Building Image Material Extraction via Segmentation and Mutual Information
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Solution Overview
Problem
Current methods for extracting wall materials from building pictures are inefficient and prone to manual interference, leading to reduced accuracy and difficulty in achieving high-precision reconstruction.
Innovation Solution
A method and apparatus for material information extraction that utilizes image segmentation and feature mutual information analysis to quickly and accurately determine an object image block, allowing for efficient and precise material extraction without manual screening.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual experience-based screening is used to extract representative wall area blocks, then material classification can be performed, but the process is susceptible to manual experience, increases interference in subsequent material extraction, and affects efficiency and accuracy
Solution Approach 1:
The image processing system segments the building image into multiple image blocks automatically, dividing the wall area into discrete regions for analysis. This segmentation replaces manual screening with automated computational division, eliminating subjectivity while maintaining detailed analysis capability.
Solution Approach 2:
The patent replaces manual experience-based screening with an automated computer vision system that uses neural networks and image processing algorithms. This substitution eliminates human intervention, removing the susceptibility to manual experience and interference, while improving both efficiency and accuracy through consistent automated processing.
2Measurement precision
If manual experience-based screening is used to extract wall area blocks, then material classification can be performed, but efficiency of material extraction is reduced
Solution Approach 1:
The system performs preliminary automatic segmentation and identification of wall area blocks before material classification. By pre-processing the image to automatically identify and extract relevant blocks using neural networks, the system prepares the data in advance, eliminating the need for time-consuming manual screening and improving overall processing efficiency.
Solution Approach 2:
Manual screening is replaced with automated neural network-based image processing that rapidly identifies and extracts wall area blocks. This automated system processes images much faster than manual methods while maintaining or improving accuracy, thereby significantly increasing material extraction efficiency.
3Ease of manufacture
If manual screening is used to identify representative wall blocks, then material extraction can proceed, but interference in subsequent extraction increases
Solution Approach 1:
The patent replaces manual screening with an automated neural network-based system that consistently applies the same algorithms to all images. This substitution eliminates the variability and interference introduced by human operators, ensuring reliable and consistent extraction results across different images and operators, while maintaining process simplicity through automation.
Data Source
AI summary
A method, apparatus, and computer-readable storage medium for extracting material information from images. The method segments an image to obtain an object segmentation area representing non-background areas, which is then divided into multiple image blocks. Feature mutual information is calculated between every pair of image blocks to determine an object image block from among the divided blocks. Based on the identified object image block, material information associated with the object is extracted. This computational approach enables automated material information extraction through image analysis, providing an efficient method for identifying and characterizing objects in images without manual intervention.