A Novel Edge Detection Approach Based on Soft Morphological Operations

2012 ◽  
Vol 220-223 ◽  
pp. 2828-2832
Author(s):  
Bo Chen ◽  
Meng Jia
Keyword(s):  
Edge Detection ◽  
Edge Effect ◽  
Detection Method ◽  
Experimental Results ◽  
Morphological Operations ◽  
The Novel ◽  
Target Segmentation ◽  
Detection Approach ◽  
Edge Detection Method ◽  
Image Details

Edge detection and target segmentation is difficult due to noise existing in an image. A novel edge detection method is proposed based on soft morphological operations in this paper. Because soft morphological operations can remove noise while preserving image details, which can be used to construct morphological edge detection operators with high robustness and better edge effect. Experimental results show that, comparing with the existing edge detection operators, the novel edge detection method can get better edge effect while removing pseudo edges.

Adaptive edge detection method for images

2008 ◽  
Vol 16 (1) ◽  
Author(s):  
A. Walczak ◽  
L. Puzio
Keyword(s):  
Edge Detection ◽  
Extraction Method ◽  
Detection Method ◽  
Polar Coordinate System ◽  
The Novel ◽  
Edge Extraction ◽  
Anisotropic Property ◽  
Multi Scale ◽  
Polar Coordinate ◽  
Edge Detection Method

AbstractThe novel two-dimensional (2D) wavelet with anisotropic property and application of it has been presented. Wavelet is constructed in the polar coordinate system to obtain anisotropic properties. A novel edge detection method has been developed with the aid of this wavelet. This method detects gradient jump and than follows along this jump. In this way the number of calculation for edge localization is reduced. Moreover, the presented method is able to detect all edges in an image in multi-scale together with its spatial orientation. Proposed wavelet as well as edge extraction method seems to be new way to edge detection for an image.

A Novel Improved Edge Detection Method

2011 ◽  
Vol 225-226 ◽  
pp. 1096-1099
Author(s):  
Yan Ying Guo ◽  
Yan Ying Guo
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Adaptive Algorithms ◽  
Morphological Operations ◽  
Training Method ◽  
Morphological Operators ◽  
Structuring Element ◽  
Edge Strength ◽  
Addition And Subtraction ◽  
Edge Detection Method

In this paper, a novel morphological edge detection using adaptive weighted morphological operators is presented. The newly introduced operators employ weighted structuring element (SE) and apply multiplication or division in place of addition and subtraction in classical morphological operations. It judges its edge and its direction by means of training method and differentiable equivalent representations for the operators, efficient adaptive algorithms to optimize SEs are derived. The gradient of the adaptive weighted morphology utilizes a set of SEs to detect the edge strength with a view to decrease the spurious detail edge and suppressed the noise. Results will be presenting for images in comparison with the others edging detectors.

An Edge Detection Approach for Fractal Image Processing

2020 ◽  
pp. 1-22
Author(s):  
Kalyan Kumar Jena ◽  
Sasmita Mishra ◽  
Sarojananda Mishra
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Detection Method ◽  
Challenging Problem ◽  
Gray Scale ◽  
Fractal Image ◽  
Detection Approach ◽  
Fractal Images ◽  
Morphological Operator ◽  
Edge Detection Method

Research in the field of fractal image processing (FIP) has increased in the current era. Edge detection of fractal images can be considered as an important domain of research in FIP. Detecting edges in different fractal images accurate manner is a challenging problem in FIP. Several methods have introduced by different researchers to detect the edges of images. However, no method works suitably under all conditions. In this chapter, an edge detection method is proposed to detect the edges of gray scale and color fractal images. This method focuses on the quantitative combination of Canny, LoG, and Sobel (CLS) edge detection operators. The output of the proposed method is produced using matrix laboratory (MATLAB) R2015b and compared with the edge detection operators such as Sobel, Prewitt, Roberts, LoG, Canny, and mathematical morphological operator. The experimental outputs show that the proposed method performs better as compared to other traditional methods.

scholarly journals Depth edge detection by image-based smoothing and morphological operations

2016 ◽  
Vol 3 (3) ◽  
pp. 191-197 ◽  
Author(s):  
Syed Mohammad Abid Hasan ◽  
Kwanghee Ko
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Depth Image ◽  
Morphological Operations ◽  
Edge Preservation ◽  
Canny Edge Detection ◽  
Depth Images ◽  
Canny Edge ◽  
Computer Vision Applications ◽  
Edge Detection Method

Abstract Since 3D measurement technologies have been widely used in manufacturing industries edge detection in a depth image plays an important role in computer vision applications. In this paper, we have proposed an edge detection process in a depth image based on the image based smoothing and morphological operations. In this method we have used the principle of Median filtering, which has a renowned feature for edge preservation properties. The edge detection was done based on Canny Edge detection principle and was improvised with morphological operations, which are represented as combinations of erosion and dilation. Later, we compared our results with some existing methods and exhibited that this method produced better results. However, this method works in multiframe applications with effective framerates. Thus this technique will aid to detect edges robustly from depth images and contribute to promote applications in depth images such as object detection, object segmentation, etc. Highlights A method is proposed that can detect edges from depth images more profoundly. We modified the Canny edge detection method using morphological operations. The proposed method works in multi-frames.

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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