The demand for speedier algorithms that can extract information from photos is becoming more crucial due to the continuously growing amount of image data in practically all disciplines. The practise of utilising digital image processing methods to extract significant information from a 2-dimensional picture is known as image analysis. The term "digital image processing" refers to a group of methods used in preprocessing (such as noise removal and image enhancement), image compression, feature extraction (such as edges and contours), and various key point detection methods (such as corners and joints, areas with particular colours or textures). This study focuses on the recognition and representation of texture, a crucial aspect of picture analysis. Utilizing a well-known texture identification technique built on filter banks, we conduct tests on a variety of artificial and real-world photos. We pinpoint certain particular situations in which the algorithm fails and suggest a change to the original approach that produces better segmentations.

Machine Learning, Digital Image, Filters, Feature extraction and Noise