Image Processing Circuit Edge Detection via Selective Pixel Analysis
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Solution Overview
Problem
Conventional Sobel masks for edge detection in images are inaccurate due to reliance on limited pixel data, leading to errors in straight line detection, especially in regions with irregular brightness values or thin lines, and increasing memory requirements when more pixels are considered.
Innovation Solution
An image processing circuit and method that selects pixels across N horizontal and M vertical lines, calculates brightness variations, determines representative values, and generates an energy-variation value to produce an edge score for the target pixel, effectively assessing edge components while minimizing memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a Sobel mask uses only a few pixels (3 pixels on each side) for edge detection, then the processing speed is fast and memory usage is low, but the edge detection accuracy is poor and prone to errors in irregular brightness regions
Solution Approach 1:
The patent segments the pixel set into multiple groups based on their spatial relationships to the target pixel. Instead of processing all pixels uniformly, it divides them into distinct groups (e.g., horizontal line pixels, vertical line pixels, diagonal pixels) and processes each group separately to calculate different brightness variation values. This segmentation allows accurate edge detection using a manageable number of pixels from each group.
Solution Approach 2:
The patent applies different processing approaches to different pixel groups based on their local characteristics. Each pixel group is assigned a specific role in detecting edges in particular directions. By giving different weights and processing methods to different local regions, the system achieves high accuracy in multi-directional edge detection without needing to process all pixels with the same complexity.
2Measurement precision
If more pixels are taken into consideration to improve edge detection accuracy, then the measurement precision improves, but the memory requirement increases and cost increases
Solution Approach 1:
The patent extracts only the essential pixel groups needed for accurate edge detection in multiple directions, rather than storing and processing all pixels in the image. It selectively extracts pixels from horizontal lines, vertical lines, and diagonal lines that are most relevant for detecting edges in corresponding directions, thereby reducing memory requirements while maintaining detection accuracy.
Solution Approach 2:
The patent uses a partial action approach by selecting specific pixel groups that provide sufficient information for accurate edge detection. Rather than using all possible pixels (excessive action), it identifies and processes only the necessary subsets of pixels from different directional lines, achieving good detection accuracy with reduced computational and memory resources.
3Reliability
If conventional Sobel mask is used with limited pixels, then the processing complexity is low, but the reliability of edge detection is poor in regions with irregular brightness values or thin lines
Solution Approach 1:
The patent extends the conventional 2D Sobel mask approach by incorporating pixels from multiple dimensional directions - horizontal lines, vertical lines, and diagonal lines. This multi-dimensional pixel selection strategy allows the system to detect edges in various orientations reliably, improving detection reliability in complex regions with irregular brightness patterns or thin lines.
Solution Approach 2:
The patent creates a composite processing approach by combining results from multiple pixel groups and directional analyses. It integrates brightness variation values calculated from horizontal, vertical, and diagonal pixel groups to form a comprehensive edge detection result, thereby improving reliability through composite information from multiple sources.
Data Source
AI summary
An image processing circuit capable of detecting an edge component includes: a selecting circuit acquiring the brightness values of pixels of an image according to the position of a target pixel and a processing region, wherein the pixels include N horizontal lines and M vertical lines; a brightness-variation calculating circuit generating N horizontal-line-brightness-variation values according to brightness variation of the N horizontal lines, and generating M vertical-line-brightness-variation values according to brightness variation of the M vertical lines; a brightness-variation determining circuit choosing a horizontal-line-brightness-variation representative value among the N horizontal-line-brightness-variation values, choosing a vertical-line-brightness-variation representative value among the M vertical-line-brightness-variation values, and choosing a brightness-variation representative value between the two representative values; an energy-variation calculating circuit generating an energy-variation value according to the brightness values of the pixels; and an edge-score calculating circuit generating an edge score of the target pixel according to the brightness-variation representative value and energy-variation value.