Comparison of Crack Extraction Performance According to Different Edge Detectors

2014 ◽  
Vol 607 ◽  
pp. 669-672 ◽  
Author(s):  
Hyun Woo Cho ◽  
Hyuk Jin Yoon
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Concrete Structures ◽  
Edge Detector ◽  
Concrete Walls ◽  
Edge Detectors ◽  
Extraction Performance

Edges of images contain important information such as form, size, and location of objects. Thus, edge detection is an extremely important factor in image processing. In order to detect such edges, much research has been carried out, and edge detectors such as Sobel, Prewitt, Robert, Canny, and Laplacian of Gaussian (“LoG”) are universally used. This Research compares and analyzes the crack extraction function of each edge detector to automatically detect cracks occurring in concrete structures. In order to do this, simulated crack samples of various widths and contrasts were produced and attached to concrete walls, and crack images were taken from different distances. Cracks were extracted from the images taken using edge detectors like Sobel, Prewitt, Robert, Canny, and LoG, and the crack extraction performance of the edge detectors used were compared and analyzed based on the photographed distance.

Detecting Edge Using Support Vector Machine

2012 ◽  
Vol 588-589 ◽  
pp. 974-977 ◽  
Author(s):  
Jih Pin Yeh
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Edge Detection ◽  
Supervised Learning ◽  
Classical Method ◽  
Support Vector ◽  
Learning Method ◽  
Edge Detector

The edge detection is used in many applications in image processing. It is currently crucial technique of image processing. There are various methods for promoting edge detection. Here, it is presented that edge detection can be achieved using Support Vector Machine (SVM). Supervised learning method is applied. Laplacian edge detector is an instructor of Support Vector Machine. In this research, it is presented that any classical method can be applied for training of SVM as edge detector.

A Novel Approach for Edge Detection in Images Based on Cellular Learning Automata

2012 ◽  
Vol 2 (4) ◽  
pp. 51-61 ◽  
Author(s):  
Farhad Soleimanian Gharehchopogh ◽  
Samira Ebrahimi
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Learning Automata ◽  
Edge Detector ◽  
Noise Elimination ◽  
Minimum Period ◽  
Detection Algorithms ◽  
Novel Approach ◽  
Result Show ◽  
Cellular Learning Automata

Cellular Learning Automata (CLA) has been used in many fields of image processing such as noise elimination, smoothing, retrieval, fractionated and extraction of the content Characteristics of the images. The edge detection in images and methods if edge detection, have a great role in machine vision and cognizance systems. This method uses operands for analyzing images and digital image processing. Many studios here been conducted till now in edge detection algorithms of various conditions. In this study a new method for edge detection in images with the use of CLA is recommended. The proposed method of edge detection in images was tested with different sizes and the results were compared with Sobel edge detector classic method. The result show that the method based on CLA has a desirable performance in edge detection and compares the images with a more uniformity during a minimum period of time.

A Novel Edge Detection Algorithm Based on Texture Feature Coding

2015 ◽  
Vol 24 (2) ◽  
pp. 235-248 ◽  
Author(s):  
Abdulkadir Sengur ◽  
Yanhui Guo ◽  
Mehmet Ustundag ◽  
Ömer Faruk Alcin
Keyword(s):  
Edge Detection ◽  
Texture Feature ◽  
Detection Algorithm ◽  
Input Image ◽  
Edge Detector ◽  
Edge Image ◽  
Hardware System ◽  
Analysis Scheme ◽  
Edge Detectors ◽  
Field Programmable

AbstractA new edge detection technique based on the texture feature coding method (TFCM) is proposed. The TFCM is a texture analysis scheme that is generally used in texture-based image segmentation and classification applications. The TFCM transforms an input image into a texture feature image whose pixel values represent the texture information of the pixel in the original image. Then, on the basis of the transformed image, several features are calculated as texture descriptors. In this article, the TFCM is employed differently to construct an edge detector. In particular, the texture feature number (TFN) of the TFCM is considered. In other words, the TFN image is used for subsequent processes. After obtaining the TFN image, a simple thresholding scheme is employed for obtaining the coarse edge image. Finally, an edge-thinning procedure is used to obtain the tuned edges. We conducted several experiments on a variety of images and compared the results with the popular existing methods such as the Sobel, Prewitt, Canny, and Canny–Deriche edge detectors. The obtained results were evaluated quantitatively with the Figure of Merit criterion. The experimental results demonstrated that our proposed method improved the edge detection performance greatly. We further implemented the proposed edge detector with a hardware system. To this end, a field programmable gate array chip was used. The related simulations were carried out with the MATLAB Simulink tool. Both software and hardware implementations demonstrated the efficiency of the proposed edge detector.

A NEW EDGE DETECTION METHOD THROUGH TEMPLATE MATCHING

1994 ◽  
Vol 08 (04) ◽  
pp. 899-917 ◽  
Author(s):  
MAO-JIUN J. WANG ◽  
SHIAU-CHYI CHANG ◽  
CHIH-MING LIU ◽  
WEN-YEN WU
Keyword(s):  
Edge Detection ◽  
Template Matching ◽  
Pattern Analysis ◽  
Detection Method ◽  
Performance Criteria ◽  
Detection Methods ◽  
Edge Detector ◽  
Edge Detectors ◽  
Edge Detection Method ◽  
Single Pixel

This paper reviews some gradient edge detection methods and proposes a new detector — the template matching edge detector (TMED). This detector utilizes the concepts of pattern analysis and the template matching of 3×3 masks. A set of performance criteria was used to evaluate the gradient edge detectors as well as the template matching edge detector. The results indicate that the new method is superior to the other gradient edge detectors. In addition, the template matching edge detector has also demonstrated good performance on noisy images. It can obtain very precise edge detection of single pixel width.

A step edge detector based on bilinear transformation

2021 ◽  
pp. 1-12
Author(s):  
Koorosh Dabighi ◽  
Akbar Nazari ◽  
Saeid Saryazdi
Keyword(s):  
Edge Detection ◽  
Signal To Noise Ratio ◽  
Quantitative Comparison ◽  
Discrete Version ◽  
Edge Detector ◽  
Bilinear Transformation ◽  
Proposed Model ◽  
Edge Detectors ◽  
Canny Edge ◽  
Error Metric

Nowadays, Canny edge detector is considered to be one of the best edge detection approaches for the images with step form. Various overgeneralized versions of these edge detectors have been offered up to now, e.g. Saryazdi edge detector. This paper proposes a new discrete version of edge detection which is obtained from Shen-Castan and Saryazdi filters by using bilinear transformation. Different experimentations are conducted to decide the suitable parameters of the proposed edge detector and to examine its validity. To evaluate the strength of the proposed model, the results are compared to Canny, Sobel, Prewitt, LOG and Saryazdi methods. Finally, by calculation of mean square error (MSE) and peak signal-to-noise ratio (PSNR), the value of PSNR is always equal to or greater than the PSNR value of suggested methods. Moreover, by calculation of Baddeley’s error metric (BEM) on ten test images from the Berkeley Segmentation DataSet (BSDS), we show that the proposed method outperforms the other methods. Therefore, visual and quantitative comparison shows the efficiency and strength of proposed method.

scholarly journals Hyperanalytic Wavelet-Based Robust Edge Detection

2021 ◽  
Vol 13 (15) ◽  
pp. 2888
Author(s):  
Alexandru Isar ◽  
Corina Nafornita ◽  
Georgiana Magu
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
State Of The Art ◽  
Speckle Noise ◽  
Transform Domain ◽  
Edge Detector ◽  
Edge Detectors ◽  
Gradient Based ◽  
New Type ◽  
Edge Detection Method

The imperfections of image acquisition systems produce noise. The majority of edge detectors, including gradient-based edge detectors, are sensitive to noise. To reduce this sensitivity, the first step of some edge detectors’ algorithms, such as the Canny’s edge detector, is the filtering of acquired images with a Gaussian filter. We show experimentally that this filtering is not sufficient in case of strong Additive White Gaussian or multiplicative speckle noise, because the remaining grains of noise produce false edges. The aim of this paper is to improve edge detection robustness against Gaussian and speckle noise by preceding the Canny’s edge detector with a new type of denoising system. We propose a two-stage denoising system acting in the Hyperanalytic Wavelet Transform Domain. The results obtained in applying the proposed edge detection method outperform state-of-the-art edge detection results from the literature.

An Edge Detection Method Based on Adjacent Dispersion

2016 ◽  
Vol 30 (10) ◽  
pp. 1655026
Author(s):  
Qindong Sun ◽  
Yimin Qiao ◽  
Hua Wu ◽  
Jiamin Wang
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Detection Method ◽  
Superior Performance ◽  
Gray Level ◽  
Edge Detector ◽  
Edge Detectors ◽  
Gray Level Image ◽  
Novel Method ◽  
Edge Detection Method

Edge detection is a vital part in image segmentation. In this paper, a novel method based on adjacent dispersion for edge detection is proposed. This method utilizes adjacent dispersion to detect edges, avoiding thresholds selection, anisotropy in convolution computation and discontinuity in edges, and it is composed of two modules, namely the dispersion operator and the refinement. The dispersion is to obtain a matrix of discrete coefficient of a gray level image and the refinement is to thin edges to one-pixel-point and ensure it logically continuous. The performance of the proposed edge detector is evaluated on different test images and compared with popular edge detectors, Canny and Sobel. Experiment results indicate that the proposed method performs well without thresholds and offers superior performance in continuity in edge detection in digital images.

scholarly journals Robust Edge Detection Method for Segmentation of Diabetic Foot Ulcer Images

2020 ◽  
Vol 10 (4) ◽  
pp. 6034-6040
Author(s):  
R. H. Mwawado ◽  
B. J. Maiseli ◽  
M. A. Dida
Keyword(s):  
Edge Detection ◽  
Diabetic Foot ◽  
Skin Color ◽  
Foot Ulcer ◽  
Foot Ulcers ◽  
Semantic Features ◽  
Diabetic Foot Ulcers ◽  
Edge Detector ◽  
Comparative Performance ◽  
Edge Detectors

Segmentation is an open-ended research problem in various computer vision and image processing tasks. This pre-processing operation requires a robust edge detector to generate appealing results. However, the available approaches for edge detection underperform when applied to images corrupted by noise or impacted by poor imaging conditions. The problem becomes significant for images containing diabetic foot ulcers, which originate from people with varied skin color. Comparative performance evaluation of the edge detectors facilitates the process of deciding an appropriate method for image segmentation of diabetic foot ulcers. Our research discovered that the classical edge detectors cannot clearly locate ulcers in images with black-skin feet. In addition, these methods collapse for degraded input images. Therefore, the current research proposes a robust edge detector that can address some limitations of the previous attempts. The proposed method incorporates a hybrid diffusion-steered functional derived from the total variation and the Perona-Malik diffusivities, which have been reported to can effectively capture semantic features in images. The empirical results show that our method generates clearer and stronger edge maps with higher perceptual and objective qualities. More importantly, the proposed method offers lower computational times—an advantage that gives more insights into the possible application of the method in time-sensitive tasks.

WAVELET-HOUGH TRANSFORM WITH APPLICATIONS IN EDGE AND TARGET DETECTIONS

2006 ◽  
Vol 04 (03) ◽  
pp. 567-587 ◽  
Author(s):  
LUOQING LI ◽  
YUAN Y. TANG
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Edge Detection ◽  
Detection Algorithm ◽  
Remarkable Feature ◽  
Edge Detectors ◽  
History Of ◽  
Physical Phenomena ◽  
Direction Effect ◽  
Grey Level Image

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. 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. In this paper, we briefly describe some existing edge detectors. We investigate the wavelet transform with direction which is related to the Canny and Marr–Hildreth's edge detectors. This wavelet transform has a remarkable feature that plays an important role in image processing applications. We discuss its applications to edge and target detections. The direction effect enables the wavelets to analyze the directional features of images. Algorithms and experiments for edge and target detections have been developed based on the theory. And, very promising results have been shown in image processing.

Sign in / Sign up

Export Citation Format

Share Document