Edge detection method based on relative variation

An edge detection and edge extraction technology, which is applied in the fields of computer vision and video retrieval, and can solve problems such as complex wavelet transforms

Inactive Publication Date: 2014-07-30
SHANGHAI UNIV
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But the wavelet tran

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  • Edge detection method based on relative variation
  • Edge detection method based on relative variation
  • Edge detection method based on relative variation

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Embodiment 1

[0064] see Figure 1 to Figure 5 , this edge detection method based on relative variation, the specific operation steps are as follows:

[0065] 1. Provide photos that require edge extraction: mainly for photos with complex textures or noise effects;

[0066] 2. Image preprocessing: For the provided photos, use relative variation for smoothing, suppress noise and remove texture;

[0067] 3. Image edge detection: For the smoothed photo, the traditional cellular neural network edge detection algorithm is used to detect the edge.

Embodiment 2

[0069] This embodiment is basically the same as Embodiment 1, and the special case is as follows:

[0070] In the image preprocessing of step 2, firstly, the texture of the picture can be smoothed better, and the influence of noise on edge detection is suppressed. The specific operation steps are as follows:

[0071] 1. Calculate the relative variation in the two directions of the image from the two directions of the abscissa and ordinate;

[0072] 2. Construct a diagonal matrix in each direction according to the obtained relative variational values ​​in each direction;

[0073] 3. Calculate the smoothed image according to the variational minimization model in the image and the obtained diagonal matrix;

[0074] 4. Loop until the energy after variational minimization is minimum to get the final preprocessed image;

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Abstract

The invention proposes a cellular neural network edge detection method based on relative variation regularization. The method includes image preprocessing and edge detection after the preprocessing. The image preprocessing mainly adopts the relative variation to smooth noises and textures; and the edge detection after the preprocessing mainly adopts a cellular neural network method to detect images. The method is capable of extracting effective and reliable and accurate effective edge information from pictures which are rich in noises and complex in textures. Compared with a traditional Canny method, the method restrains effects from the noises and the textures and compared with a standard cellular neural network method, the method prevents advance design of complex CNN template parameters.

Description

technical field [0001] The invention relates to an edge detection method based on relative variation, belonging to the fields of computer vision and video retrieval. technical background [0002] The edge of an image is one of the most basic features of an image. Its detection and extraction has always been a research hotspot in the field of image processing and computer vision. It is an important basis for image analysis and understanding such as image segmentation, texture feature extraction, and shape feature extraction. Many scholars at home and abroad have done in-depth research in this area. At present, the classic image edge detection methods include differential operator methods, such as Roberts operator, Sobel operator, Prewitt operator, Laplacian operator, and most operator methods, such as LOG operator, Canny operator. In addition, in recent years, some new methods have emerged in the field of image edge detection, such as wavelet multi-scale methods, cellular ne...

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Application Information

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IPC IPC(8): G06T7/00G06N3/06
Inventor 徐林黄东晋谢志峰丁友东
Owner SHANGHAI UNIV
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