Parallel Implementation of Neighbors-Based Binary Pattern Using CUDA

Texture analysis is a critical aspect of image processing and computer vision, finding applications in fields such as object recognition, image classification, and image analysis. One prominent method for capturing texture information is the Neighbors Based Binary Pattern (NBP). The NBP method encodes the relative relationships between a pixel and its neighbors through binary patterns, providing a robust representation of local texture variations and feature extraction. However, the computational requirements for NBP are notably elevated, particularly when dealing with high-resolution images. In this paper, we undertake the parallel implementation of NBP by harnessing the power of Compute Unified Device Architecture (CUDA) technology on Graphics Processing Units (GPUs). This approach involves leveraging the computational capabilities of GPUs to execute the NBP algorithm concurrently, enabling enhanced processing speed and efficiency compared to traditional sequential implementations. Overall, the research aims to contribute valuable perspectives and further innovation in tasks involving texture analysis and parallel computation.