Dynamic Classified Image Generation via Sequential Classifier Display
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Solution Overview
Problem
Existing image classification systems often misclassify image regions, leading to a need for improved accuracy.
Innovation Solution
A dynamic classification system that applies multiple classifiers to an input image, displaying their outputs sequentially to a human observer, creating a dynamic classified image that encourages the human visual system to correct occasional errors and retain correct classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single classifier is used for image classification, then the device complexity is low, but the classification accuracy deteriorates due to misclassification errors
Solution Approach 1:
The patent divides the classification task into multiple independent classifiers, each handling a portion of the classification work. Multiple classifiers process the same input image and generate separate classified images, which are then combined through temporal integration. This segmentation allows the system to achieve higher accuracy by distributing the classification burden across multiple specialized units rather than relying on a single complex classifier.
Solution Approach 2:
The patent merges the outputs of multiple classifiers by displaying them sequentially to a human observer. The human visual system integrates the information from multiple classified images presented in rapid succession, combining the results to produce a final classification that is more accurate than any single classifier could achieve alone. This merging leverages human cognitive processing as part of the classification system.
2Measurement precision
If multiple classifiers are applied to improve classification accuracy, then the classification accuracy improves, but the computational speed deteriorates
Solution Approach 1:
The patent employs periodic action by displaying multiple classified images in rapid sequential succession rather than processing them all simultaneously. The classifiers operate in a time-multiplexed manner, with each classifier's output displayed for a brief period. This periodic presentation allows the system to leverage the persistence of human visual perception, integrating information over time without requiring simultaneous computational processing of all classifiers.
Solution Approach 2:
The patent substitutes mechanical/computational integration with human visual integration. Instead of using a computer to algorithmically combine and process outputs from multiple classifiers simultaneously, the system displays the classified images to a human observer whose visual system performs the integration. This substitution transfers the computational burden of merging results from the machine to the human observer, significantly improving processing speed while maintaining accuracy.
3Measurement precision
If multiple classifiers are used to produce multiple classified images, then the classification accuracy improves through error correction, but the ease of operation deteriorates due to sequential display requirements
Solution Approach 1:
The patent implements self-service by allowing the human visual system to automatically perform the integration of multiple classified images without requiring active user intervention. The sequential display is designed so that the human observer's natural visual processing and memory capabilities automatically combine the information from multiple images. The system leverages inherent human cognitive functions rather than requiring users to manually compare, analyze, or synthesize the multiple classified images, thereby maintaining ease of operation while achieving improved accuracy.
Data Source
AI summary
The systems and methods can apply a plurality of different classifiers to a given input image instead of a single classifier. The plurality of classifiers produce a plurality of classified images based on the input image, which are sequentially displayed to a human observer. The sequential display of the classified images produces a dynamic classified image, in which the classification of the input image varies with time depending on which one of the classified image is displayed at a given instant. The dynamic classified image provides dynamic stimuli that encourages the human visual system to excuse occasional classification errors from a minority of the classified images and to retain generally correct classifications from a majority of the classified images.