What is the Hough Transform?

The Hough Transform is a feature-extraction technique that finds geometric shapes — lines, circles, ellipses — inside noisy or complex images. Instead of looking for shapes directly, it converts the problem into a vote: each edge pixel casts votes in a parameter space, and shapes emerge wherever votes accumulate.

A straight line in an image becomes a single bright point in Hough space. That insight is what makes it robust: real signal piles up, noise scatters.

Where it gets used

• Blood microscopy — automated counting and analysis of red blood cells, which are nearly perfect circles, in stained slide images.
• Astronomy & cosmic imaging — detecting filamentary structures in radio telescope data, finding stellar trails, and identifying ring nebulae from survey images.
• Lane detection in autonomous vehicles — finding road lane markings under varying light and weather in real time.
• Industrial inspection — locating edges, seams, and defects on manufactured parts on a production line.
• Document scanning — straightening skewed pages by detecting the dominant line orientation of text rows.
• Medical imaging — identifying anatomical boundaries in CT and MRI scans, such as bone edges and organ walls.

This lab runs the standard Hough line transform in-browser. Upload any image, hit Run, and watch it find the dominant line in the frame.