Combining Independent Image Processing Algorithms via Iterative Fitness Evaluation
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Solution Overview
Problem
Existing image and video processing algorithms often produce varying results due to factors like object motion and lighting, and there is a need for a method to optimize the combination of these algorithms for improved performance in tasks such as background segmentation and highlight detection.
Innovation Solution
A method that involves receiving a training set of input images or videos, applying multiple base algorithms, and using a combination of operations to generate and evaluate different combining algorithms, selecting an optimized algorithm based on fitness scores to achieve better performance in tasks like background segmentation and highlight detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple base algorithms are applied to perform the same image or video processing task, then the reliability of the processing result is improved, but the device complexity increases
Solution Approach 1:
The patent segments the algorithm combination process into multiple generations, where each generation refines the combining algorithm through iterative evaluation and selection. This divides the complex task of finding the optimal algorithm combination into manageable stages, improving reliability while controlling complexity.
Solution Approach 2:
The patent changes parameters of the combining algorithms across generations, including selection criteria, evaluation metrics, and combination strategies. By systematically adjusting these parameters, the system improves processing reliability while managing the complexity through controlled parameter variation rather than unbounded algorithmic complexity.
2Manufacturing precision
If multiple base algorithms are combined with multiple operations, then the manufacturing precision of the processing solution is improved, but the device complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining a set of operations that can be applied to base algorithm results. These operations are prepared in advance and systematically applied across generations, improving processing precision while managing complexity through pre-planned operation sets rather than ad-hoc complex combinations.
Solution Approach 2:
The patent introduces dynamics by allowing the combining algorithm to evolve across generations, where the structure and operations of the combining algorithm change adaptively based on performance feedback. This dynamic evolution improves processing precision while managing complexity through iterative refinement rather than static complex structures.
3Reliability
If a comprehensive evaluation of combining algorithms is performed, then the reliability of the optimized algorithm selection is improved, but the loss of time increases
Solution Approach 1:
The patent implements periodic action by evaluating combining algorithms at discrete generation intervals rather than continuously. Each generation represents a periodic evaluation cycle where algorithms are assessed and refined. This periodic approach maintains selection reliability while reducing time loss by avoiding continuous evaluation.
Solution Approach 2:
The patent maintains continuity of useful action by iteratively improving combining algorithms across generations, where each generation builds upon previous results. This continuous iterative process improves selection reliability over time while managing evaluation time through progressive refinement rather than exhaustive re-evaluation at each step.
Data Source
AI summary
An algorithm for performing an image or video processing task is generated that may be used to combine a plurality of different independent solutions to the image or video processing task in an optimized manner. A plurality of base algorithms may be applied to a training set of images or video and a first generation of different combining algorithms may be applied to combine the respective solutions from each of the respective base algorithms into respective combined solutions. The respective combined solutions may be evaluated to generate respective fitness scores representing measures of how well the plurality of different combining algorithms each perform the image or video processing task. The algorithms may be iteratively updated to generate an optimized combining algorithm that may be applied to an input image or video.