Hough transform in computer vision. - GeeksforGeeks (2024)

The Hough Transform is a popular technique in computer vision and image processing, used for detecting geometric shapes like lines, circles, and other parametric curves. Named after Paul Hough, who introduced the concept in 1962, the transform has evolved and found numerous applications in various domains such as medical imaging, robotics, and autonomous driving. In this article, we will discuss how Hough transformation is utilized in computer vision.

What is Hough Transform?

A feature extraction method called the Hough Transform is used to find basic shapes in a picture, like circles, lines, and ellipses. Fundamentally, it transfers these shapes’ representation from the spatial domain to the parameter space, allowing for effective detection even in the face of distortions like noise or occlusion.

How Does the Hough Transform Work?

The accumulator array, sometimes referred to as the parameter space or Hough space, is the first thing that the Hough Transform creates. The available parameter values for the shapes that are being detected are represented by this space. The slope (m) and y-intercept (b) of a line, for instance, could be the parameters in the line detection scenario.

The Hough Transform calculates the matching curves in the parameter space for each edge point in the image. This is accomplished by finding the curve that intersects the parameter values at the spot by iterating over all possible values of the parameters. The “votes” or intersections for every combination of parameters are recorded by the accumulator array.

In the end, the programme finds peaks in the accumulator array that match the parameters of the shapes it has identified. These peaks show whether the image contains lines, circles, or other shapes.

Variants and Techniques of Hough transform

The performance and adaptability of the Hough Transform have been improved throughout time by a number of variations and techniques:

• Paul Hough’s initial formulation for line identification is known as the Standard Hough Transform (SHT). It entails voting for every possible combination of parameters and discretizing the parameter space.
• Probabilistic Hough Transform (PHT): The PHT randomly chooses a subset of edge points and only applies line detection to those locations in order to increase efficiency. For real-time applications, this minimizes processing complexity while maintaining accuracy in the output.
• Generalized Hough Transform (GHT): By recording the spatial relationships of every shape using a template, the GHT can detect any shape, in contrast to the SHT’s limited ability to detect just specified shapes. After that, a voting system akin to the SHT is used to match this template with the image.
• Accumulator Space Dimensionality: The classic Hough Transform can identify lines in two dimensions, but it can also detect more complicated forms, such ellipses or circles, in higher dimensions. Every extra dimension translates into an extra parameter of the identified shape.

Implementation of Hough transform in computer vision

The Python code implementation for line detection utilizing the Hough Transform on this image and OpenCV is described in detail below.

1) Import necessary libraries

This code imports OpenCV for image processing and the NumPy library for numerical computations.

import numpy as npimport cv2

2) Read the image

img = cv2.imread('lane_hough.jpg', cv2.IMREAD_COLOR) # lane_hough.jpg is the filename

3) Convert the Image to Grayscale

Convert the loaded image to grayscale for edge detection.

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

4) Apply Canny Edge Detector:

Detect edges in the grayscale image using the Canny edge detection method.

edges = cv2.Canny(gray, 50, 200)

5) Detect Lines using Probabilistic Hough Transform:

In order to find lines in the edge-detected image, use the ‘cv2.HoughLinesP’ function. An array of lines is returned by this method, with each line’s end points (x1, y1, x2, y2) serving as its representation.

lines = cv2.HoughLinesP(edges, 1, np.pi/180, 68, minLineLength=15, maxLineGap=250)

6) Draw Detected Lines on the Original Image:

Draw each identified line using ‘cv2.line’ on the original image after iterating over them. The lines’ thickness is set to 3 pixels, and their color is set to blue (255, 0, 0).

for line in lines: x1, y1, x2, y2 = line[0] cv2.line(img, (x1, y1), (x2, y2), (255, 0, 0), 3)

7) Display the Result:

A statement explaining that line detection is being done should be printed. Next, use ‘cv2.imshow’ to display the image with the lines that have been detected. Finally, use ‘cv2.waitKey(0)’ and ‘cv2.destroyAllWindows()’ to wait for a key press to close the window.

print("Line Detection using Hough Transform")cv2.imshow('lanes', img)cv2.waitKey(0)cv2.destroyAllWindows()

Taking complete code at once, we get

import numpy as npimport cv2# Read imageimg = cv2.imread('lane_hough.jpg',cv2.IMREAD_COLOR) # road.png is the filename# Convert the image to grayscalegray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# Find the edges in the image using canny detectoredges = cv2.Canny(gray, 50, 200)# Detect points that form a linelines = cv2.HoughLinesP(edges, 1, np.pi/180, 68, minLineLength=15, maxLineGap=250)#lines = cv2.HoughLinesP(edges, 1, np.pi/180, minLineLength=10, maxLineGap=250)# Draw lines on the imagefor line in lines: x1, y1, x2, y2 = line[0] cv2.line(img, (x1, y1), (x2, y2), (255, 0, 0), 3)# Show resultprint("Line Detection using Hough Transform")cv2.imshow('lanes',img)cv2.waitKey(0)cv2.destroyAllWindows()

Output:

Applications in Computer Vision

In computer vision, the Hough Transform has several uses, some of which are as follows:

• Edge Detection: The Hough Transform is an essential part of edge detection algorithms that facilitate the extraction of significant information from images by identifying lines or curves in the image.
• Object Recognition: To aid in the identification and categorization of items, the Hough Transform can be utilized in object recognition tasks to pinpoint particular forms within an image.
• Lane detection: To help autonomous cars stay in their assigned lanes, lane markers on the road are commonly detected using the Hough Transform.
• Medical Imaging: The Hough Transform can be used to identify and evaluate different anatomical features in medical imaging applications, such as MRI or CT scans, which can help with diagnosis and therapy planning.
• In the manufacturing sector, the Hough Transform can be applied to quality control tasks like measuring component dimensions or looking for flaws.

Conclusion

The Hough Transform has many different and extensive uses in computer vision. In order to extract meaningful characteristics from images and enable additional functions in artificial intelligence systems, the technique is essential for tasks like edge detection, object recognition, lane detection in autonomous vehicles, and medical imaging analysis. Because of its adaptability and efficiency, it is a vital tool for researchers, engineers, and developers who want to address practical image processing and analysis issues.

The Hough Transform is essential to computer vision and will only become more so as technology develops and the need for complex picture analysis increases. Its precise and effective geometric shape detection makes it a useful tool across a range of industries, propelling breakthroughs in artificial intelligence and fostering inventions that improve society at large.

Hough Transform FAQs

What is the Hough Transform ?

In computer vision, the Hough Transform is a feature extraction method used to find geometric shapes in an image, like lines, circles, and ellipses.

How does the Hough Transform work ?

In order to enable shape detection even in the presence of noise or other distortions, the Hough Transform works by translating the representation of shapes from the spatial domain to the parameter space.

What are some variants of the Hough Transform ?

The Probabilistic Hough Transform (PHT), Generalized Hough Transform (GHT), and extensions to identify more intricate shapes are examples of Hough Transform variations.

What are the applications of the Hough Transform in computer vision ?

Many computer vision applications, such as edge detection, object recognition, lane detection, medical image analysis, and quality control, use the Hough Transform.

How can I learn more about the Hough Transform and its applications ?

Research articles, online tutorials and courses, open-source libraries, and interactions with the computer vision community are good resources for learning more about the Hough Transform.