Background Segmentation Error Correction Through Spatial Image Substitution
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Solution Overview
Problem
Conventional background segmentation systems suffer from errors due to erroneous sensor data, which can lead to incorrect control commands for robots and other systems, resulting in unintended movements.
Innovation Solution
A system that replaces erroneous image element coding information in a first image with spatially corresponding, non-erroneous coding information from a second image, using a processor to determine and associate substitute coding information to improve the reliability of background segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional background segmentation systems use sensor data directly, then processing is simple and fast, but errors occur in background segmentation leading to incorrect control commands
Solution Approach 1:
The system performs preliminary error detection and replacement of erroneous image element coding information before background segmentation processing. By identifying and correcting errors in advance using multiple imaging devices, the system prevents erroneous data from propagating through the segmentation process, thereby improving reliability without significantly increasing overall system complexity.
Solution Approach 2:
The system introduces an intermediary processing step that acts as a mediator between raw sensor data and background segmentation. This intermediary layer detects erroneous coding information and replaces it with valid data from other imaging devices, serving as a buffer that enhances data quality before the main segmentation process occurs.
2Reliability
If multiple imaging devices are used to replace erroneous data, then reliability of background segmentation improves, but device complexity increases
Solution Approach 1:
The system merges data from multiple imaging devices by combining valid image element coding information to replace erroneous data. By integrating data from multiple sources and using spatial correspondence to match pixels across devices, the system achieves improved reliability through data fusion rather than simply adding more devices, thereby managing complexity through intelligent combination rather than brute-force redundancy.
3Reliability
If erroneous image element coding information is replaced early in the process, then reliability of background segmentation is enhanced, but processing time increases
Solution Approach 1:
The system performs error detection and replacement as a preliminary step before main background segmentation processing. By identifying and correcting erroneous coding information early using data from multiple imaging devices, the system prevents reprocessing and iterations later, which actually reduces total processing time despite the initial overhead of early error correction.
Solution Approach 2:
The system rapidly processes error detection and replacement by using spatial correspondence to directly substitute erroneous pixels with valid data from other imaging devices. This approach skips complex error analysis and correction algorithms, rushing through the error correction phase efficiently by leveraging the redundancy already present in multiple simultaneous imaging device captures.
Data Source
AI summary
A system includes a processor configured to determine a first image of a first capture area including one first image element associated with first image element coding information classified as erroneous; determine a second image of a second capture area, the first capture area and the second capture area overlapping in an overlap area; determine a second image element spatially corresponding to the first image element in the second image, wherein the first image element and the second image element represent a part of the overlap area; determine substitute image element coding information for the first image element of the first image using second image element coding information associated with the second image element of the second image that is not classified as erroneous; and associate the determined substitute image element coding information with the first image element of the first image to obtain a first substitute image.