Single Core Hardware Approach to Implement Fuzzy Wavelet Based Textures Segmentation with a Single System Clock

Texture refers to the surface properties that can be easily described by its primitives (tones) and their spatial relationship. Texture analysis is a process to find the shape, segment or identify the region of interest on the object. In this research study, we approach to implement unsupervised texture segmentation in hardware. First, the Discrete Wavelet Transform (DWT) is used to extract the input image and sample it into different frequency bands. After this process, the input image becomes smaller and compressed. This input image is then fed into fuzzy K-mean Clustering algorithm. and many computations are needed for the correct segmentation assignment. Hence, the total execution time for segmentation is improved using compressed image as input. From the simulation result, images with two to five t Fuzzy K-mean is a well-known and precise supervised clustering algorithm that divides the image into different segmentations ypes of textures were successfully detected around 0.025-0.033 sec. The proposed hardware approach for wavelet based texture segmentation is able to reduce the execution time, to enhance the performance for 128*128 pixels, which can be considered as fast segmentation solution.