Wavelets: a tutorial in theory and applications
Wavelets: a tutorial in theory and applications
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Objects Tracking by Using Statistical Features Shape Modeling and Histogram Analysis
ICAPR '09 Proceedings of the 2009 Seventh International Conference on Advances in Pattern Recognition
An Approach for Image Thresholding Using CNN Associated with Histogram Analysis
ICMTMA '09 Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 01
Efficient Enhancement of Microarray Image Using Histogram Specification
ICCTD '09 Proceedings of the 2009 International Conference on Computer Technology and Development - Volume 01
Fast Similarity Search with Blocking Wavelet-Histogram and Adaptive Particle Swarm Optimization
WKDD '10 Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining
Histograms and Wavelets on Probabilistic Data
IEEE Transactions on Knowledge and Data Engineering
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
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The Wavelets are mathematical functions. They are used to catch up the data into different frequency components and study the components with a resolution matched to its scale. Wavelets were developed for the fields of mathematics, modern physics, electrical and electronics engineering, and seismic geology. The remarkable developments is being observed in these fields during the last 15 years and have led to many new wavelet applications such as image compression, de-noising, computer vision, automation, automatic visual inspection systems, turbulence, human vision, radar, and earth quake prediction etc. The proposed methodology provides an approach for exploring unique statistical information contained in 2D images, which may further be utilized for developing image recognition techniques.