Image Processing Apparatus Region Segmentation Vectorization
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Solution Overview
Problem
Existing vectorizing methods are inadequate for handling changes in line thickness and character size, leading to inefficient data representation and quality deterioration in digital images, particularly when applying similar techniques to characters with different fonts and sizes.
Innovation Solution
An image processing apparatus that includes region segmentation, outlining, and function approximation units, which selectively apply thinning or Bezier approximation processing based on region attributes to generate suitable outline data for character and drawing regions, optimizing vector data generation for individual images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If contour vector data is generated for all drawings, then magnification-varying processing can be performed, but the data cannot be efficiently reused when only line thickness or length needs to be changed
Solution Approach 1:
The patent segments the drawing data into two distinct types: contour vector data for shape preservation and linear vector data for thickness/length modification. This segmentation allows each data type to be optimized for its specific purpose, enabling efficient reuse without requiring complete redrawing when only thickness or length changes are needed.
Solution Approach 2:
The patent applies different vectorization methods to different parts of the drawing based on their characteristics. Character regions are processed differently from drawing regions, and within drawing regions, areas suitable for thinning processing are identified and handled separately from those that require contour preservation. This local differentiation optimizes data representation for specific modification needs.
2Ease of manufacture
If similar vectorizing methods are applied to all characters, then processing is simplified, but character shape quality deteriorates and data size increases considerably
Solution Approach 1:
The patent segments character processing into two categories based on size: small characters (equal to or smaller than a predetermined size) and large characters. Different vectorization methods are applied to each category, with small characters using one approach and large characters using another, optimizing both quality and data efficiency.
Solution Approach 2:
The patent applies different function-approximation methods (straight-line approximation vs. Bezier approximation) to different character sizes. This local differentiation ensures that small characters use the appropriate simplification method while large characters receive the more precise approximation, optimizing both shape quality and data size reduction.
3Manufacturing precision
If function-approximation processing is applied to all character images, then vector data is generated, but processing time increases and quality deteriorates for small characters
Solution Approach 1:
The patent applies function-approximation processing selectively only to large characters (greater than predetermined size) rather than all characters. Small characters are processed using a different, faster method, avoiding unnecessary processing time and quality deterioration that would result from applying complex approximation to all character sizes.
Data Source
AI summary
An input image is divided into a plurality of regions, and it is determined whether each of the divided regions is suitable for thinning processing. In accordance with a result of the determination, an outlining processing is selected to generate outline data (vector data) for each of the regions. The generated outline data is output. For example, a character region and a drawing region are discriminated from each other and outline data having a format suitable for a discriminated type of region is generated. In addition, generation of outline data (vector data) which pass through a center line of the line drawing or generation of outline data (vector data) indicating a contour of a drawing is automatically selected. Furthermore, in accordance with the size of a character, function-approximation processing may be selected.