Houghspace

Houghspace is a concept used in image processing and computer vision, particularly within the context of the Hough Transform. The Hough Transform is an algorithm used to detect shapes, such as lines, circles, or ellipses, in an image. It works by transforming the image space, where pixels have coordinates, into a parameter space, often referred to as Hough space.

In Hough space, each point represents a specific instance of the shape being searched for. For instance, when detecting lines, each point in Hough space might represent a unique line defined by its parameters, such as the distance from the origin and its angle (rho and theta). A pixel in the image space that lies on a particular line will contribute a "vote" to the corresponding accumulator cell in Hough space that represents that line.

The process involves iterating through all the pixels in the input image. For each pixel that is considered part of a potential shape (e.g., an edge pixel detected by an edge detector), the algorithm determines all possible shape instances that could pass through that pixel. These possibilities are then mapped as points in Hough space. Accumulating these votes across all relevant pixels allows for the identification of shapes. Peaks or high-density regions in Hough space correspond to the most likely instances of the target shape present in the original image. This technique is robust to noise and partial occlusion of the shapes.