Experimental results prove that Canny operator is better than Prewitt and Sobel for the selected image and Subjective and Objective methods are used to evaluate the different edge operators.
Abstract: Detection of edge is a terminology in image processing and computer vision particularly in the areas of feature detection and extraction to refer to the algorithms which aims at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities. Edge is a basic feature of image. The image edges include rich information that is very significant for obtaining the image characteristics by object recognition. Edge detection refers to the process of identifying and locating sharp discontinuities in an image. So, edge detection is a vital step in image analysis and it is the key of solving many complex problems. This paper, describes edge detection algorithms for image segmentation using various computing approaches which have got great fruits. Experimental results prove that Canny operator is better than Prewitt and Sobel for the selected image. Subjective and Objective methods are used to evaluate the different edge operators. The performance of Canny, Sobel and Prewitt Edge Detection are evaluated for detection of edges in digital images.
References
More filters
Journal ArticleDOI
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


"Comparative Study of Edge Detection..." refers background in this paper

  • ...The Canny (1986) filter is one of the most widely applied edge detectors in the image processing community....

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Journal ArticleDOI
TL;DR: Taner et al. as mentioned in this paper presented a generalized Hilbert transform (GHT) which has many advantages over the traditional Hilbert transform, such as robustness to noise and variety of applications.
Abstract: The Hilbert transform (HT) has been used in seismic data processing and interpretation for many years. A well-known application of HT is seismic complex-trace analysis using instantaneous phase, frequency, and amplitude (Taner et al. 1979). We present a new generalized Hilbert transform (GHT), which has many advantages over the traditional HT—particularly robustness to noise and variety of applications.

58 citations

Journal ArticleDOI
TL;DR: A new approach for the design of differential operators, based on the Green's function solution to a signal matching equation, is introduced, illustrated by the construction of step-edge enhancement filters whose performance figures are comparable, and even superior, to those reported in the literature.

12 citations

Proceedings ArticleDOI
TL;DR: Torreao and Amaral's filter as mentioned in this paper is based on the Green's function and it can detect sharp edges better than the gradients amplitude filters reported in literatures, and it has been successfully applied to synthetic and real 3D seismic data from the Vinton dome, Louisiana.
Abstract: We introduce a very interesting new filter for edgedetection: the Torreao and Amaral’s filter that is based on the Green’s function. We applied this filter to detect sharp edges and we found good results. The importance of this fact in geology is that features like faults, fractures and channels appear as sharp edges in seismic data. Thus, this filter is efficient to detect these geological features and we have successfully applied this new filter to synthetic and real 3D seismic data from the Vinton dome, Louisiana and we found that it can detect edges better than the gradients amplitude filters reported in literatures.

10 citations


"Comparative Study of Edge Detection..." refers methods in this paper

  • ...In this article, we compare the most commonly used derivative and Canny filters with a new filter based on the Torreao and Amaral’s function to an interpolator problem, recently applied to seismic data (Al-Dossary and Marfurt, 2003)....

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