Hyperspectral Machine Vision with Adaptive Color Primitives
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Solution Overview
Problem
Conventional machine vision systems are limited by using only three color primitives, similar to human vision, which are inadequate for distinguishing materials, and hyperspectral imaging techniques are not effectively utilized to enhance color discrimination for robotic tasks.
Innovation Solution
A neural network-based method and system that initializes multiple chromatic primitives, optimizes sensitivity functions, and generates new artificial color values to improve color discrimination by reshaping hyperspectral signals and iteratively training the network to detect target materials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine vision systems use three color primitives (RGB) similar to human vision, then the system is simple and easy to operate, but the color discrimination capability is insufficient for distinguishing materials
Solution Approach 1:
The system dynamically determines the optimal number of primitives (k) based on the specific task and material discrimination requirements, rather than using a fixed number. This allows the system to adapt the complexity of the vision system to the actual needs, improving discrimination capability when necessary while maintaining simplicity when sufficient.
Solution Approach 2:
The invention changes the parameter of the number of primitives from the conventional fixed value of 3 (RGB) to a variable k that can be optimized for specific tasks. By treating the number of primitives as a可调 parameter rather than a fixed constraint, the system achieves better material discrimination while managing complexity through optimization.
2Measurement precision
If hyperspectral imaging techniques use narrow bands to collect data, then the spectral information is detailed, but the system does not create an effective color discriminator for detecting objects
Solution Approach 1:
The system extracts the most discriminative spectral information from hyperspectral data by learning optimal spectral response functions that act as artificial color primitives. Instead of using all narrow band data directly, it extracts the essential discriminatory features into a compact set of k primitives that are effective for object detection and material discrimination.
Solution Approach 2:
The invention introduces learned spectral response functions as intermediary components that transform detailed hyperspectral data into effective color discriminators. These intermediaries bridge the gap between raw spectral information and practical object detection, creating artificial color primitives that are optimized for specific detection tasks.
3Measurement precision
If the number of primitives is increased beyond three to improve material discrimination, then the discrimination capability improves, but determining the optimal number becomes practically impossible
Solution Approach 1:
The system uses a loss function that provides feedback on material discrimination performance to guide the optimization of the number of primitives. By evaluating discrimination accuracy and using this feedback to adjust the number of primitives, the system can automatically determine the optimal k for specific tasks without requiring manual trial and error.
Solution Approach 2:
The invention performs preliminary optimization to determine the optimal number of primitives before actual detection tasks. By pre-determining the optimal k based on the specific application and material types, the system avoids the complexity of dynamically adjusting the number of primitives during operation while still achieving optimal discrimination capability.
Data Source
AI summary
Embodiments herein provide a method and system for a hyperspectral artificial vision for machines. The system receives a hyperspectral signal of a target material as an input to a neural network model. The system initializes by selecting the number of primitive layers to be used. The system iteratively cycles through all training data (pixels) and updating weights for each unsuccessful material class prediction. Model with two primitives serves as baseline, after which the system adds another primitive layer and repeats the training procedure. The system keeps repeating these processes until obtains convergence. Where the system come to a halt, the system obtains the optimal number of primitives for the given materials. The generated new color pixel is used as a discriminator to aid in locating the target material. The new artificial color is a mixture of weighted chromatic primitives which are optimized for sensitivity/(Spectral Response Functions) SRFs.