@conference {12441,
	title = {Synthesis-based recognition of low resolution faces},
	booktitle = {2011 International Joint Conference on Biometrics (IJCB)},
	year = {2011},
	month = {2011/10/11/13},
	pages = {1 - 6},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Recognition of low resolution face images is a challenging problem in many practical face recognition systems. Methods have been proposed in the face recognition literature for the problem when the probe is of low resolution, and a high resolution gallery is available for recognition. These methods modify the probe image such that the resultant image provides better discrimination. We formulate the problem differently by leveraging the information available in the high resolution gallery image and propose a generative approach for classifying the probe image. An important feature of our algorithm is that it can handle resolution changes along with illumination variations. The effective- ness of the proposed method is demonstrated using standard datasets and a challenging outdoor face dataset. It is shown that our method is efficient and can perform significantly better than many competitive low resolution face recognition algorithms.},
	keywords = {Dictionaries, Face, face images, face recognition, face recognition literature, face recognition systems, illumination variations, image resolution, low resolution faces, Organizations, PROBES, support vector machines, synthesis based recognition},
	isbn = {978-1-4577-1358-3},
	doi = {10.1109/IJCB.2011.6117545},
	author = {Shekhar, S. and Patel, Vishal M. and Chellapa, Rama}
}
@article {15912,
	title = {Compressed Synthetic Aperture Radar},
	journal = {IEEE Journal of Selected Topics in Signal Processing},
	volume = {4},
	year = {2010},
	month = {2010/04//},
	pages = {244 - 254},
	abstract = {In this paper, we introduce a new synthetic aperture radar (SAR) imaging modality which can provide a high-resolution map of the spatial distribution of targets and terrain using a significantly reduced number of needed transmitted and/or received electromagnetic waveforms. This new imaging scheme, requires no new hardware components and allows the aperture to be compressed. It also presents many new applications and advantages which include strong resistance to countermesasures and interception, imaging much wider swaths and reduced on-board storage requirements.},
	keywords = {Compressed sensing, compressed sensing (CS), compressive sensing, electromagnetic waveforms, image resolution, radar imaging, SAR imaging modality, synthetic aperture radar, Synthetic aperture radar (SAR), targets spatial distribution},
	isbn = {1932-4553},
	doi = {10.1109/JSTSP.2009.2039181},
	author = {Patel, Vishal M. and Easley,G. R and Healy,D. M and Chellapa, Rama}
}
@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}
}
@conference {15908,
	title = {Compressed sensing for Synthetic Aperture Radar imaging},
	booktitle = {2009 16th IEEE International Conference on Image Processing (ICIP)},
	year = {2009},
	month = {2009/11//},
	pages = {2141 - 2144},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, we introduce a new Synthetic Aperture Radar (SAR) imaging modality that provides a high resolution map of the spatial distribution of targets and terrain based on a significant reduction in the number of transmitted and/or received electromagnetic waveforms. This new imaging scheme, which requires no new hardware components, allows the aperture to be compressed and presents many important applications and advantages among which include resolving ambiguities, strong resistance to countermeasures and interception, and reduced on-board storage constraints.},
	keywords = {Compressed sensing, High-resolution imaging, Image coding, image resolution, Optical imaging, Radar antennas, radar imaging, Radar polarimetry, Reflectivity, SAR, spatial distribution, synthetic aperture radar, synthetic aperture radar imaging},
	isbn = {978-1-4244-5653-6},
	doi = {10.1109/ICIP.2009.5414307},
	author = {Patel, Vishal M. and Easley,G. R and Healy,D. M and Chellapa, Rama}
}
@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}
}
@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 {17875,
	title = {High Performance Communication between Parallel Programs},
	booktitle = {Parallel and Distributed Processing Symposium, 2005. Proceedings. 19th IEEE International},
	year = {2005},
	month = {2005/04//},
	pages = {177b- 177b - 177b- 177b},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present algorithms for high performance communication between message-passing parallel programs, and evaluate the algorithms as implemented in InterComm. InterComm is a framework to couple parallel programs in the presence of complex data distributions within a coupled application. Multiple parallel libraries and languages may be used in the different programs of a single coupled application. The ability to couple such programs is required in many emerging application areas, such as complex simulations that model physical phenomena at multiple scales and resolutions, and image data analysis applications. We describe the new algorithms we have developed for computing inter-program communication patterns. We present experimental results showing the performance of various algorithmic tradeoffs, and also compare performance against an earlier system.},
	keywords = {Adaptive arrays, Analytical models, Chaotic communication, Computational modeling, Computer science, Data analysis, data distribution, Educational institutions, high performance communication, image data analysis, image resolution, inter-program communication patterns, InterComm, Libraries, Message passing, parallel languages, parallel libraries, parallel programming, parallel programs, performance evaluation, Wind},
	isbn = {0-7695-2312-9},
	doi = {10.1109/IPDPS.2005.243},
	author = {Jae-Yong Lee and Sussman, Alan}
}
@conference {14224,
	title = {Integration of motion fields through shape},
	booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005},
	volume = {2},
	year = {2005},
	month = {2005/06/20/25},
	pages = {663- 669 vol. 2 - 663- 669 vol. 2},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Structure from motion from single flow fields has been studied intensively, but the integration of information from multiple flow fields has not received much attention. Here we address this problem by enforcing constraints on the shape (surface normals) of the scene in view, as opposed to constraints on the structure (depth). The advantage of integrating shape is two-fold. First, we do not need to estimate feature correspondences over multiple frames, but we only need to match patches. Second, the shape vectors in the different views are related only by rotation. This constraint on shape can be combined easily with motion estimation, thus formulating motion and structure estimation from multiple views as a practical constrained minimization problem using a rank-3 constraint. Based on this constraint, we develop a 3D motion technique, which locates through color and motion segmentation, planar patches in the scene, matches patches over multiple frames, and estimates the motion between multiple frames and the shape of the selected scene patches using the image gradients. Experiments evaluate the accuracy of the 3D motion estimation and demonstrate the motion and shape estimation of the technique by super-resolving an image sequence.},
	keywords = {3D motion estimation, Automation, CAMERAS, computational geometry, Computer vision, constrained minimization problem, decoupling translation from rotation, Educational institutions, image colour analysis, image gradients, image resolution, Image segmentation, image sequence, Image sequences, integration of motion fields, Layout, minimisation, Motion estimation, motion field integration, motion segmentation, parameter estimation, planar patches, rank-3 constraint, scene patches, SHAPE, shape and rotation, shape estimation, structure estimation},
	isbn = {0-7695-2372-2},
	doi = {10.1109/CVPR.2005.190},
	author = {Ji,H. and Ferm{\"u}ller, Cornelia}
}
@conference {12000,
	title = {Eye design in the plenoptic space of light rays},
	booktitle = {Ninth IEEE International Conference on Computer Vision, 2003. Proceedings},
	year = {2003},
	month = {2003/10/13/16},
	pages = {1160-1167 vol.2 - 1160-1167 vol.2},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Natural eye designs are optimized with regard to the tasks the eye-carrying organism has to perform for survival. This optimization has been performed by the process of natural evolution over many millions of years. Every eye captures a subset of the space of light rays. The information contained in this subset and the accuracy to which the eye can extract the necessary information determines an upper limit on how well an organism can perform a given task. In this work we propose a new methodology for camera design. By interpreting eyes as sample patterns in light ray space we can phrase the problem of eye design in a signal processing framework. This allows us to develop mathematical criteria for optimal eye design, which in turn enables us to build the best eye for a given task without the trial and error phase of natural evolution. The principle is evaluated on the task of 3D ego-motion estimation.},
	keywords = {3D ego-motion estimation, Assembly, B-splines, Camera design, CAMERAS, captured image, compound eyes, Computer vision, data mining, eye, eye-carrying organism, Eyes, filter optimization, image representation, image resolution, Information geometry, Laboratories, light field reconstruction, light gathering power, light rays, mixed spherical-Cartesian coordinate system, Motion estimation, natural evolution process, natural eye designs, natural image statistics, optical nanotechnology, Optical signal processing, optimal eye design mathematical criteria, Organisms, plenoptic image formation, plenoptic space, plenoptic video geometry, sampling operators, sensory ecology, Signal design, Signal processing, signal processing framework, signal processing tool, square-summable sequences, visual acuity},
	isbn = {0-7695-1950-4},
	doi = {10.1109/ICCV.2003.1238623},
	author = {Neumann, J. and Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@article {14189,
	title = {A syntactic approach to scale-space-based corner description},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {16},
	year = {1994},
	month = {1994/07//},
	pages = {748 - 751},
	abstract = {Planar curves are described by information about corners integrated over various levels of resolution. The detection of corners takes place on a digital representation. To compensate for ambiguities arising from sampling problems due to the discreteness, results about the local behavior of curvature extrema in continuous scale-space are employed},
	keywords = {Computer vision, corner detection, curvature extrema, edge detection, IMAGE PROCESSING, image resolution, Image segmentation, Laboratories, Large-scale systems, PARALLEL PROCESSING, pattern recognition, planar curves, resolution, Sampling methods, sampling problems, scale space based corner description, SHAPE, Smoothing methods, syntactic approach},
	isbn = {0162-8828},
	doi = {10.1109/34.297957},
	author = {Ferm{\"u}ller, Cornelia and Kropatsch,W.}
}
@conference {14169,
	title = {Hierarchical curve representation},
	booktitle = {, 11th IAPR International Conference on Pattern Recognition, 1992. Vol.III. Conference C: Image, Speech and Signal Analysis, Proceedings},
	year = {1992},
	month = {1992/09/30/Aug-3},
	pages = {143 - 146},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Presents a robust method for describing planar curves in multiple resolution using curvature information. The method is developed by taking into account the discrete nature of digital images as well as the discrete aspect of a multiresolution structure (pyramid). The authors deal with the robustness of the technique, which is due to the additional information that is extracted from observing the behavior of corners in the pyramid. Furthermore the resulting algorithm is conceptually simple and easily parallelizable. They develop theoretical results, analyzing the curvature of continuous curves in scale-space, which show the behavior of curvature extrema under varying scale. These results are used to eliminate any ambiguities that might arise from sampling problems due to the discreteness of the representation. Finally, experimental results demonstrate the potential of the method},
	keywords = {Automation, continuous curves, curvature, data mining, digital images, Educational institutions, Feature extraction, hierarchical curve representation, IMAGE PROCESSING, image recognition, image resolution, Image segmentation, multiresolution structure, Object recognition, planar curves, pyramid, Robustness, Sampling methods, Smoothing methods},
	isbn = {0-8186-2920-7},
	doi = {10.1109/ICPR.1992.201947},
	author = {Ferm{\"u}ller, Cornelia and Kropatsch,W.}
}
@conference {14233,
	title = {Multi-resolution shape description by corners},
	booktitle = {, 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1992. Proceedings CVPR {\textquoteright}92},
	year = {1992},
	month = {1992/06/15/18},
	pages = {271 - 276},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A robust method for describing planar curves in multiple resolution using curvature information is presented. The method is developed by taking into account the discrete nature of digital images as well as the discrete aspect of a multiresolution structure (pyramid). The main contribution lies in the robustness of the technique, which is due to the additional information that is extracted from observing the behavior of corners in the whole pyramid. Furthermore, the resulting algorithm is conceptually simple and easily parallelizable. Theoretical results are developed analyzing the curvature of continuous curves in scale-space and showing the behavior of curvature extrema under varying scale. The results are used to eliminate any ambiguities that might arise from sampling problems due to the discreteness of the representation. Experimental results demonstrate the potential of the method},
	keywords = {ambiguities, Automation, computational geometry, Computer vision, continuous curves, corners, curvature extrema, curvature information, curve fitting, digital images, Feature extraction, IMAGE PROCESSING, image resolution, Image segmentation, Laboratories, multiple resolution, multiresolution structure, parallelizable, planar curves, Robustness, Sampling methods, scale-space, SHAPE, Smoothing methods, varying scale},
	isbn = {0-8186-2855-3},
	doi = {10.1109/CVPR.1992.223264},
	author = {Ferm{\"u}ller, Cornelia and Kropatsch,W.}
}
