Foreground Background Image Segmentation Using Line Segment Correction
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Solution Overview
Problem
Existing image processing technologies face challenges in accurately and efficiently dividing foreground and background areas in images, particularly when pixel values are close, leading to errors and requiring numerous control points and modification steps, which increases user effort and operating complexity.
Innovation Solution
An image processing apparatus that uses a line segment input method for modifying erroneous divisions, creating and updating foreground and background models based on sampled pixel values, and employing algorithms like graph cuts for energy minimization to refine the separation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automatic division is conducted using distribution models created from sampled pixel values, then the system can automatically separate foreground and background, but errors occur in areas where pixel values are close (e.g., front tomato foreground vs. rear tomato background)
Solution Approach 1:
The patent segments the image into multiple regions based on depth information and divides the automatic division process into stages: initial automatic division, error detection in specific regions, and targeted manual correction. This allows the system to maintain high automation while addressing precision issues in challenging areas where pixel values are similar.
Solution Approach 2:
The system implements feedback by detecting division errors in automatically separated regions and providing mechanisms for user correction. The error detection feedback loop identifies areas where foreground and background pixel values are close, allowing the system to learn from user corrections and improve future automatic divisions.
2Manufacturing precision
If users manually modify erroneous areas by inputting markers and re-creating distribution models, then division accuracy can be improved, but the number of control points and modification steps increases
Solution Approach 1:
The patent extracts only the problematic regions requiring manual correction from the entire image. Instead of requiring users to modify all areas, the system identifies and isolates specific erroneous regions where automatic division failed, allowing users to focus correction efforts only on these extracted problem areas.
Solution Approach 2:
The system applies partial action by requiring users to provide correction information only for erroneous regions rather than the entire image. This partial correction approach reduces the number of control points and modification steps needed while still achieving high division accuracy in critical areas.
3Manufacturing precision
If users input markers for modification and the system re-divides using updated distribution models, then erroneous areas can be corrected, but user fatigue increases due to repeated operations
Solution Approach 1:
The system performs preliminary action by automatically detecting and identifying erroneous regions before requiring user input. This pre-processing step prepares the image data by marking only the problematic areas, so users don't need to manually scan the entire image for errors, reducing operational burden and fatigue.
Solution Approach 2:
The system implements self-service by automatically detecting division errors and presenting them to users for correction. Instead of requiring users to manually identify all errors, the system autonomously detects erroneous regions and prepares correction prompts, allowing users to focus only on providing correction information for identified areas.
Data Source
AI summary
An image processing apparatus is disclosed, including a modification part, a model creation part, and a division part. The modification part instructs modification of a portion of a foreground area or a background area which is erroneously divided as a result of dividing predetermined image data into the foreground area being an extraction target and the background area being an out-of-extraction target. The model creation part creates a foreground model of the foreground area and a background model of the background area which are modified, based on first pixel values of a predetermined line segment where the modification is instructed and second pixel values of the foreground area and the background area which are divided. The division part divides the image data into the foreground area and the background area by using the foreground model and the background model which are created by the model creation part.