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Rhinopithecus roxellana qinlingensis image edge detection method based on gray B-type related improvement Prewitt

An image edge and detection method technology, applied in the field of image processing, can solve problems such as poor image recognition, difficulty in image top detection, and fuzzy signal edges, etc., to achieve clear calculation particle outlines, enhanced anti-interference ability, accurate and continuous sex-enhancing effect

Inactive Publication Date: 2017-10-27
XI'AN PETROLEUM UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Traditional spatial noise suppression methods, such as median filter denoising method, etc., often at the cost of blurring the details of the image, will weaken the details and blur the edge of the signal. The result is not ideal, and the phenomenon of "topping" is prone to occur, that is, it is difficult to detect the top of the image, and it is easy to lose corner information. The edge of the image is not sharp and continuous, and the further accurate recognition of the image is not good.

Method used

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  • Rhinopithecus roxellana qinlingensis image edge detection method based on gray B-type related improvement Prewitt
  • Rhinopithecus roxellana qinlingensis image edge detection method based on gray B-type related improvement Prewitt
  • Rhinopithecus roxellana qinlingensis image edge detection method based on gray B-type related improvement Prewitt

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0060] The image processing in this example starts with figure 1 , the original image of 200×221 pixels is taken as an example to illustrate the implementation steps of this example:

[0061] (1), a noise detection 3×3 window of a certain pixel x(i,j) in an image with a size of 200×221 is:

[0062]

[0063] Create a one-dimensional data sequence of images. Using a simple four-neighborhood one-dimensional method, respectively take the pixel x(i,j) and four adjacent pixels in the image to form a one-dimensional data sequence {x i-1,j ,x i+1,j ,x i,j ,x i,j-1 ,x i,j+1}.

[0064] (2), determine the reference sequence and comparison sequence:

[0065] Select a reference sequence as follows: x 0 ={1,1,1,1,1},x 1 = {0,0,0,0,0}, comparison sequence: x r ={x i-1,j ,x i+1,j ,x i,j ,x i,j-1 ,x i,j+1}, where r ∈ [1,200×221].

[0066] (3), sequence initialization:

[0067] x' 0 =x 0 , x′ 1 =x 1

[0068]

[0069] (4), calculate x' 0 with x' r The absolute value...

Embodiment 2

[0097] The image processing in this example starts with image 3 , the original image of 169×154 pixels is taken as an example to illustrate the implementation steps of this example:

[0098] (1), a noise detection 3×3 window of a certain pixel x(i,j) in an image with a size of 169×154 is:

[0099]

[0100] Create a one-dimensional data sequence of images. Using a simple four-neighborhood one-dimensional method, respectively take the pixel x(i,j) and four adjacent pixels in the image to form a one-dimensional data sequence {x i-1,j ,x i+1,j ,x i,j ,x i,j-1 ,x i,j+1}.

[0101] (2), determine the reference sequence: x 0 ={1,1,1,1,1},x 1 = {0,0,0,0,0}, comparison sequence: x r ={x i-1,j ,x i+1,j ,x i,j ,x i,j-1 ,x i,j+1}, where r ∈ [1,169×154].

[0102] (3), sequence initialization:

[0103] x' 0 =x 0 , x′ 1 =x 1

[0104]

[0105] (4), calculate x' 0 with x' r The absolute value difference sequence △' of the difference between the corresponding compon...

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Abstract

The invention discloses a rhinopithecus roxellana qinlingensis image edge detection method based on gray B-type related improvement Prewitt. A high-pixel camera is used for carrying out tracking and real-time shooting on a rhinopithecus roxellana qinlingensis base, an image sample library is established, an obtained image sample adopts a gray B-type related improvement Prewitt operator to detect a rhinopithecus roxellana qinlingensis image edge, and an image edge is extracted. A calculating particle contour line is clear, a feature and a background have a good contrast ratio, the antijamming capability of edge detection is enhanced, the accuracy and the continuity of boundary detection are obviously improved, and therefore, a foundation is laid for realizing rhinopithecus roxellana qinlingensis identification.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an improved Prewitt image edge detection method for Qinling golden monkey based on gray B-type association. Background technique [0002] The wild golden monkey is a national first-class protected animal, mainly distributed in Gansu, Sichuan, Hubei and Shaanxi. The Qinling golden monkey distributed in the Qinling area of ​​Shaanxi has attracted more attention because of its rare species. Through the research on the image noise reduction and edge detection technology of golden snub-nosed monkeys in Qinling Mountains, it lays the foundation for the classification and recognition of golden snub-nosed monkeys. Technical Support. Traditional spatial noise suppression methods, such as median filter denoising method, etc., often at the cost of blurring the details of the image, will weaken the details and blur the edge of the signal. The result is not ideal, and the phenomenon of "topp...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/13G06T7/136
CPCG06T7/13G06T7/136G06T2207/10004
Inventor 爨莹薛继军史瑶杰
Owner XI'AN PETROLEUM UNIVERSITY
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