A Contour Tracing Algorithm for Ginseng Shape

2013 ◽  
Vol 401-403 ◽  
pp. 1268-1271
Author(s):  
Bing Quan Huo ◽  
Feng Ling Yin
Keyword(s):  
Image Processing ◽  
Computer Technology ◽  
Color Image ◽  
Binary Image ◽  
Color Information ◽  
Edge Information ◽  
Boundary Information ◽  
Image Edge ◽  
Tracing Algorithm ◽  
Neighbourhood Algorithm

More and more applications of computer technology are used in the field of agriculture. In this paper, Image processing technology is applied to ginseng shape. Get a color image from the device, remove the color information and reserve the boundary information. Highlight the image edge information by gradient sharpening, binary image and use 8-neighbourhood algorithm for tracking the border.

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.  

Sub-Pixel Edge Detection of Color Image Based on Dimensionality Reduction Technique and Curve Fitting

2013 ◽  
Vol 433-435 ◽  
pp. 281-287
Author(s):  
Feng Xiao ◽  
Li Na Guo
Keyword(s):  
Edge Detection ◽  
Dimensionality Reduction ◽  
Curve Fitting ◽  
Color Image ◽  
Edge Information ◽  
Projected Image ◽  
Image Edge Detection ◽  
Dimensionality Reduction Technique ◽  
Image Edge ◽  
Edge Detection Method

In order to solve the problem of the color characteristic information is often ignored in color image edge detection process, we propose a new color image sub-pixel edge detection method, which uses Ostu algorithm to get coarse positioning edges and extracts sub-pixel edge on the combination of curve fitting and coarse positioning edge in the projected image obtained by dimensionality reduction technique.Experimental results show that the algorithm positioning accuracy can reach 0.14 pixels,it is able to extract color image edge information effectively.

Contents Based Color Image Edge Detection Using Hessian Matrix

2013 ◽  
Vol 427-429 ◽  
pp. 1853-1860 ◽  
Author(s):  
Li Zhang ◽  
Lie Jun Wang ◽  
Sen Hai Zhong ◽  
Gang Zhao
Keyword(s):  
Edge Detection ◽  
Color Image ◽  
Hessian Matrix ◽  
Edge Information ◽  
Image Edge Detection ◽  
Background Texture ◽  
Image Edge ◽  
Visual Characteristics ◽  
Direction Information ◽  
Pseudo Color

The edges are the most fundamental and important characteristic of an image, edge detection is the key link and classic topic in machine vision and image processing. Considering the human visual characteristics, the intrinsic gradient direction information of color images was used to obtain the pseudo-color edges of the images by conducting multichannel edge detection. After enhancing the edge information and removing the correlation, the brightness is extracted in order to obtain complete image edge information. To make the edges more smooth and continuous, the Hessian matrix is used to remove coarse edges and edges with redundant background texture. The experiment verifies the effectiveness of the proposed algorithm, and the comparison with other scheme indicates that our scheme can improve the effectiveness, continuity and sharpness of edge detection.

An Automotive Airbag Detection Method Based on Image Processing

2013 ◽  
Vol 416-417 ◽  
pp. 1350-1354 ◽  
Author(s):  
Xiang Xin Shao ◽  
Mu Jun Xie
Keyword(s):  
Image Processing ◽  
Automobile Industry ◽  
Detection Method ◽  
Detection Methods ◽  
Edge Information ◽  
Invariant Moment ◽  
Boundary Tracking ◽  
Image Edge ◽  
Auto Parts ◽  
Dial Indicator

The development of the automobile industry led to the development of auto parts, and airbags are one of the most important safety components of cars. In this paper, to meet the shortfall of traditional way of using a dial indicator to detect airbags deficiencies, a new automotive airbag shape detection methods based on image processing technology is put forward. First, extract the airbags image edge information using the method of boundary tracking, then detect whether airbags are qualified according to the similarity of image invariant moment. Experiments confirmed this method can improve the range and accuracy of the airbag detection.

Design and Implementation of Digital Image Processing Teaching Demonstration System

2013 ◽  
Vol 347-350 ◽  
pp. 3237-3241
Author(s):  
Shi Min Zhang
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Color Image ◽  
Color Image Processing ◽  
Practical Application ◽  
Image Transformation ◽  
Design And Implementation ◽  
Image Edge Detection ◽  
Image Edge

Digital image processing technology is widely used in the further application of computer graphics. This thesis introduces a digital image processing teaching demonstration system including image file management, image transformation, color image processing, binarization, image enhancement, and image edge detection extra. In function, it embraces basic skills in digital image processing. This thesis is a favorable assistant in your study and practical application by means of friendly operation interface, the contrast of image processing effect demonstration and algorithm routine.

Computer Processing of an Image

2021 ◽  
pp. 1-22
Author(s):  
Chandra Prabha R. ◽  
Shilpa Hiremath
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Digital Images ◽  
Color Image ◽  
Image Representation ◽  
Computer Processing ◽  
Binary Image ◽  
Grayscale Image ◽  
Under Sampling

In this chapter, the authors have briefed about images, digital images, how the digital images can be processed. Image types like binary image, grayscale image, color image, and indexed image and various image formats are explained. It highlights the various fields where digital image processing can be used. This chapter introduces a variety of concepts related to digital image formation in a human eye. The mechanism of the human visual system is discussed. The authors illustrate the steps of image processing. Explanation on different elements of digital image processing systems like image acquisition, and others are also provided. The components required for capturing and processing the image are discussed. Concepts of image sampling, quantization, image representation are discussed. It portrays the operations of the image during sampling and quantization and the two operations of sampling which is oversampling and under-sampling. Readers can appreciate the key difference between oversampling and under-sampling applied to digital images.

DESIGNING CNN GENES FOR BINARY IMAGE EDGE SMOOTHING AND NOISE REMOVING

2006 ◽  
Vol 16 (10) ◽  
pp. 3007-3013 ◽  
Author(s):  
QINBIN HE ◽  
FANGYUE CHEN
Keyword(s):  
Image Processing ◽  
Binary Image ◽  
Common Method ◽  
Original Image ◽  
Image Edge

Edge smoothing and noise removing for images are a common method of image processing. By designing CNN genes, edges can be smoothed and particles can be removed from a binary image. However, a satisfying result cannot be obtained by choosing only one CNN gene. In this paper, a group of edge smoothing and noise removing CNN genes is proposed as a synthetic disposal for a binary image. Disposed by the group of CNN genes, the characteristics of the original image can be preserved as much as possible. Two examples of edge smoothing and noise removing for a binary image are well illustrated by this method in this paper.

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