Standard Hough Transform

The Hough Transform is a classic image processing technique commonly used for detecting geometric shapes such as lines and circles in images. The Standard Hough Transform primarily focuses on line detection, with its core concept involving the conversion of lines from image space to parameter space (Hough space) through an accumulation voting mechanism, enabling effective line identification.

Standard Hough Transform Process Edge Detection: First, edge detection (e.g., using Canny edge detection) is applied to the input image to obtain a binary edge map. Parameter Space Conversion: In Hough space, a line can be represented using polar coordinates (ρ, θ). Each edge point in the image corresponds to a sinusoidal curve in the parameter space, and the intersections of these curves from all edge points indicate potential lines. Peak Detection: Through an accumulation voting mechanism, high-accumulation points in the parameter space correspond to lines in the image. Detecting these peaks (typically using thresholding or non-maximum suppression) identifies the prominent lines present in the image. Line Extraction and Drawing: Based on the detected peak parameters (ρ, θ), the line equations are converted back to image space, and the corresponding pixel points are extracted to draw the lines.

Peak Extraction Optimization To improve detection accuracy and efficiency, peak extraction in the parameter space is often optimized: Non-Maximum Suppression: Ensures only local maxima are detected, preventing redundant detection of similar lines. Threshold Setting: Filters out low-accumulation noise lines by setting a minimum vote threshold.

The Hough Transform is widely used in computer vision, particularly excelling in applications such as lane detection and document analysis. Although computationally intensive, its robustness and accuracy make it a fundamental method for line detection.