Mark Position Detection Using Inner and Outer Edge Midpoints
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Solution Overview
Problem
Conventional image forming apparatuses face challenges in accurately detecting the reference position of marks on recording media when the marks are distorted due to bleeding or deformation, leading to significant deviations from the original position.
Innovation Solution
The apparatus employs a first detecting unit to identify positions of outer and inner edges in both directions, and a second detecting unit to calculate the reference position as the midpoint of line segments connecting the intersections of inner and outer edges, thereby stabilizing the detection even under distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional edge detection methods are used to detect mark positions, then the detection process is simple, but the detected reference position deviates significantly when the mark is distorted due to bleeding or deformation
Solution Approach 1:
The detection process is segmented into multiple distinct steps: detecting outer edges, detecting inner edges, identifying line segments for both edge types, calculating intersections, and finally computing the midpoint. This segmentation allows each step to be optimized independently, improving overall accuracy while maintaining manageable complexity through systematic breakdown of the detection task.
Solution Approach 2:
The invention transitions from detecting only outer edges (single dimension of edge detection) to detecting both outer edges and inner edges (adding another dimensional layer of edge detection). By incorporating inner edge detection and using both sets of edges to define line segments, the method adds a dimensional layer to the detection process that provides redundancy and improves accuracy against distortion.
2Measurement precision
If only outer edges are detected for mark position detection, then the detection process is straightforward, but accuracy deteriorates when marks are distorted
Solution Approach 1:
The invention applies different detection qualities to different parts of the mark: outer edges are detected to define the overall mark boundary, while inner edges are detected to provide internal reference points. This local differentiation in edge detection quality allows the system to maintain high accuracy even when parts of the mark are distorted, as the inner edges provide stable reference points unaffected by outer boundary variations.
Solution Approach 2:
The method adds inner edge detection as an additional dimensional layer to the traditional outer edge-only detection. By incorporating both outer and inner edge information, the system creates a more robust detection framework that can withstand distortion, transforming a single-dimensional detection approach into a multi-dimensional one that provides redundancy and improved accuracy.
3Measurement precision
If regression calculation is performed on detected line segments, then edge position can be obtained, but the reference position deviates when marks are distorted in specific manners
Solution Approach 1:
The detection methodology is segmented to identify and process different types of line segments separately: those connecting outer edges and those connecting inner edges. By segmenting the line segment identification process and treating outer and inner edge connections differently, the system can compute intersections from each segment type and use their midpoint, providing reliability under distortion through this segmented approach.
Solution Approach 2:
The invention adds another dimensional layer by incorporating both outer edge line segments and inner edge line segments into the intersection calculation. Instead of relying solely on outer edge segments, the method computes intersections from both outer and inner edge segments and takes their midpoint, creating a multi-dimensional reference system that maintains reliability even when marks are distorted in specific manners.
Data Source
AI summary
An image forming apparatus is configured to detect, from image data of a recording medium on which a plurality of marks are printed, positions of outer edges being edges closer to ends of the image data and positions of inner edges being inside edges not closer to the ends of the image data in both of a first direction and a second direction different from the first direction; identify, with respect to a target mark for which a reference position is to be detected and two marks adjacent to the target mark, first line segments connecting positions of inner edges and second line segments connecting positions of outer edges; and detect, as a reference position of the target mark, a midpoint of a line segment connecting an intersection of the two first line segments and an intersection of the two second line segments.