Object material classification method for pulse type ToF depth camera

Abstract

The invention provides an object material classification method for a pulse type ToF camera. The object material classification method belongs to the field of pattern recognition and image processing.The object material classification method comprises the following steps: de-noising original data of a pulse type ToF camera; converting the image data into a feature vector; and optimizing a radialbasis function neural network classifier. The object material classification method is characterized in that an object material classification method is provided for a pulse type ToF imaging model; asecond-order full generalized variational model and a semi-blind deconvolution method are used to carry out denoising processing on the original image; and then sampling, curve fitting and resamplingare performed on the denoised image so as to obtain the feature vector of the object material, and finally optimization of singular value decomposition on a radial basis function neural network classifier is facilitated so that high accuracy and robustness of object material classification can be met.

Description

technical field [0001] The invention relates to the fields of pattern recognition and image processing, in particular to a method for classifying object materials suitable for pulse-type Time-of-Flight (ToF) depth cameras. Background technique [0002] With the development of computer vision and artificial intelligence, the demand for high-precision object material classification will increase. In the research field of computer vision, non-invasive and non-contact object material classification is an important research direction. According to the different types of cameras used, they can be divided into two categories: the field of visible light cameras and the field of Time-of-Flight (ToF) depth cameras. In the method of classifying object materials using visible light cameras, they use the visual appearance of object materials as the characteristics of material classification, and the representative features are the texture, color, roughness and color information of objec...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the shortcomings of the existing non-invasive and non-contact object material classification methods that are easily affected by ambient light and the classification accuracy is not high, the present invention proposes a method for object material classification for pulse-type ToF depth cameras. Image denoising module, eigenvector normalization module and radial basis neural network classifier training and optimization module, through which the influence of ambient light can be reduced and the result of high classification accuracy can be obtained

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