Straight Line Detection via Gradient Histogram Features
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Solution Overview
Problem
Existing methods for detecting straight lines in images using the Hough transform are prone to inaccuracies when edge strength is weak or noise is present, leading to broken lines and reduced detection accuracy.
Innovation Solution
The method involves acquiring gradient histogram feature sets for each pixel point, determining candidate directions based on these features, and then determining the precise direction and position of the straight line, without using Hough Transform or binarizing the image, thus avoiding errors caused by binarization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Hough transform with binarization is used to detect straight lines, then the detection method is simple and fast, but the detection accuracy deteriorates when edge strength is weak or noise is present
Solution Approach 1:
The patent changes the parameter representation from binary values (0 or 1) to gradient magnitude values. Instead of using binarized images where pixels are either black or white, the invention uses gradient magnitude values that continuously represent the strength of edge detection at each pixel location. This parameter change allows the Hough transform to work with nuanced gradient information rather than harsh binary decisions, improving accuracy while maintaining computational efficiency.
2Speed
If binarization is applied to enhance edge detection, then the processing speed is improved, but the straight line detection accuracy deteriorates due to sensitivity to parameters and noise
Solution Approach 1:
The patent introduces gradient magnitude as an intermediary between the raw image data and the binary decision-making process of traditional edge detection. Instead of directly binarizing pixel values, the method first computes gradient magnitudes which serve as a mediator that preserves continuous information about edge strength. This intermediary representation allows for more reliable detection by maintaining subtle variations in edge intensity that would otherwise be lost in binary conversion.
3Measurement precision
If gradient histogram feature sets are computed for each pixel point to improve detection accuracy, then the detection precision is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the computational process into two distinct phases: a preprocessing phase where gradient histogram feature sets are computed and stored for each pixel point, and a detection phase where these pre-computed features are used to determine candidate directions and final line parameters. This segmentation allows the computationally intensive feature extraction to be performed once, with the results reused during detection, thereby reducing the overall computational complexity while maintaining high detection precision.
Data Source
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AI summary
The present disclosure relates to a method and a device for detecting a straight line. The method includes: acquiring a gradient histogram feature set of each of respective pixel points in an image, wherein the gradient histogram feature set is configured to reflect straight line characteristics of a local area where the pixel point locates (102); determining at least one candidate direction of a straight line to be detected according to the gradient histogram feature sets of the respective pixel points (104); and determining a precise direction and a precise position of the straight line to be detected according to the at least one candidate direction (106). The present disclosure may avoid the influences of errors caused by binarization sufficiently, and increase the accuracy of the detection of the straight line.