Image Segmentation via Luminance Histogram Analysis
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Solution Overview
Problem
Existing image segmentation techniques struggle to accurately segment areas in images due to luminance differences and brightness unevenness, often requiring user input for precise area specification, which can lead to adverse results, especially when the extraction target has a complicated shape.
Innovation Solution
An image processing device and method that determines pixel values based on a pixel value histogram to segment images into foreground and background, using positional information to differentiate between background and foreground pixels, allowing for precise area extraction without user auxiliary operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If image segmentation is performed using conventional techniques (e.g., Grab Cut), then segmentation processing can be performed with reduced user burden, but the extracted area may not be precisely specified and incorrect specifying may create adverse results
Solution Approach 1:
The system performs automatic image segmentation by analyzing luminance histograms and determining background pixels without requiring user input. The control section automatically identifies the extraction target area by detecting luminance peaks and classifying pixels, enabling the system to serve itself rather than relying on user specification operations.
2Ease of operation
If a predetermined-shaped area (e.g., circular) is created including a tapped coordinate point, then segmentation processing can be performed simply, but complicated shaped extraction targets may not be entirely included
Solution Approach 1:
The image is divided into multiple regions (first region with luminance peak, second region with other luminance values) based on histogram analysis. This segmentation approach allows the system to identify background areas and extraction targets without being constrained by predetermined shapes, thereby completing the extraction of complicated shaped targets.
Solution Approach 2:
The system changes the parameter basis for area determination from fixed geometric shapes to luminance-based pixel classification. By using luminance histogram analysis and peak detection, the system dynamically determines extraction areas that adapt to the actual content shape rather than forcing content into predetermined geometric forms.
3Reliability
If luminance differences and brightness unevenness are present in the image, then image segmentation becomes more difficult, but conventional techniques still require user input for area specification
Solution Approach 1:
The manual mechanical operation of user tapping and area specification is replaced with an automated optical analysis system. The control section analyzes luminance distributions, detects histogram peaks, and automatically determines extraction areas, substituting the mechanical user interaction system with an automated image analysis system that handles luminance variations.
Data Source
AI summary
An image processing device of the present invention determines the pixel values of background pixels based on a pixel value histogram of a target image to be segmented into a foreground and a background, determines pixels of the target image having pixel values equivalent to the pixel values of these background pixels, as a portion of the background pixels, and determines other background pixels and foreground pixels in the target image by use of the pixel value of each pixel in the target image and the positional information of this portion of the background pixels.