Self-adaptive target detection method based on SPCNN

Abstract

The invention discloses a self-adaptive target detection method based on an SPCNN, and belongs to the technical field of computer vision. The implementation method comprises the following steps: calculating an image static attribute parameter; deducing a theoretical formula according to the Stevens law, and calculating a threshold attenuation time constant [alpha]e, so that the threshold attenuation time constant [alpha]e can be adaptively set according to the overall gray feature of the target image; based on an adaptive side inhibition mechanism, improving an inhibition coefficient calculation model by using a hyperbolic tangent function, and calculating a link weight matrix of each pixel point by using the inhibition coefficient calculation model; inputting the image into an SPCNN with complete self-adaptive setting of parameters, continuously iterating and generating a binarization segmentation result, and extracting candidate targets. Based on a fast connection mechanism in neuron synchronization, in combination with a grayscale image criterion, automatic output of an optimal segmentation result is realized by calculating the similarity of adjacent iteration segmentation results and searching a similarity maximum value, and meanwhile, iteration is automatically controlled, so that the efficiency and intelligence of a target detection method are improved.

Description

technical field [0001] The invention relates to an adaptive target detection method, in particular to an adaptive target detection method based on a Simplified Pulse Coupled Neural Network (SPCNN, Simplified Pulse Coupled Neural Network), and belongs to the technical field of computer vision. Background technique [0002] Pulse coupled neural network (PCNN) belongs to the new neural network model of the third generation artificial neural network. Pulse-coupled neural network (PCNN) is inspired by the neuron activity in the main visual area V1 of the human neocortex. It has translation, rotation, scale invariance, good noise resistance and no need to process digital images. training and other advantages. The properties of PCNN for image processing mechanisms are divided into a new three-dimensional, the first dimension specifies the time matrix of PCNN, the second dimension captures the firing rate of PCNN, and the third dimension is the synchronization of PCNN. [0003] At...

AI Technical Summary

Problems solved by technology

[0006] For SPCNN, it is necessary to manually set parameters, manually select the optimal output, and the existing methods are complex in calculation, time-consuming and poor in robustness.

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