The purpose of this study is to develop a classification method for a crack on a pavement surface image using machine learning to reduce a maintenance fee. Our database consists of 3500 pavement surface images. This includes 800 crack and 2700 normal pavement surface images. The pavement surface images first are decomposed into several sub-images using a discrete wavelet transform (DWT) decomposition. We then calculate the wavelet sub-band histogram from each several sub-images at each level. The support vector machine (SVM) with computed wavelet sub-band histogram is employed for distinguishing between a crack and normal pavement surface images. The accuracies of the proposed classification method are 85.3% for crack and 84.4% for normal pavement images. The proposed classification method achieved high performance. Therefore, the proposed method would be useful in maintenance inspection.
KEYWORDS: Principal component analysis, Wavelets, RGB color model, Shape analysis, Statistical analysis, Data modeling, Data conversion, 3D modeling, Photography, Machine learning, 3D image processing
3D Facial aging changes in more than 10 years of identical persons are being measured at National Research Institute of Police Science. We performed machine learning using such measured data as teacher data and have developed the system which convert input 2D face image into 3D face model and simulate aging. Here, we report about processing and accuracy of our system.
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