Research on improved edge extraction algorithm of rectangular piece

2018 ◽  
Vol 29 (01) ◽  
pp. 1850007 ◽  
Author(s):  
Yi-Bin He ◽  
Ya-Jun Zeng ◽  
Han-Xin Chen ◽  
San-Xia Xiao ◽  
Yan-Wei Wang ◽  
...  
Keyword(s):  
Edge Detection ◽  
Detection Algorithm ◽  
Experimental Result ◽  
Structural Elements ◽  
Canny Operator ◽  
Edge Detection Algorithm ◽  
Morphological Gradient ◽  
Extraction Algorithm ◽  
Image Edge ◽  
Rectangular Piece

Traditional edge detection operators such as Prewitt operator, LOG operator and Canny operator, etc. cannot meet the requirements of the modern industrial measurement. This paper proposes a kind of image edge detection algorithm based on improved morphological gradient. It can be detect the image using structural elements, which deals with the characteristic information of the image directly. Choosing different shapes and sizes of structural elements to use together, the ideal image edge information can be detected. The experimental result shows that the algorithm can well extract image edge with noise, which is clearer, and has more detailed edges compared with the previous edge detection algorithm.

Edge Detection of Color Images Based on Improved Morphological Gradient Operators

2014 ◽  
Vol 511-512 ◽  
pp. 550-553 ◽  
Author(s):  
Jian Yong Liang
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Color Image ◽  
Color Images ◽  
Morphological Gradient ◽  
Image Edge Detection ◽  
Gradient Based ◽  
Image Edge ◽  
Edge Detection Method ◽  
Traditional Approaches

Edge detection is an old and hot topic in image processing, pattern recognition and computer vision. Numerous edge detection approaches have been proposed to gray images. It is difficult to extend these approaches to color image edge detection. A novel edge detection method based on mathematical morphology for color images is proposed in this paper. The proposed approach firstly compute vector gradient based on morphological gradient operators, and then compute the optimal gradient according to structure elements with different size. Finally, we use a threshold to binary the gradient images and then obtain the edge images. Experimental results show that the proposed approach has advantages of suppressing noise and preserving edge details and it is not sensitive to noise pixel. The finally edge images via the proposed method have high PSNR and NC compared with the traditional approaches.

A Novel Approach of Edge Detection Based on Vector Morphology

2014 ◽  
Vol 511-512 ◽  
pp. 545-549
Author(s):  
Qiang Chen
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Color Image ◽  
Detection Algorithm ◽  
Difficult Problem ◽  
Color Images ◽  
Edge Detection Algorithm ◽  
Morphological Gradient ◽  
Novel Approach ◽  
Image Edges

Edge detection of color image is a difficult problem in image processing. Although a lot of corresponding to methods have been proposed, however, none of them can effectively detect image edges while suppressing noises. In this paper, a novel edge detection algorithm of color images based on mathematical morphology is proposed. Through designing a new anti-noise morphological gradient operators, we can obtain better edge detection results. The proposed gradient operators are applied to detect edge for three components of a color image. An then, the final edge can be obtained by fusing the three edge results. Experimental results show that the feasibility and effectiveness of the proposed algorithm. Moreover, the proposed algorithm has better effect of preserving the edge details and better robustness to noises than traditional methods.

An efficient fuzzy deep learning approach to recognize 2D faces using FADF and ResNet-164 architecture

2021 ◽  
pp. 1-10
Author(s):  
K. Seethalakshmi ◽  
S. Valli
Keyword(s):  
Deep Learning ◽  
Edge Detection ◽  
Error Rate ◽  
Recognition Rate ◽  
Learning Approach ◽  
Noise Detection ◽  
Accuracy Rate ◽  
Automatic Feature Extraction ◽  
Morphological Gradient ◽  
Smoothing Effects

Deep learning using fuzzy is highly modular and more accurate. Adaptive Fuzzy Anisotropy diffusion filter (FADF) is used to remove noise from the image while preserving edges, lines and improve smoothing effects. By detecting edge and noise information through pre-edge detection using fuzzy contrast enhancement, post-edge detection using fuzzy morphological gradient filter and noise detection technique. Convolution Neural Network (CNN) ResNet-164 architecture is used for automatic feature extraction. The resultant feature vectors are classified using ANFIS deep learning. Top-1 error rate is reduced from 21.43% to 18.8%. Top-5 error rate is reduced to 2.68%. The proposed work results in high accuracy rate with low computation cost. The recognition rate of 99.18% and accuracy of 98.24% is achieved on standard dataset. Compared to the existing techniques the proposed work outperforms in all aspects. Experimental results provide better result than the existing techniques on FACES 94, Feret, Yale-B, CMU-PIE, JAFFE dataset and other state-of-art dataset.

Spline Interpolation Sub Pixel Edge Detection Method Based on Improved Morphological Gradient

2014 ◽  
Vol 971-973 ◽  
pp. 1756-1759 ◽  
Author(s):  
Chun Yan Nan ◽  
Xiao Hui Yang
Keyword(s):  
Edge Detection ◽  
Simulation Experiment ◽  
Detection Method ◽  
Interpolation Method ◽  
Spline Interpolation ◽  
Morphological Gradient ◽  
Image Edge Detection ◽  
Image Edge ◽  
Edge Detection Method ◽  
Improved Methods

In order to improve the accuracy of image edge detection.A spline interpolation sub pixel edge detection method based on improved morphological gradient is proposed in the thesis.Firstly,using improved morphological gradient filter operator for image coarse positioning;Then,the cubic spline interpolation method is carried out for pixel-level edge of the image interpolation so that the image edge locates in sub-pixel level.Have a simulation experiment to improved methods by Matlab, results show that the improved method can accurately detect the edge of the image, edge detection is fine, the precision of positioning is hight and the result of detection is good.

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