Raster to Vector Conversion Anchor Point Extraction
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Solution Overview
Problem
Conventional image processing techniques for converting raster images to vector images face challenges in precision and data size due to complex extraction rules, leading to incorrect or excessive anchor point extraction, especially when dealing with images of varying resolutions.
Innovation Solution
An image processing apparatus and method that simplifies anchor point extraction by using predetermined rules and priority orders to reduce anchor point candidates, thereby improving precision and reducing data size by comparing neighboring points and executing reduction processing based on attribute priorities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional extraction rules referring to multiple sides before and after a point of interest are used to extract anchor points, then extraction precision may be improved, but the complexity of processing increases in proportion to the number of sides to be referred to
Solution Approach 1:
The patent segments the complex extraction rules into two distinct stages: a candidate extraction stage that identifies potential anchor points using simple rules, and a reduction stage that refines the selection based on geometric relationships. This segmentation divides the originally complex single-stage process into manageable parts, reducing overall processing complexity while maintaining precision.
Solution Approach 2:
The patent extracts and isolates the essential geometric relationship (collinearity of three consecutive points) from the complex multi-side reference rules. By taking out only the critical condition for anchor point identification rather than referencing multiple sides, the system achieves accurate extraction with simpler processing rules.
2Device complexity
If extraction rules are simplified, then processing complexity is reduced, but extraction errors increase and anchor points may be incorrectly identified or excessively extracted
Solution Approach 1:
The patent performs preliminary extraction of anchor point candidates using simple rules before applying the reduction filter. This preliminary action ensures that all potential candidates are captured initially, and then the collinearity check removes false positives, thereby maintaining high reliability without requiring complex rules in the initial extraction phase.
Solution Approach 2:
The reduction process provides feedback by re-evaluating the extracted candidates against the collinearity condition. This feedback mechanism allows the system to correct extraction errors by identifying and removing points that do not satisfy the geometric relationship, thereby improving reliability while keeping the initial extraction rules simple.
3Reliability
If anchor points are excessively extracted without reduction processing, then no anchor points are missed, but data size increases
Solution Approach 1:
The patent changes the parameter used for anchor point selection from a simple count-based approach to a geometric relationship-based approach (collinearity). By using this parameter change in the reduction process, the system maintains extraction completeness for significant features while reducing the total number of points by removing redundant collinear points, thus decreasing data size.
4Productivity
If conventional vectorization methods are used for raster images, then vectorization can be achieved, but precision deteriorates when images are displayed at different resolutions
Solution Approach 1:
The patent creates an accurate geometric copy of the raster image features by identifying anchor points that preserve the essential shape characteristics through collinearity detection. This copying process produces vector data that faithfully represents the original image geometry, enabling high-quality rendering at any resolution without the precision deterioration associated with conventional methods.
Data Source
AI summary
An image processing apparatus comprises an anchor point candidate information extraction unit configured to decide coordinates of anchor point candidates and attributes of the anchor point candidates based on a plurality of predetermined extraction rules and a sequence of coordinate points that expresses an outline of image data; an anchor point decision unit configured to decide an anchor point candidate to be reduced based on the attributes of the anchor point candidates and priority orders set in advance for the attributes, and configured to decide anchor points by reducing the decided anchor point candidates to be reduced; a control point coordinate decision unit configured to decide control point coordinates based on the anchor points decided by the anchor point decision unit and the sequence of coordinate points; and a data output unit configured to output information including the coordinates of the decided anchor points and the decided control point coordinates.