Image Processing Apparatus Edge Clarification via Pixel Segmentation
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Solution Overview
Problem
Conventional image processing techniques fail to appropriately emphasize edges in images, particularly when pixel values in edge regions change gradually, making it difficult to detect black or white edges and clarify color boundaries.
Innovation Solution
An image processing apparatus and method that acquire target image data, specify candidate pixels for different colors, and perform a specific condition check to determine peripheral pixels, generating processed image data by using specified pixels to enhance edge detection and clarify color boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional edge detection methods are used to detect black or white edges, then the processing is simple and fast, but the edge cannot be appropriately emphasized when pixel values change gradually
Solution Approach 1:
The image processing is segmented into multiple stages: first identifying candidate first pixels based on color information, then determining peripheral pixels within a prescribed range, and finally identifying second pixels from the peripheral pixels. This segmentation allows complex edge emphasis to be broken down into manageable steps, improving detection accuracy without overwhelming system complexity.
Solution Approach 2:
The method performs preliminary identification of candidate first pixels and their peripheral pixels before final edge determination. By pre-identifying pixels within the prescribed range and evaluating their color conditions in advance, the system prepares data structures and candidate sets that facilitate accurate edge detection while avoiding redundant computations during the final processing stage.
2Measurement precision
If a large prescribed range is used to specify peripheral pixels, then more pixels are available for edge detection, but distant pixels are excessively influenced
Solution Approach 1:
The patent applies local quality by using a prescribed range that adapts to local image characteristics. The range is defined based on local color variations and pixel density, allowing the detection to focus on locally relevant pixels while excluding distant pixels that would introduce noise. This local adaptation ensures that edge detection accuracy is improved without excessive influence from distant pixels.
3Measurement precision
If all peripheral pixels are evaluated for color conditions, then comprehensive edge detection is achieved, but processing time increases
Solution Approach 1:
The method implements partial action by evaluating only those peripheral pixels that meet specific color conditions rather than all peripheral pixels. The system identifies candidate second pixels from the peripheral pixels based on color difference thresholds and other criteria, processing only the necessary subset of pixels required for accurate edge detection, thereby maintaining completeness while improving processing speed.
Data Source
AI summary
An image processing apparatus specifies, from among target pixels in a target image, first pixels related to a first color, and determines whether a specific condition is met for each peripheral pixel of the target pixel. The specific condition includes a condition that each peripheral pixel is a candidate for a pixel representing one of colors different from the first color. The apparatus specifies, as a second pixel related to a second color, each peripheral pixel meeting the specific condition. The apparatus performs an image process on target image data by using specified first and second pixels to generate processed image data. In a processed image, first processed pixels correspond to the specified first pixels and have the first color, and second processed pixels correspond to the specified second pixels and have the second color.