Material Detection via Image Segmentation and Neural Networks
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Solution Overview
Problem
Existing material detection methods from images are unreliable and require significant computational resources, often failing to accurately identify materials, especially in complex scenarios where objects are covered by various materials.
Innovation Solution
A computer-implemented method that involves receiving a pattern image of an object, manipulating it to generate a partial image, and using a data-driven model trained on partial images and material information to extract the material information from the partial image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If filtering techniques are used to analyze beam profile for material detection, then material detection can be performed, but intensive computing resources are required and reliability is insufficient
Solution Approach 1:
The patent segments the image into multiple patches and processes each patch independently through the neural network. This divides the complex task of analyzing the entire image into smaller, more manageable units, reducing the computational burden while maintaining detection reliability through aggregated results from multiple patches.
Solution Approach 2:
The patent replaces traditional filtering techniques and beam profile analysis with a data-driven neural network approach. This substitution transitions from conventional signal processing methods to a learned model that automatically extracts relevant features, improving reliability while the patch-based processing keeps computational requirements manageable.
2Measurement precision
If complete pattern images are used for material information extraction, then comprehensive material analysis is possible, but computational resources and training data requirements increase significantly
Solution Approach 1:
The patent divides complete pattern images into smaller patches for processing. This segmentation reduces the computational complexity of analyzing entire images while maintaining measurement precision through the neural network's ability to learn relevant features from multiple patches, which are then aggregated to form comprehensive material information.
Solution Approach 2:
The patent processes only partial portions (patches) of the complete image rather than the entire image at once. This partial action approach reduces computational resource requirements and training data needs while still achieving accurate material information extraction by combining results from multiple patches.
3Reliability
If background information is included in the pattern image for material detection, then complete scene context is available, but the variable background increases complexity and reduces detection reliability
Solution Approach 1:
The patent extracts and processes only relevant patches from the complete image, effectively separating the object of interest from the variable background. By focusing computational resources on specific regions containing material information rather than processing the entire scene including background, the system improves material identification reliability while reducing complexity related to background variability.
Data Source
AI summary
Disclosed herein is a method for extracting material information of an object from a pattern image of the object. The pattern image showing the object while the object is illuminated with a light pattern is received. The pattern image is manipulated to generate a partial image from the pattern image. Material information of the object is extracted from the partial image by providing the partial image to a data-driven model. The data-driven model is parametrized according to a training data set including partial images and material information. The extracted material information is provided.