Topic
Multiresolution analysis
About: Multiresolution analysis is a research topic. Over the lifetime, 4032 publications have been published within this topic receiving 140743 citations. The topic is also known as: Multiresolution analysis, MRA.
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TL;DR: A comparative study between wavelet and curvelet transform for breast cancer diagnosis in digital mammogram suggests that curvelettransform outperforms wavelet transform and the difference is statistically significant.
120 citations
TL;DR: This paper demonstrates how adaptive wavelet basis can be used to locate defects in woven fabrics.
Abstract: Many textures such as woven fabrics and composites have a regular and repeating texture. This paper presents a new method to capture the texture information using adaptive wavelet bases. Wavelets are compact functions which can be used to generate a multiresolution analysis. Texture constraints are used to adapt the wavelets to better characterize specific textures. An adapted wavelet basis has very high sensitivity to the abrupt changes in the texture structure caused by de- fects. This paper demonstrates how adaptive wavelet basis can be used to locate defects in woven fabrics. © 1996 Society of Photo-Optical Instrumenta- tion Engineers.
120 citations
TL;DR: It is concluded that WDDFF is a useful tool for forecasting real-world hydrological and water resources processes that overcomes the limitations of many earlier wavelet-based forecasting methods and should be explored further for forecasting different processes such as streamflow, rainfall, evaporation, etc.
120 citations
TL;DR: Evaluating several wavelet pyramids that may be used both for invariant feature extraction and for representing images at multiple spatial resolutions to accelerate registration finds that the bandpass wavelets obtained from the steerable pyramid due to Simoncelli performs best in terms of accuracy and consistency.
Abstract: The problem of image registration, or the alignment of two or more images representing the same scene or object, has to be addressed in various disciplines that employ digital imaging. In the area of remote sensing, just like in medical imaging or computer vision, it is necessary to design robust, fast, and widely applicable algorithms that would allow automatic registration of images generated by various imaging platforms at the same or different times and that would provide subpixel accuracy. One of the main issues that needs to be addressed when developing a registration algorithm is what type of information should be extracted from the images being registered, to be used in the search for the geometric transformation that best aligns them. The main objective of this paper is to evaluate several wavelet pyramids that may be used both for invariant feature extraction and for representing images at multiple spatial resolutions to accelerate registration. We find that the bandpass wavelets obtained from the steerable pyramid due to Simoncelli performs best in terms of accuracy and consistency, while the low-pass wavelets obtained from the same pyramid give the best results in terms of the radius of convergence. Based on these findings, we propose a modification of a gradient-based registration algorithm that has recently been developed for medical data. We test the modified algorithm on several sets of real and synthetic satellite imagery.
118 citations
TL;DR: Neural Networks are trained with the wavelet transformed templates providing an efficient detector even for temporally varying patterns within the complete time series, which solves the problem of automatic P wave detection in Holter ECG recordings.
117 citations