scholarly journals Comparative studies of multiscale edge detection using different edge detectors for MRI thigh

2021 ◽  
Vol 10 (4) ◽  
pp. 1979-1986
Author(s):  
Belinda Chong Chiew Meng ◽  
Dayang Suhaida Awang Damit ◽  
Nor Salwa Damanhuri
Keyword(s):  
Edge Detection ◽  
Image Features ◽  
Multiscale Method ◽  
Multiscale Methods ◽  
Object Boundary ◽  
Translation Invariant ◽  
Edge Detectors ◽  
Transform Coefficients ◽  
Edge Detection Method ◽  
Average Filter

Edge detection plays an important role in computer vision to extract object boundary. Multiscale edge detection method provides a variety of image features by different resolution at multiscale of edges. The method extracts coarse and fine structure edges simultaneously in an image. Due to this, the multiscale method enables more reliable edges are detected. Most of the multiscale methods are not translation invariant due to the decimated process. They mostly depend on the corresponding transform coefficients. These methods need more computation and a larger storage space. This study proposes a multiscale method that uses an average filter to smooth image at three different scales. Three different classical edge detectors namely Prewitt, Sobel and Laplacian were used to extract the edges from the smooth images. The edges extracted from the different scales of smooth images were then combined to form the multiscale edge detection. The performances of the multiscale images extracted from the three classical edge detectors were then compared and discussed.

Early Fire Detection of Large Space Combining Thresholding with Edge Detection Techniques

2010 ◽  
Vol 44-47 ◽  
pp. 2060-2064
Author(s):  
Guo Liang Hu ◽  
Xi Jiang
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Fire Detection ◽  
Image Features ◽  
Large Space ◽  
Detection Techniques ◽  
Edge Detectors ◽  
Otsu’S Method ◽  
Image Edges ◽  
Early Fire Detection

Image segmentation is a crucial step of the early fire detection in large space based on image processing technology. The image edges contain abundant feature information, and the edge detection has been a main topic of image segmentation algorithm. In this paper, several kinds of traditional edge detectors have been used to detect the edge of frame target in the fire video images, and the results have been contrasted and analyzed. Considering the influence of breaks in the edge caused by noise, nonuniform illumination and spurious intensity discontinuities, proposing the method of combining thresholding with edge detection, using Otsu’s method to compute a threshold for segmentation, extracting the flame area from the background, and then using the traditional edge detectors to detect the flame edge. At the same time, the simulation results based on the MATLAB kits indicate that this kind of method has good effectiveness and strong robustness, the detected flame edges have better effect in integrality and definition, and the relevant result can be the basis of the subsequent extraction and analysis of the fire image features as well as the space positioning of the fire.

WAVELET-BASED EDGE DETECTION IN DIGITAL IMAGES

2008 ◽  
Vol 08 (04) ◽  
pp. 513-533 ◽  
Author(s):  
MUHAMMAD HUSSAIN ◽  
TURGHUNJAN ABDUKIRIM ◽  
YOSHIHIRO OKADA
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Local Maxima ◽  
Wavelet Filters ◽  
Dyadic Wavelet ◽  
Edge Detectors ◽  
Frame Work ◽  
Dyadic Wavelet Transform ◽  
Edge Detection Method ◽  
Better Than

This paper proposes a wavelet based multilevel edge detection method that exploits spline dyadic wavelets and a frame work similar to that of Canny's edge detector.2 Using the recently proposed dyadic lifting schemes by Turghunjan et al.1 spline dyadic wavelet filters have been constructed, which are characterized by higher order of regularity and have the potential of better inherent noise filtering and detection results. Edges are determined as the local maxima in the subbands at different scales of the dyadic wavelet transform. Comparison reveals that our method performs better than Mallat's and Canny's edge detectors.

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.

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.

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.

scholarly journals Payload and quality augmentation using steganographic optimization technique based on edge detection

2021 ◽  
Vol 23 (1) ◽  
pp. 529
Author(s):  
Mafaz Alanezi ◽  
Iman Subhi Mohammed Altaay ◽  
Saja Younis Hamid Malla'aloo
Keyword(s):  
Edge Detection ◽  
Signal To Noise Ratio ◽  
Optimization Technique ◽  
Human Perception ◽  
Maximum Load ◽  
Cover Image ◽  
Least Significant Bit ◽  
Edge Pixel ◽  
Edge Detectors ◽  
Edge Detection Method

Information security is one of the most significant processes that must be taken into account when confidentially transferring information. This paper introduces a steganography technique using the edge detection method. It focused on three basic and important aspects’ payload, quality and security. Well-known edge detectors were used to generate as many edge pixels as possible to hide data and achieve the highest payload. The least significant bit (LSB) algorithm has been improved by extending the bits used to embed between 2-4 bits in smooth and sharp areas. To increase security, the transaction between the two parties is based on dividing the key and the cover image into several parts and agreeing on the type of edge detection.The experiments achieved the maximum load, for instance with a fuzzy edge detector, at first, embedding in 4 bitplanes if edge pixel and in 2 bitplanes if non-edge pixel, the peak signal-to-noise ratio (PSNR) increased from 43.580to 45.790. At second, embedding in 2 bitplanes if edge pixel, and in 4 bitplanes if non-edge pixel, the PSNR decreased between 38.433-41.593. The suggested scheme achieved a high pay load to embed in the cover image and according to human perception, it preserved the nature of the original image.

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