scholarly journals Comparative Analysis of common Edge Detection Algorithms using Pre-processing Technique

2017 ◽  
Vol 7 (5) ◽  
pp. 2574 ◽  
Author(s):  
R. Vijaya Kumar Reddy ◽  
K. Prudvi Raju ◽  
M. Jogendra Kumar ◽  
L. Ravi Kumar ◽  
P Ravi Prakash ◽  
...  
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Processing Technique ◽  
Detection Methods ◽  
Common Edge ◽  
Detection Algorithms ◽  
Segmentation Methods ◽  
Edge Detection Method ◽  
Inherent Quality

<p>Edge detection is the process of segmenting an image by detecting discontinuities in brightness. So far, several standard segmentation methods have been widely used for edge detection. However, due to inherent quality of images, these methods prove ineffective if they are applied without any preprocessing. In this paper, an image preprocessing approach has been adopted in order to get certain parameters that are useful to perform better edge detection with the standard edge detection methods. The proposed preprocessing approach involves median filtering to reduce the noise in image and then Edge Detection technique is carried out. And atlast Standard edge detection methods can be applied to the resultant preprocessing image and its Simulation results are show that our preprocessed approach when used with a standard edge detection method enhances its performance.</p>

Finger Vein ROI Extraction Based on Robust Edge Detection and Flexible Sliding Window

2017 ◽  
Vol 32 (04) ◽  
pp. 1856002 ◽  
Author(s):  
Mingwen Wang ◽  
Dongming Tang ◽  
Zhangyou Chen
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Finger Joint ◽  
Reference Line ◽  
Detection Methods ◽  
Joint Region ◽  
Sliding Windows ◽  
Finger Vein ◽  
Roi Extraction ◽  
Edge Detection Method

An accurate region of interest extraction (ROI) plays an important role for both finger vein recognition systems and finger vein-based cryptography systems. In order to localize the rectangle ROI accurately, the edges of the finger and a line in the finger joint region should be detected accurately as a reference position. Because most of the existing finger edge detection methods do not work well, a robust finger edge detection method is proposed in this paper. An inner line of the finger is first detected to divide the finger vein image by two parts, after that two edge detection templates and a series of technologies such as interpolation, fit, etc. are used to detect and fix the wrong edges of the finger. Furthermore, considering that the shapes of the brighter finger joint region are irregular, multiple sliding windows including rectangle, disk, diamond and ellipse are generated, respectively to detect the reference line of the finger joint. Finally, a contour similarity distance-based method is introduced to evaluate the performance of various sliding windows. The experimental results show that the proposed edge detection method can 100% successfully detect the edges of the fingers in our finger vein image database. And for various detection windows, the ellipse window is more suitable for the detection of the finger joint reference line. So, the proposed ROI extraction method for finger vein images has a better overall performance compared with the other methods.

scholarly journals A novel edge detection method based on efficient gaussian binomial filter

2021 ◽  
Vol 7 (2) ◽  
pp. 211
Author(s):  
El Houssain Ait Mansour ◽  
Francois Bretaudeau
Keyword(s):  
Edge Detection ◽  
High Speed ◽  
Detection Method ◽  
Detection Methods ◽  
Gaussian Filter ◽  
Image Edge Detection ◽  
Low Pass ◽  
Image Edge ◽  
Edge Detection Method ◽  
Low Pass Filters

Most basic and recent image edge detection methods are based on exploiting spatial high-frequency to localize efficiency the boundaries and image discontinuities. These approaches are strictly sensitive to noise, and their performance decrease with the increasing noise level. This research suggests a novel and robust approach based on a binomial Gaussian filter for edge detection. We propose a scheme-based Gaussian filter that employs low-pass filters to reduce noise and gradient image differentiation to perform edge recovering. The results presented illustrate that the proposed approach outperforms the basic method for edge detection. The global scheme may be implemented efficiently with high speed using the proposed novel binomial Gaussian filter.

scholarly journals Comparative Analysis of Image Quality Values on Edge Detection Methods

2020 ◽  
Vol 4 (2) ◽  
pp. 345-351
Author(s):  
Wicaksono Yuli Sulistyo ◽  
Imam Riadi ◽  
Anton Yudhana
Keyword(s):  
Image Processing ◽  
Comparative Analysis ◽  
Edge Detection ◽  
Computer Technology ◽  
Detection Method ◽  
Detection Methods ◽  
Sobel Operator ◽  
Image Edge Detection ◽  
Image Edge ◽  
Edge Detection Method

Identification of object boundaries in a digital image is developing rapidly in line with advances in computer technology for image processing. Edge detection becomes important because humans in recognizing the object of an image will pay attention to the edges contained in the image. Edge detection of an image is done because the edge of the object in the image contains very important information, the information obtained can be either size or shape. The edge detection method used in this study is Sobel operator, Prewitt operator, Laplace operator, Laplacian of Gaussian (LoG) operator and Kirsch operator which are compared and analyzed in the five methods. The results of the comparison show that the clear margins are the Sobel, Prewitt and Kirsch operators, with PSNR calculations that produce values ​​above 30 dB. Laplace and LoG operators only have an average PSNR value below 30 dB. Other quality comparisons use the histogram value and the contrast value with the highest value results in the Laplace and LoG operators with an average histogram value of 110 and a contrast value of 24. The lowest histogram and contrast value are owned by the Sobel and Prewitt operators.  

Edge Detection Using the Bhattacharyya Distance with Adjustable Block Space

2020 ◽  
Vol 2020 (10) ◽  
pp. 133-1-133-7
Author(s):  
Jiho Yoon ◽  
Chulhee Lee
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Wiener Filter ◽  
Detection Methods ◽  
Bhattacharyya Distance ◽  
Block Space ◽  
Mean Vectors ◽  
The Mean ◽  
Edge Detection Method ◽  
Mean Vector

In this paper, we propose a new edge detection method for color images, based on the Bhattacharyya distance with adjustable block space. First, the Wiener filter was used to remove the noise as pre-processing. To calculate the Bhattacharyya distance, a pair of blocks were extracted for each pixel. To detect subtle edges, we adjusted the block space. The mean vector and covariance matrix were computed from each block. Using the mean vectors and covariance matrices, we computed the Bhattacharyya distance, which was used to detect edges. By adjusting the block space, we were able to detect weak edges, which other edge detections failed to detect. Experimental results show promising results compared to some existing edge detection methods.

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.

An Effective Method for Defects Detection in Radiographic Images of Welds Based on Edge Detection and Morphology

2013 ◽  
Vol 290 ◽  
pp. 71-77
Author(s):  
Wen Ming Guo ◽  
Yan Qin Chen
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Morphological Processing ◽  
Radiographic Images ◽  
X Ray ◽  
Weld Defects ◽  
Detection Algorithms ◽  
Detection Quality

In the current industrial production, as steel weld X-ray images are low contrasted and noisy, the efficiency and precision can’t be both ensured. This paper has studied three different edge detection algorithms and found the most suitable one to detect weld defects. Combined with this edge detection algorithm, we proposed a new weld defects detection method. This method uses defect features to find the defects in edge images with morphological processing. Compared to the traditional methods, the method has ensured detection quality of weld defects detection.

scholarly journals Processing of Drone’s Digital Image for Determining Border of Rice Fields with Edge Detection Method

2019 ◽  
Vol 2 (2) ◽  
pp. 139-144
Author(s):  
Suhardiman Diman ◽  
Zahir Zainuddin ◽  
Salama Manjang
Keyword(s):  
Edge Detection ◽  
Rice Field ◽  
Detection Method ◽  
Computing Time ◽  
Threshold Value ◽  
Error Rates ◽  
Detection Methods ◽  
Canny Edge Detection ◽  
Canny Edge ◽  
Edge Detection Method

Edge detection was the basic thing used in most image processing applications to get information from the image frame as a beginning for extracting the features of the segmentation object that will be detected. Nowadays, many edge detection methods create doubts in choosing the right edge detection method and according to image conditions. Based on the problems, a study was conducted to compare the performance of edge detection using methods of canny, Sobel and laplacian by using object of rice field. The program was created by using the Python programming language on OpenCV.  The result of the study on one image test that the Canny method produces thin and smooth edges and did not omit the important information on the image while it has required a lot of computing time. Classification is generally started from the data acquisition process; pre-processing and post-processing. Canny edge detection can detect actual edges with minimum error rates and produce optimal image edges. The threshold value obtained from the Canny method was the best and optimal threshold value for each method. The result of a test by comparing the three methods showed that the Canny edge detection method gives better results in determining the rice field boundary, which was 90% compared to Sobel 87% and laplacian 89%.

scholarly journals A Pre-processing Step for Efficient Edge Extraction

2019 ◽  
Vol 8 (4) ◽  
pp. 11550-11554
Keyword(s):  
Edge Detection ◽  
Shape Representation ◽  
Input Image ◽  
Object Localization ◽  
Edge Extraction ◽  
Detection Algorithms ◽  
Processing Step ◽  
Shape Structure ◽  
Edge Detection Method

In this paper, we propose a pre-processing step for an efficient edge extraction technique that takes input as an original image to generate an edge map. Generated edge maps could be inputted for state-of-art traditional edge detection algorithms like Canny, Sobel, Prewitt, and recent edge detection algorithms gb-UCM, CED Contours, Structured forest, Sparse Code Gradients and CNN based edge detection Deep Edge, N4 to get better performance. Further, the proposed algorithm has not required any training or learning to improve the edge detection method and is not depending on any parameters. Visual experiments and quantitative evaluation results show that our proposed algorithm greatly improves the modal quality of edge/edge maps. It preserves the original shape, structure of the objects and local features, which presents in an input image. The proposed method takes very less amount of time to execute and making it more suitable for real-time image processing and computer vision applications that depend on edge like classification, object localization, object recognition, image retrieval, segmentation, shape representation.

Macrozooplankton Image Edge Detection Using Wavelet Packet Decomposition

2010 ◽  
Vol 108-111 ◽  
pp. 44-49
Author(s):  
Jing Ying Zhao ◽  
Hai Guo ◽  
Xing Bin Sun
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Wavelet Packet ◽  
Detection Methods ◽  
Automatic Identification ◽  
Wavelet Packet Decomposition ◽  
Edge Detectors ◽  
Reconstruction Image ◽  
Image Edge ◽  
Edge Detection Method

Comparing with the phytoplankton, there are few researches on zooplanktons. Now, many waterworks don’t monitor the zooplanktons in source water. There isn’t effective detection method for several common macro zooplanktons such as chironomid larvae, cyclops and so on, and little has been done in the field of the macro zooplanktons automatic identification and monitor. This paper puts for forward a macrozooplankton edge detection method based on wavelet packet decomposition and reconstruction. We erase the high frequency parts by applying wavelet packet decomposition in the original images and then detect the edge of reconstruction images using the common edge detectors such as Prewitt, Sobel, Roberts, Laplacian of Gaussion, Canny and so on. The experimental results show that the edge detection methods in the reconstruction image work better than in the original image.

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