Wavelets for Computer Graphics: A Primer, Part 1
IEEE Computer Graphics and Applications
Adaptive image denoising and edge enhancement in scale-space using the wavelet transform
Pattern Recognition Letters - Special issue: Sibgrapi 2001
Wavelet-Based Texture Classification of Tissues in Computed Tomography
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Computers in Biology and Medicine
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
Automatic image enhancement driven by evolution based on ridgelet frame in the presence of noise
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Image enhancement based on a nonlinear multiscale method
IEEE Transactions on Image Processing
The finite ridgelet transform for image representation
IEEE Transactions on Image Processing
Gray and color image contrast enhancement by the curvelet transform
IEEE Transactions on Image Processing
Expert Systems with Applications: An International Journal
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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The research presented in this article is aimed at the development of an automated imaging system for distress detection and isolation in asphalt pavement distress obtained from pavement image acquisition system (PIAS). This article focuses on comparing the discriminating power of several multi-resolution texture analysis techniques using wavelet, ridgelet, and curvelet-based texture descriptors. The approach consists of four steps: Image collection, segmentation of regions of interest (ROI), extraction of the most discriminative texture features, creation of a classifier that automatically identifies the pavement distress, and storage. Tests comparing the wavelet, ridgelet, and curvelet texture features indicated that curvelet-based signatures outperform all other multi-resolution techniques for pothole distress, yielding accuracy rates in the 97.9%. Ridgelet-based signatures outperform all other multi-resolution techniques for cracking distress, yielding accuracy rates in the 93.6-96.4% rate.