Georgios Hadjidemetriou and Symeon E. Christodoulou
Pavement Anomalies Detection and Classification Using Entropic Texture Segmentation and Support Vector Machines
Abstract: Presented herein is a vision-based method for the detection of anomalies on roadway pavements, utilizing low-cost video acquisition and image processing of road surface frames collected by a smartphone (or camera) located on a vehicle moving in a real-life urban network, along with entropy-based texture segmentation filters, and support vector machine (SVM) classification. The proposed system, which has been developed in MATLAB, pre-processes video streams for the identification of video frames of changes in image-entropy values, isolates these frames and performs texture segmentation to identify pixel areas of significant changes in entropy values, and then classifies and quantifies these areas using SVMs. The developed SVM is trained and tested by feature vectors generated from the histogram and two texture descriptors of non-overlapped square blocks, which constitute images that includes ‘‘patch’’ and ‘‘no-patch’’ areas. The outcome is composed of block-based and image-based classification, as well as measurement of the patch area.
Keywords: Pavement Condition Evaluation, Road Anomaly Detection, Vision-Based, Entropy, Texture Segmentation
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Shahid Kabir, Dong-Chen He, Patrice Rivard
Urban Classification Of High Resolution Ikonos Imagery Using Texture
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