LBP (Local Binary Pattern) is a texture descriptor used in image processing and computer vision for characterizing patterns and textures in images. LBP is particularly useful for tasks like texture classification, facial recognition, and object detection.

The Local Binary Pattern operator works by comparing each pixel in an image with its neighboring pixels. It assigns a binary code to each pixel based on whether the intensity of its neighbors is greater than or less than the intensity of the center pixel. This process creates a binary pattern for each pixel, which encodes information about the local texture around that pixel.

LBP is characterized by its simplicity, computational efficiency, and robustness to various image transformations such as changes in illumination and noise. It has been widely adopted in many applications due to its effectiveness in capturing texture information from images.


Detection of Road Anomaly Using Distributed Fiber Optic Sensing

Detection of Road Anomaly Using Distributed Fiber Optic Sensing Road surface condition can significantly impact the interaction between vehicles and pavement structure, which may even cause high fuel consumption and safety issues of drivers and vehicles. Distributed fiber optic sensing (DFOS) technology is a useful tool to perform continuous and real-time monitoring of traffic and road surface condition. However, it is challenging to process the data for the purpose of road anomaly detection. The study proposed two approaches to detect the road anomaly using DFOS. In the first method, local binary pattern (LBP) histograms were used to extract the features of the images with and without road anomaly, and support vector machine (SVM) combined with principal component analysis (PCA) was adopted as the classifier. The convolutional neural network (CNN) was applied on the binary classification data to analyze the images in the second method. The accuracy and benefits of two methodologies were compared. The vehicle speed was estimated by detecting lines using Hough transform. The feasibility of road anomaly detection using DFOS is proved.