Automated Image Evaluation System Using Neural Networks
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Solution Overview
Problem
Conventional image processing systems require manual expertise to set optimal processing parameters for image analysis, which is time-consuming and subjective, limiting the reliability and efficiency of image evaluation and analysis.
Innovation Solution
A computer-implemented method for automated image evaluation and processing parameter setting using machine learning algorithms, such as neural networks and numerical optimizers, to objectively assess image properties and adjust parameters independently of human intervention, enabling more accurate and efficient image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation by image quality experts is used to set processing parameters, then image quality assessment can be performed with human expertise, but the process becomes time-consuming and subjective
Solution Approach 1:
The system performs self-evaluation of image quality using automated algorithms that objectively assess image properties without requiring human experts. The image processing processor automatically evaluates processed images and adjusts parameters based on quantitative metrics, enabling the system to serve itself rather than relying on external human evaluation.
Solution Approach 2:
The patent replaces the manual mechanical process of human visual inspection with automated computational algorithms. Image quality assessment transitions from subjective human evaluation to objective machine-based measurement using processed image data and mathematical metrics, eliminating the time-consuming nature of manual review while maintaining or improving assessment accuracy.
2Reliability
If manual evaluation by image quality experts is used to set processing parameters, then expert knowledge can be applied, but the process becomes subjective and lacks consistency
Solution Approach 1:
The system objectively measures and adjusts processing parameters based on quantifiable image properties rather than subjective human judgment. By transforming qualitative expert assessment into quantitative parameter optimization, the system achieves consistent and reliable results that can be reproduced across different images and conditions, eliminating subjectivity while maintaining operational simplicity through automated algorithms.
3Productivity
If conventional image processing algorithms are used with fixed parameters, then processing speed is maintained, but image quality and analysis performance are limited
Solution Approach 1:
The patent transforms static fixed parameters into dynamic adaptive parameters that automatically adjust based on image content and analysis requirements. The image processing processor continuously evaluates processed images and modifies parameters in real-time, enabling the system to maintain high processing speed while achieving optimal image quality for each specific input image, rather than relying on predetermined fixed settings.
Data Source
AI summary
A computer-implemented method for automated image evaluation, a computer-implemented method for automated setting of processing parameters of an image processing processor, and a computer-implemented method for image processing of input images of an image sensor. An image processing system and an image analysis unit are also described.