Image segmenting method based on improvement of intersecting visual cortical model

An image segmentation and cross vision technology, applied in the field of image processing, can solve problems such as limiting the application of ICM image segmentation, and achieve the effect of facilitating parameter adaptive transformation, reducing computational complexity, and simplifying models

Inactive Publication Date: 2009-11-11
BEIHANG UNIV
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Problems solved by technology

For different images, different parameters need to be selected. At present, the selection of parameters is gene...

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  • Image segmenting method based on improvement of intersecting visual cortical model
  • Image segmenting method based on improvement of intersecting visual cortical model
  • Image segmenting method based on improvement of intersecting visual cortical model

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Embodiment Construction

[0027] The present invention will be further described in detail with reference to the accompanying drawings and embodiments.

[0028] like figure 1 Shown, method of the present invention is divided into following five steps:

[0029] Step 1: Improve the basic ICM model, form a network based on the improved basic ICM model, initialize network parameters, and input the original image to be segmented.

[0030] Since the current network based on the ICM basic model is not suitable for adaptive transformation of its network parameters, in order to achieve the purpose of parameter self-adaptation, the ICM basic model is improved first. ICM basic model such as figure 2 As shown, a single neuron in the ICM is composed of a receiving module 1 , a modulating module 2 , a pulse generating module 3 and a threshold signal generator 4 . Wherein the receiving module 1 is composed of an external stimulation input module 5 and a linear connection input module 6, the external stimulation i...

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Abstract

The invention discloses an image segmenting method based on improvement of an intersecting visual cortical model. The method comprises the following steps of: firstly improving ICM basic model, establishing a network on the basis of improved ICM basic model, initiating the network parameter, inputting the original image to be segmented, self-adaptively adjusting each parameter in each neuron model according to the self-characteristic of the input image; subsequently updating the internal state value, dynamic threshold and output value of each neuron, outputting the sequentially iterative segmented images, calculating and memorizing the mutual information amount of the original image and the segmented images; and finally judging whether to iterate continuously or output the best segmented image according to the current iterative times. The method simplifies the model, improves the efficiency, completes the segmentation automation, solves the problems that the current model parameter needs to be adjusted manually by test according to different images and the best segmentation needs to be adopted manually, and has more obvious visual advantages simultaneously.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to an image segmentation method based on an improved cross visual cortex model. Background technique [0002] Image segmentation is the key content of image analysis, and has been highly valued by researchers at home and abroad for many years. Thousands of various types of segmentation methods have been proposed so far. However, these segmentation methods cannot meet the satisfaction level of human vision in some cases. In recent years, some scholars have proposed a neural network that simulates a biological system by combining the human visual neural perception mechanism with the help of a biological model of the mammalian visual cortex, and obtained a more accurate image segmentation effect. [0003] The Intersecting Cortical Model (ICM) is the product of the crossover of various cortical models. It is mainly evolved from the Eckhorn model and the Rybak model, while extracting oth...

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Application Information

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IPC IPC(8): G06T7/00G06N3/02
Inventor 牛建伟沈思思马建
Owner BEIHANG UNIV
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