Shadow Edge Continuity Detection for Accurate Document Boundary Sensing
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Solution Overview
Problem
Existing electronic skew correction techniques in image forming apparatuses have lower accuracy in detecting the boundary between an object and the background member due to changes in shadow width based on document thickness or illumination angle.
Innovation Solution
An image processing device that includes a processor to capture image data, sequentially selects target pixels, calculates edge amounts, determines edge continuity, and detects boundary positions using edge continuity numbers to accurately identify the boundary between the object and background member, irrespective of shadow width changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional luminance difference thresholding is used to detect boundary positions, then the detection process is simple and fast, but the detection accuracy decreases when shadow width changes due to document thickness or illumination angle variations
Solution Approach 1:
The patent segments the shadow region into multiple regions along the shadow width direction and performs luminance difference calculations for each segment. By dividing the shadow detection into multiple discrete measurement points rather than a single threshold check, the system achieves more accurate boundary position detection that is insensitive to overall shadow width variations caused by document thickness or illumination angle changes.
2Measurement precision
If the detection method adapts to different shadow widths, then detection accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent performs luminance difference calculations at multiple discrete positions within the shadow region, using more measurements than the minimum single-point threshold check. This partial sampling approach across the shadow width provides sufficient accuracy for boundary detection without requiring exhaustive analysis of every pixel, thus balancing detection precision with processing efficiency.
3Measurement precision
If multiple luminance difference calculations are performed at different distances, then detection accuracy improves, but the computational load increases
Solution Approach 1:
The patent applies different detection strategies to different local regions within the shadow. By performing luminance difference calculations at specific discrete positions rather than uniformly across the entire shadow region, the system optimizes computational resources by focusing measurements where they provide the most information for boundary detection, reducing overall computational load while maintaining accuracy.
Data Source
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AI summary
An image processing device (120) includes a processor (208) including an edge amount calculation unit (310) that calculates an edge amount (S101) in a shadow region (402) in image data; an edge determination unit (320) that determines whether the target pixel corresponds to an edge; an edge continuity determination unit (330) that determines whether the edge is a part of a continuous edge; an edge continuity counter (340) that counts an edge continuity number for the target pixel (S104); and a boundary position detection unit (350). The boundary position detection unit (350) counts the edge continuity number for a previous target pixel and determines whether the edge continuity number for the previous target pixel is greater than a preset number (S 105); and detects a boundary position, based on the edge amount of each of the target pixels forming the continuous edge up to the previous target pixel (S106).