Method and system for realizing combined semantic hierarchical connection model based on panoramic area scene perception

Abstract

The invention discloses a method and a system for realizing a combined semantic hierarchical connection model based on panoramic area scene perception. The system comprises an ROI extraction module, apanoramic region segmentation module, a spatial information acquisition module and a multi-level modeling module; the ROI extraction module is in segmentation connection with a target instance and transmits target salient region information; the panoramic region segmentation module is connected with the interest point 3D reconstruction module and transmits region boundary information; the spatialinformation acquisition module is connected with the semantic subspace model and transmits region position correlation information, and the multi-level modeling module outputs spatial semantics and correlation degree information of each region. On the basis that the ROI is obtained through region significance for panoramic segmentation, on the premise that interest points are extracted for geometric reconstruction and element space semantic information association, multi-level modeling of scene perception is achieved according to analysis of probability symbiosis of scene composition elements.

Description

technical field [0001] The present invention relates to a technology in the field of graphics processing, in particular to a method and system for realizing a combined semantic hierarchical connection model based on panoramic area scene perception. Background technique [0002] In the late 1970s and early 1980s, a complete scene understanding system began to appear, but the existing systems that try to fully understand the visual scene are not self-adaptive, and need to manually set parameters for specific scenes, which cannot be applied to new scene. Although the global energy optimization method based on the high-order Markov model can effectively describe the structural prior of the scene, express the local and global prior of the scene, and effectively integrate multiple scene understanding methods under the same energy optimization framework, but The contradiction between expressiveness and solvability. Although many complex high-order energy models provide rich const...

AI Technical Summary

Problems solved by technology

Although many complex high-order energy models provide rich constraints for scene understanding, due to the lack of corresponding optimization methods, they can only be solved by general optimization methods that are not suitable for the model

Another problem is that the current high-order energy models or solution methods lack sufficient generality, which limits its application range and scientific value

Images