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 Cut-Edge Detection Method for Rice Harvesting Based on Machine Vision

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 590
Author(s):  
Zhenqian Zhang ◽  
Ruyue Cao ◽  
Cheng Peng ◽  
Renjie Liu ◽  
Yifan Sun ◽  
...  
Keyword(s):  
Machine Vision ◽  
Edge Detection ◽  
Detection Method ◽  
Region Of Interest ◽  
Average Error ◽  
Vertical Projection ◽  
Cut Edge ◽  
Fitting Method ◽  
Combine Harvester ◽  
Edge Detection Method

A cut-edge detection method based on machine vision was developed for obtaining the navigation path of a combine harvester. First, the Cr component in the YCbCr color model was selected as the grayscale feature factor. Then, by detecting the end of the crop row, judging the target demarcation and getting the feature points, the region of interest (ROI) was automatically gained. Subsequently, the vertical projection was applied to reduce the noise. All the points in the ROI were calculated, and a dividing point was found in each row. The hierarchical clustering method was used to extract the outliers. At last, the polynomial fitting method was used to acquire the straight or curved cut-edge. The results gained from the samples showed that the average error for locating the cut-edge was 2.84 cm. The method was capable of providing support for the automatic navigation of a combine harvester.

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