Image Segmentation Error Weighting for Boundary Precision
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Solution Overview
Problem
Existing image segmentation techniques face challenges in accurately labeling pixels in images with blurry or irregular boundaries, particularly in print products where ink bleeding and scanning blurs make it difficult to determine precise region boundaries.
Innovation Solution
An information processing apparatus and method that utilize a learning model to estimate pixel regions, calculate errors between label data and estimation results, and update the learning model. The apparatus specifically calculates different error components for boundary and non-boundary regions, with controls to minimize the influence of boundary errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If error calculation is performed uniformly for all regions, then calculation simplicity is maintained, but boundary region errors negatively impact learning accuracy
Solution Approach 1:
The image is divided into boundary regions and non-boundary regions. The error calculation unit calculates first errors for boundary regions and second errors for non-boundary regions separately, then combines them with appropriate weighting. This segmentation allows the system to handle boundary uncertainties specifically while maintaining overall calculation structure.
Solution Approach 2:
Different error calculation strategies are applied to different regions of the image. Boundary regions use first error calculation with lower weighting to account for uncertainty, while non-boundary regions use second error calculation with higher weighting for precise labeling. This local differentiation improves overall learning accuracy.
2Measurement precision
If boundary region errors are given high influence, then boundary precision is improved, but learning stability deteriorates due to irregular boundaries
Solution Approach 1:
The system changes the parameter of error weighting based on region type. Boundary regions are assigned lower weighting coefficients while non-boundary regions receive higher weighting coefficients. This parameter adjustment balances the influence of different regions, preventing boundary irregularities from destabilizing learning while still maintaining boundary precision.
3Measurement precision
If uniform error weighting is used, then calculation simplicity is maintained, but region classification accuracy deteriorates in boundary areas
Solution Approach 1:
The error calculation is segmented into two distinct pathways: one for boundary regions calculating first errors, and another for non-boundary regions calculating second errors. This segmentation enables region-specific error handling that improves classification accuracy without overwhelming complexity.
Solution Approach 2:
The system performs preliminary region classification to identify boundary and non-boundary regions before error calculation. This preliminary action allows the error calculation unit to apply appropriate error types and weighting coefficients, improving overall classification accuracy.
Data Source
AI summary
A calculation unit calculates a first error for a boundary region of an image represented by image data, calculates a second error for a non-boundary region different from the boundary region, and calculates an error between label data and an estimation result based on the first error and the second error. And an influence of the first error on the calculation by the calculation unit is controlled to be smaller than an influence of the second error on the calculation by the calculation unit.