Method for identifying monocular visual spaces in terrestrial gravitational field environments

Abstract

The invention discloses a method for identifying monocular visual spaces in terrestrial gravitational field environments. The method is characterized by comprising steps of firstly, dividing ultra-pixels of images on the basis of CIELAB color space values L, a and b of pixels and coordinate values x and y of the pixels to generate ultra-pixel images; secondly, reducing dimensions of the divided and formed ultra-pixel images by a general clustering algorithm on the basis of vector distances from color characteristics to feature characteristics of the ultra-pixels and adjacency relations, and generating large image blocks; thirdly, respectively multiplying pixels of the obtained large image blocks by fuzzy distribution density functions of gravitational fields and solving expected values of the large image blocks so as to initially classify the sky, the ground and vertical objects; fourthly, extracting classified images of the sky, the ground and the vertical objects by the aid of single-layer wavelet sampling and characteristics of the Manhattan direction; fifthly, generating spatial depth perception images on the basis of wavelet imaging models and ground linear perspective information. The fuzzy distribution density functions of the gravitational fields represent the sky, the ground and the vertical objects. The method has the advantages of simplicity, feasibility, high resolution and wide application range.

Description

technical field [0001] The present invention relates to an image processing method, especially an image processing method that can be widely used in fields such as robot visual navigation, large-space target measurement, target tracking and positioning, and can improve spatial recognition. Monocular visual space recognition method in the environment of gravity field. Background technique [0002] Understanding 3D spatial structure as a basic problem of machine vision has been paid attention to and studied for a long time. Early research work focused on stereo vision or obtaining 3D clues through the movement of the viewing angle. In recent years, many researchers have focused on reconstructing 3D spatial structures from monocular vision images. At present, most monocular vision 3D spatial recognition methods use supervised machine learning methods, such as: Markov Random Fields (MRFs ), Conditional Probability Random Fields (CRFs), and Dynamic Bayesian Networks (DBN), etc. ...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to solve the problems that most of the existing image recognition methods need to be learned through image learning. These methods have the problems of large data processing capac...

Images