@conference {14208,
	title = {Combining powerful local and global statistics for texture description},
	booktitle = {IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009},
	year = {2009},
	month = {2009/06/20/25},
	pages = {573 - 580},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A texture descriptor is proposed, which combines local highly discriminative features with the global statistics of fractal geometry to achieve high descriptive power, but also invariance to geometric and illumination transformations. As local measurements SIFT features are estimated densely at multiple window sizes and discretized. On each of the discretized measurements the fractal dimension is computed to obtain the so-called multifractal spectrum, which is invariant to geometric transformations and illumination changes. Finally to achieve robustness to scale changes, a multi-scale representation of the multifractal spectrum is developed using a framelet system, that is, a redundant tight wavelet frame system. Experiments on classification demonstrate that the descriptor outperforms existing methods on the UIUC as well as the UMD high-resolution dataset.},
	keywords = {Computer science, discretized measurements, fractal geometry, Fractals, geometric transformations, global statistics, Histograms, illumination transformations, image classification, image resolution, Image texture, lighting, local measurements SIFT features, local statistics, MATHEMATICS, multifractal spectrum, multiscale representation, Power engineering and energy, Power engineering computing, Robustness, Solids, Statistics, texture description, UMD high-resolution dataset, wavelet frame system, Wavelet transforms},
	isbn = {978-1-4244-3992-8},
	doi = {10.1109/CVPR.2009.5206741},
	author = {Yong Xu and Si-Bin Huang and Hui Ji and Ferm{\"u}ller, Cornelia}
}
@article {14254,
	title = {Robust Wavelet-Based Super-Resolution Reconstruction: Theory and Algorithm},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {31},
	year = {2009},
	month = {2009/04//},
	pages = {649 - 660},
	abstract = {We present an analysis and algorithm for the problem of super-resolution imaging, that is the reconstruction of HR (high-resolution) images from a sequence of LR (low-resolution) images. Super-resolution reconstruction entails solutions to two problems. One is the alignment of image frames. The other is the reconstruction of a HR image from multiple aligned LR images. Both are important for the performance of super-resolution imaging. Image alignment is addressed with a new batch algorithm, which simultaneously estimates the homographies between multiple image frames by enforcing the surface normal vectors to be the same. This approach can handle longer video sequences quite well. Reconstruction is addressed with a wavelet-based iterative reconstruction algorithm with an efficient de-noising scheme. The technique is based on a new analysis of video formation. At a high level our method could be described as a better-conditioned iterative back projection scheme with an efficient regularization criteria in each iteration step. Experiments with both simulated and real data demonstrate that our approach has better performance than existing super-resolution methods. It can remove even large amounts of mixed noise without creating artifacts.},
	keywords = {batch algorithm, better-conditioned iterative back projection scheme, Enhancement, homography estimation, image denoising, image denoising scheme, image frame alignment, Image processing software, Image reconstruction, image resolution, image sequence, Image sequences, iterative methods, regularization criteria, robust wavelet-based iterative super-resolution reconstruction, surface normal vector, video formation analysis, video sequence, video signal processing, Wavelet transforms},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2008.103},
	author = {Hui Ji and Ferm{\"u}ller, Cornelia}
}
@article {15909,
	title = {Shearlet-Based Deconvolution},
	journal = {IEEE Transactions on Image Processing},
	volume = {18},
	year = {2009},
	month = {2009/12//},
	pages = {2673 - 2685},
	abstract = {In this paper, a new type of deconvolution algorithm is proposed that is based on estimating the image from a shearlet decomposition. Shearlets provide a multidirectional and multiscale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets. Constructions such as curvelets and contourlets share similar properties, yet their implementations are significantly different from that of shearlets. Taking advantage of unique properties of a new M-channel implementation of the shearlet transform, we develop an algorithm that allows for the approximation inversion operator to be controlled on a multiscale and multidirectional basis. A key improvement over closely related approaches such as ForWaRD is the automatic determination of the threshold values for the noise shrinkage for each scale and direction without explicit knowledge of the noise variance using a generalized cross validation (GCV). Various tests show that this method can perform significantly better than many competitive deconvolution algorithms.},
	keywords = {approximation inversion operator, contourlets, curvelets, Deconvolution, distributed discontinuities, generalized cross validation, image restoration, M-channel implementation, shearlet-based deconvolution, shearlets, Wavelet transforms, wavelets},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2009.2029594},
	author = {Patel, Vishal M. and Easley,G. R and Healy,D. M}
}
@conference {15060,
	title = {A New Approach of Dynamic Background Modeling for Surveillance Information},
	booktitle = {Computer Science and Software Engineering, 2008 International Conference on},
	volume = {1},
	year = {2008},
	month = {2008/12//},
	pages = {850 - 855},
	abstract = {This paper presents a new approach of best background modeling for surveillance information. The approach makes orthogonal non-separable wavelet transformation of information frames used for background modeling, extracts the approximate information to reconstruct information frames, filters out the disturbance, shadow and noise from the reconstructed frames, constructs basic background with the method of binary mask images, uses multi-frame combination of non-uniform noise to filter noise in basic background, applies mutual information to detect the situation of adjacent changes. If the background has a gradual change, weighted superposition of multi background modeling images with time will be applied to update the background. If the background has a major or sudden change, the background will remodel from this frame.},
	keywords = {approximate information extraction, binary mask images, disturbance filtering, dynamic background modeling, Feature extraction, filtering theory, Image reconstruction, information frame reconstruction, NOISE, noise filtering, orthogonal nonseparable wavelet transformation, Surveillance, surveillance information, Wavelet transforms},
	doi = {10.1109/CSSE.2008.601},
	author = {Gao,Dongfa and Zhuolin Jiang and Ye,Ming}
}
@conference {15916,
	title = {A new multiresolution generalized directional filter bank design and application in image enhancement},
	booktitle = {15th IEEE International Conference on Image Processing, 2008. ICIP 2008},
	year = {2008},
	month = {2008/10//},
	pages = {2816 - 2819},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, we present an image enhancement technique based on a new multiscale generalized directional filter bank design. The design presented is a shift-invariant overcomplete representation, which is well suited to extracting geometric features such as edges. Special cases of this design method can be made to reduce to different and improved implementations of the shearlet and the contourlet transforms, which are known to represent certain classes of images optimally. Use of this new filter bank design has proven itself competitive in image restoration for noisy images and is well suited for distinguishing noise from weak edges. Experimental results show that our unique image enhancement technique out-performs wavelet and contourlet based enhancement methods.},
	keywords = {Algorithm design and analysis, Approximation error, Channel bank filters, contourlet transform, Design methodology, Discrete transforms, Feature extraction, Filter bank, Frequency, geometric feature extraction, Image Enhancement, IMAGE PROCESSING, image resolution, image restoration, Multidimensional digital filters, Multidimensional systems, multiresolution generalized directional filter bank design, shearlet transform, shift-invariant overcomplete representation, transforms, Wavelet transforms},
	isbn = {978-1-4244-1765-0},
	doi = {10.1109/ICIP.2008.4712380},
	author = {Patel, Vishal M. and Easley,G. R and Healy,D. M}
}
@conference {11894,
	title = {Hidden Markov models for silhouette classification},
	booktitle = {Automation Congress, 2002 Proceedings of the 5th Biannual World},
	volume = {13},
	year = {2002},
	month = {2002///},
	pages = {395 - 402},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, a new technique for object classification from silhouettes is presented. Hidden Markov models are used as a classification mechanism. Through a set of experiments, we show the validity of our approach and show its invariance under severe rotation conditions. Also, a comparison with other techniques that use hidden Markov models for object classification from silhouettes is presented.},
	keywords = {Computer vision, Feature extraction, Fourier transforms, hidden Markov models, HMM, image classification, Neural networks, object classification, Object recognition, parameter estimation, pattern recognition, Probability distribution, Shape measurement, silhouette classification, Wavelet transforms},
	isbn = {1-889335-18-5},
	doi = {10.1109/WAC.2002.1049575},
	author = {Abd-Almageed, Wael and Smith,C.}
}
@conference {15592,
	title = {Robust matching of wavelet features for sub-pixel registration of Landsat data},
	booktitle = {Geoscience and Remote Sensing Symposium, 2001. IGARSS {\textquoteright}01. IEEE 2001 International},
	volume = {2},
	year = {2001},
	month = {2001///},
	pages = {706 -708 vol.2 - 706 -708 vol.2},
	abstract = {For many Earth and space science applications, automatic geo-registration at sub-pixel accuracy has become a necessity. In this work, we are focusing on building an operational system, which will provide a sub-pixel accuracy registration of Landsat-5 and Landsat-7 data. The input to our registration method consists of scenes that have been geometrically and radiometrically corrected. Such preprocessed scenes are then geo-registered relative to a database of Landsat chips. The method assumes a transformation composed of a rotation and a translation, and utilizes rotation- and translation-invariant wavelets to extract image features that are matched using statistically robust feature matching and a partial Hausdorff distance metric. The registration process is described and results on four Landsat input scenes of the Washington, D.C., area are presented},
	keywords = {Feature extraction, geo-registration, geophysical measurement technique, geophysical signal processing, geophysical techniques, Hausdorff distance metric, image registration, infrared, IR, land surface, Landsat, Landsat-5, Landsat-7, multispectral remote sensing, robust feature matching, robust matching, sub pixel registration, subpixel accuracy, terrain mapping, visible, wavelet feature, wavelet method, Wavelet transforms},
	doi = {10.1109/IGARSS.2001.976609},
	author = {Le Moigne,J. and Netanyahu,N. S and Masek,J. G and Mount, Dave and Goward, S.N.}
}
@conference {15591,
	title = {Geo-registration of Landsat data by robust matching of wavelet features},
	booktitle = {Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International},
	volume = {4},
	year = {2000},
	month = {2000///},
	pages = {1610 -1612 vol.4 - 1610 -1612 vol.4},
	abstract = {The goal of our project is to build an operational system, which will provide a sub-pixel accuracy registration of Landsat-5 and Landsat-7 data. Integrated within an automated mass processing/analysis system for Landsat data (REALM), the input to our registration method consists of scenes that have been geometrically and radiometrically corrected, as well as pre-processed for the detection of clouds and cloud shadows. Such pre-processed scenes are then geo-registered relative to a database of Landsat chips. This paper describes our registration process, including the use of a database of landmark chips, a feature extraction performed by an overcomplete wavelet representation, and feature matching using statistically robust techniques. Knowing the approximate longitudes and latitudes of the four corners of the scene, a subset of chips which represent landmarks included in the scene are extracted from the database. For each of these selected landmark chips, a corresponding window is extracted from the scene, and each chip-window pair is registered using our robust wavelet feature matching. First results and future directions are presented in the paper},
	keywords = {atmospheric techniques, automated mass processing/analysis system, chip-window pair, cloud shadows, Clouds, Feature extraction, feature matching, geo-registration, geometrically corrected scene, geophysical signal processing, Image matching, image registration, landmark chips, Landsat chips, Landsat data, Landsat-5 data, Landsat-7 data, overcomplete wavelet representation, pre-processed scenes, radiometrically corrected scene, REALM, Remote sensing, robust matching, robust wavelet feature matching, scenes, statistically robust techniques, sub-pixel accuracy registration, wavelet features, Wavelet transforms, window},
	doi = {10.1109/IGARSS.2000.857287},
	author = {Le Moigne,J. and Netanyahu,N. S and Masek,J. G and Mount, Dave and Goward,S. and Honzak,M.}
}
