Pixel-Wise Image Segmentation Using Color Triangle Sectors
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Solution Overview
Problem
Existing image segmentation methods face challenges in achieving real-time processing due to high computational demands, especially in dynamic scenes, and struggle with reliable color estimation in low brightness areas.
Innovation Solution
A pixel-wise image segmentation method that utilizes a color triangle to classify pixels based on color and brightness, employing a hierarchical classification table to reduce memory usage and enable on-the-fly processing, with the color triangle divided into sectors adjusted to human color perception for efficient segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image segmentation methods are used to achieve accurate color and brightness classification, then segmentation precision is improved, but computational complexity increases making real-time processing difficult
Solution Approach 1:
The color space is segmented into a color triangle with multiple sectors (e.g., 9, 15, or 36 divisions) representing different color categories. This segmentation allows the system to classify pixels into discrete color bins rather than continuous color values, reducing computational complexity while maintaining segmentation precision. The hierarchical classification table further segments the classification process into multiple levels for efficient lookup.
Solution Approach 2:
The classification table is pre-computed and stored in memory before runtime. This preliminary action converts complex color space calculations into simple table lookups during image segmentation, dramatically reducing real-time computational requirements while preserving accurate color and brightness classification.
2Measurement precision
If detailed color classification with many sectors is used, then segmentation precision is improved, but memory usage increases
Solution Approach 1:
The color triangle is divided into a manageable number of sectors (9, 15, or 36) rather than using continuous color space. This segmentation provides sufficient color classification precision for most applications while keeping the classification table size reasonable for memory storage.
Solution Approach 2:
Different regions of the color triangle are divided into different numbers of sectors based on human color perception requirements. Areas where human eyes are more sensitive to color differences have finer segmentation, while less sensitive areas have coarser segmentation. This local quality approach optimizes precision where needed while minimizing memory usage elsewhere.
3Measurement precision
If comprehensive color and brightness analysis is performed on all pixels, then segmentation accuracy is improved, but processing speed decreases
Solution Approach 1:
The classification table is pre-computed offline, converting complex color and brightness analysis into simple index lookups. During real-time image segmentation, each pixel is classified by quickly looking up its color and brightness values in the pre-computed table, maintaining high segmentation accuracy while achieving real-time processing speeds.
Solution Approach 2:
The color space is segmented into discrete sectors, allowing the use of discrete indexing rather than continuous computation. This segmentation enables efficient array-based lookup operations that are much faster than traditional continuous color space calculations, achieving both accuracy and speed.
Data Source
AI summary
Image segmentation utilizing the color and brightness of sections of the image to generate super-pixels is described herein. The image segmentation is implemented pixel-wise. A color triangle is utilized in the classification of the pixels. By using a hierarchical classification table, the memory usage is significantly reduced, and the image segmentation is able to be performed on-the-fly in real-time.