Saliency Prioritization for Image Pixel Anomaly Correction
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Solution Overview
Problem
Conventional pixel error correction in images relies heavily on human inspectors, making it expensive and time-consuming, and lacks an efficient automated method to prioritize corrections based on the significance of pixel anomalies within an image.
Innovation Solution
The development of automated saliency prioritization systems using neural networks for identifying salient regions in images, such as character and foreground object masking and attention mapping, to prioritize pixel anomalies based on their location and attention scores, allowing for automated flagging and potential disregard of non-salient anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human inspectors are used for pixel error correction, then correction accuracy is maintained, but processing time and cost increase significantly
Solution Approach 1:
The patent replaces the mechanical human inspection system with an automated computational system that uses machine learning models and algorithms to detect and prioritize pixel errors. This substitution maintains detection accuracy while dramatically reducing processing time and eliminating the need for manual human review.
Solution Approach 2:
The system enables self-service by automatically detecting, analyzing, and prioritizing pixel errors without requiring human intervention. The automated pipeline includes error detection, saliency assessment, and prioritization algorithms that work independently to identify and rank errors based on their visual impact.
2Manufacturing precision
If all pixel anomalies are corrected, then image quality is maximized, but processing resources and time are wasted on insignificant errors
Solution Approach 1:
The patent applies local quality by differentiating between salient and non-salient regions of an image. Instead of treating all pixel errors uniformly, the system assesses each error's location and visual impact, prioritizing correction of errors in salient areas (such as foreground objects or regions attracting human attention) while deprioritizing or ignoring errors in non-salient background areas.
Solution Approach 2:
The system performs partial action by correcting only the most significant pixel errors rather than attempting to correct all anomalies. The saliency-based prioritization identifies a subset of errors that have the greatest visual impact, allowing the system to achieve acceptable image quality with reduced processing effort.
3Productivity
If automated systems are implemented for pixel error detection, then processing speed increases, but system complexity increases
Solution Approach 1:
The patent segments the complex task of pixel error correction into distinct modular components: error detection module, saliency assessment module, prioritization module, and correction module. Each component performs a specific function and can be independently optimized, maintained, and processed, reducing overall system complexity while maintaining high processing speed.
Data Source
AI summary
According to one implementation, a system includes a computing platform having a hardware processor and a system memory storing a software code including a trained neural network (NN). The hardware processor executes the software code to receive an input image including a pixel anomaly, identify, using the trained NN, one or more salient regions of the input image, and determine whether the pixel anomaly is located inside any of the one or more salient regions. The hardware processor further executes the software code to assign a first priority to the pixel anomaly when it is determined that the pixel anomaly is located inside any of the one or more salient regions, and to assign a second priority, lower than the first priority, to the pixel anomaly when it is determined that the pixel anomaly is not located inside any of the one or more salient regions.