Edge Pixel Specification via Component Data Binarization
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Solution Overview
Problem
Existing image processing techniques inaccurately specify edge pixels due to density differences, leading to incorrect identification of non-edge pixels as edge pixels, which reduces the accuracy of edge pixel specification.
Innovation Solution
An image processing apparatus and method that generates first and second component data representing maximum and minimum pixel values, calculates edge strengths, and specifies edge pixels by binarizing edge strength data, thereby improving the accuracy of edge pixel identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional edge extraction using Gaussian filter and differentiation filter is applied, then edge detection is performed, but density differences in uniform color portions cause erroneous specification of non-edge pixels as edge pixels
Solution Approach 1:
The patent segments the RGB image into three separate component images (R, G, B channels), processes each component independently to calculate edge strengths, and then integrates the results. This segmentation allows the system to handle density differences in uniform color portions more effectively by analyzing edge characteristics across multiple color components rather than relying on a single composite image, thereby reducing erroneous edge pixel specification.
2Productivity
If threshold processing is applied to edge strength image, then edges are extracted, but pixels in uniform portions with density differences are incorrectly identified as edges
Solution Approach 1:
The patent merges the edge strength information from three separate component images by calculating the sum of edge strengths across R, G, and B channels. This merging approach creates a more robust edge strength map that distinguishes true edges (which appear consistently across multiple components) from false edges caused by density variations in uniform portions (which appear randomly in single components), thereby improving identification accuracy while maintaining extraction efficiency.
Data Source
AI summary
An image processing apparatus performs: acquiring target image data representing a target image including a plurality of pixels, the target image data including a plurality of pixel values each having a plurality of component values; generating image data, the image data being one of first and second component data respectively including a plurality of first and second pixel values, each of the plurality of first and second pixel values being respectively related to maximum and minimum values among the plurality of component values of corresponding one of the plurality of pixels; calculating a plurality of edge strengths corresponding to respective ones of the plurality of pixels using the image data to generate edge strength data including the plurality of edge strengths; and specifying a plurality of edge pixels included in the target image. The specifying includes binarizing the edge strength data to generate binary image data.