To demonstrate image feature Extraction using Hough Transform

Feature extraction is the process of defining a set of features, or image characteristics, which will most efficiently or meaningfully represent the information that is important for analysis and classification.

The Hough transform is a popular feature extraction technique that converts an image from Cartesian to polar coordinates. Any point within the image space is represented by a sinusoidal curve in the Hough space. In addition, two points in a line segment generate two curves, which are overlaid at a location that corresponds with a line through the image space. Even though this model form is very easy, it is deeply complicated for the case of complex shapes due to noise and shape imperfection, as well as the problem of finding slopes of vertical lines.

The Circular Hough Transform (CHT) is one of the modified versions of the HT. The purpose of using CHT is to find the circular patterns within an image scene. The CHT is used to transform a set of feature points in the image space into a set of accumulated votes in a parameter space. Then, for each feature point, votes are accumulated in an accumulator array for all parameter combinations. The array elements that contain the highest number of votes indicate the presence of the shape.

In this code, the experimental work is implemented using python libraries linked with pycharm. Libraries like CV2 , numpy and os are really efficient and has fast processing and less time consumption for performing computer vision tasks. Pycharm is adopted as an implementation tool in our method.

It can be used for medical purpose to study the imperfection

In this code, Binary cross image is taken for feature extraction and hough transform was implemented to find imperfect instances of objects.