The identification and classification of patterns in data is the primary emphasis of the machine learning and artificial intelligence field of pattern recognition. It entails the process of categorizing incoming data according to important characteristics or patterns into objects or classes. Text, sounds, pictures, and signals are just a few of the many data kinds to which this approach can be used.
Chain Codes
In image processing and computer vision, chain codes are a technique used to describe an object's border in a digital image. By encoding the orientations of the border segments, they are able to characterize the geometry of a boundary. An introduction to chain codes and their uses is provided below:
How Chain Codes Work
Boundary Detection: First, the boundary of the object in the image is detected. This is usually done through edge detection algorithms.
Starting Point: Select a starting point on the boundary.
Direction Encoding: Move along the boundary in a consistent direction (e.g., clockwise) and record the direction of each segment using a predefined code. Commonly used directions are based on 4-connectivity or 8-connectivity:
4-connectivity: Uses 4 possible directions (up, right, down, left).
