Image Processing System Using Color Saturation Metrics for Recognition Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image processing technologies, such as those used in mobile devices, face challenges in efficiently identifying and processing visual stimuli due to the need for extensive computational resources and the inability to autonomously determine appropriate recognition processes, leading to delayed and resource-intensive solutions.
Innovation Solution
The implementation of a system that uses color saturation and contrast metrics to discriminate between different image recognition processes, allowing for the activation of relevant agents such as barcode decoders or facial recognition, and throttling processes based on success and user interest, to efficiently allocate resources and improve processing speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple recognition processes are applied to all input imagery, then recognition accuracy is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The system changes parameters by analyzing color saturation metrics of input imagery to dynamically select which recognition processes to apply. By measuring color saturation and comparing it against thresholds, the system determines whether to apply OCR, barcode reading, or other recognition processes, thereby avoiding unnecessary processing while maintaining recognition accuracy for relevant imagery types.
2Reliability
If multiple recognition processes are applied to all input imagery, then recognition accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The system uses color saturation as a key parameter to control computational resource allocation. By evaluating the color saturation metric and comparing it to predefined thresholds, the system selectively activates only those recognition processes that are likely to succeed, thereby reducing overall computational resource consumption while maintaining recognition accuracy for appropriate image types.
3Productivity
If color saturation analysis is used to select recognition processes, then processing efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments the image processing workflow into distinct stages: first analyzing color saturation metrics, then using those metrics to select appropriate recognition processes. This segmentation allows the system to add functionality in a modular way, where each component (color analysis, threshold comparison, process selection) is relatively simple, reducing overall system complexity while improving processing efficiency.
Data Source
AI summary
Image data, such as from a mobile phone camera, is analyzed to determine a colorfulness metric (e.g., saturation) or a contrast metric (e.g., Weber contrast). This metric is then used in deciding which of, or in which order, plural different image recognition processes should be invoked in order to present responsive information to a user. A great number of other features and arrangements are also detailed.