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作者： Wang, Xiaolong Ly, Vincent Sorensen, Scott Kambhamettu, Chandra Video/Image Modeling and Synthesis Laboratory Department of Computer and Information Sciences University of Delaware NewarkDE United States

ISBN: (纸本)9781479957514

Object recognition is an important problem with a wide range of applications. It is also a challenging problem, especially for animal categorization as the differences among breeds can be subtle. In this paper, based on statistical techniques for landmark-based shape representation, we propose to model the shape of dog breed as points on the Grassmann manifold. We consider the dog breed categorization as the classification problem on this manifold. The proposed scheme is tested on a dataset including 8,351 images of 133 different breeds. Experimental results demonstrate the advocated scheme outperforms state of the art approaches by nearly 20%. © 2014 IEEE.

关键词： Learning systems

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Shape from stereo and shading by gradient constrained interpolation

Shape from stereo and shading by gradient constrained interp...

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IEEE International Conference on image Processing

作者： M V Rohith Scott Sorensen Stephen Rhein Chandra Kambhamettu University of Delaware Newark DE US Video/Image Modeling and Synthesis Laboratory University of Delaware Newark DE

Textureless regions, though error prone in stereo, may contain shading information that may be exploited. Shape from shading (SFS) results relate to world coordinates by arbitrary scaling factors which are difficult to estimate. We propose a method for estimating dense disparities from sparse correspondences using SFS cues. We show that SFS can impose constraints on the gradient of disparity in textureless regions with constant albedo. Gradient Constrained Interpolation (GCI), which solves the estimation problem in one dimension, is presented. We efficiently generate paths between correspondences that cover the image and then use GCI to fill the pixels in between. Results are presented on real and synthetic images, and provide quantitative evaluations to show that the method outperforms baseline methods.

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Towards Auto-calibration of Smart Phones Using Orientation Sensors

Towards Auto-calibration of Smart Phones Using Orientation S...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Philip Saponaro Chandra Kambhamettu Video Image Modeling and Synthesis (VIMS) Laboratory Department of Computer and Information Science University of Delaware Newark DE USA

In this paper, we address the problem of auto calibration of cameras which can rotate freely and change focal length, and we present an algorithm for finding the intrinsic parameters using only two images. We utilize orientation sensors found on many modern smart phones to help decompose the infinite homography into two equivalent upper triangular matrices based only on the intrinsic parameters. We account for small translations between views by calculating the homography based on correspondences on objects that are far away from the camera. We show results based on both real and synthetic data, and quantify the tolerance of our system to small translations and errors in the orientation sensors. Our results are comparable to other recent auto-calibration work while requiring only two images and being tolerant to some translation.

关键词： Cameras Calibration Smart phones Sensors Equations Mathematical model Accuracy

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Can faces verify blood-relations?

Can faces verify blood-relations?

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IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS)

作者： Gowri Somanath Chandra Kambhamettu Video/Image Modeling and Synthesis (VIMS) Laboratory Department of Computer and Information Sciences University of Delaware Newark DE USA

Humans can verify unknown parent-offspring and sibling pairs over unrelated subject pairs. A computational scheme to accomplish the task robustly, in the presence of challenges due to gender and age gap between related-pairs, finds many applications such as matching orphaned/lost children, identifying relatives from a photo collection. We propose one of the first computational schemes to verify sibling pairs along with parent-child relation. Towards the same, we present a novel ensemble metric learning scheme that combines the advantages of task-specific learning, adaptive prototype and feature selection and `late fusion'. We demonstrate the robustness of the scheme on a very large scale, real-world dataset. Specifically, we show that the gender difference among related-pairs leads to lower performance of traditional verification and metric learning algorithms. Through various experiments, we quantitatively study the robustness of the proposed scheme in the general and specific case of different gender related-pairs, achieving up to 80%, 75% accuracy for the parent-child and siblings relations respectively.

关键词： Kernel Measurement Training Testing Robustness Support vector machines Covariance matrix

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Can discriminative cues aid face recognition across age?

Can discriminative cues aid face recognition across age?

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International Conference on Automatic Face and Gesture Recognition

作者： Gayathri Mahalingam Chandra Kambhamettu Video/Image Modeling and Synthesis (VIMS) Laboratory University of Delaware Newark DE USA

In this paper, we study the effects of discriminative cues (gender and age) in an image based face recognition system across age. We propose a pipeline framework to prune the search space based on gender and age of the test image to aid the process of recognition. A feature based approach which uses the Gabor phase and magnitude images of the face image is used in face image representation. The Gabor phases and magnitudes are encoded through Local Gabor Binary Pattern (LGBP) histograms. The gender and age group information of the face image is extracted using a random forest classifier based on a confidence measure and are used to prune the search space for efficient recognition. The effects of these discriminative cues are studied using a simple face recognition system in which the recognition is performed by histogram intersection technique which computes a similarity score between the LGBP histograms of two face images. The experiments on the FG-NET dataset and our private dataset show that the discriminative cues can indeed improve the performance of a face recognition system in terms of accuracy, lower time requirements, and graceful degradation.

关键词： Face Face recognition Histograms Feature extraction Accuracy Pixel Pipelines

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Age invariant face recognition using graph matching

Age invariant face recognition using graph matching

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IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS)

作者： Gayathri Mahalingam Chandra Kambhamettu Video Image Modeling and Synthesis (VIMS) Laboratory Department of Computer Science University of Delaware Newark DE USA

In this paper, we present a graph based face representation for efficient age invariant face recognition. The graph contains information on the appearance and geometry of facial feature points. An age model is learned for each individual and a graph space is built using the set of feature descriptors extracted from each face image. A two-stage method for matching is developed, where the first stage involves a Maximum a Posteriori solution based on PCA factorization to efficiently prune the search space and select very few candidate model sets. A simple deterministic algorithm which exploits the topology of the graphs is used for matching in the second stage. The experimental results on the FGnet database show that the proposed method is robust to age variations and provides better performance than existing techniques.

关键词： Feature extraction Face Aging Computational modeling Shape Training Kernel

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Face Recognition Using a Color Subspace LDA Approach

Face Recognition Using a Color Subspace LDA Approach

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： M. Thomas C. Kambhamettu S. Kumar Video Image Modeling and Synthesis Laboratory University of Delaware Newark DE USA Bell Laboratories Lucent Technologies Inc. Murray Hill NJ USA

This paper delves into the problem of face recognition using color as an important cue in improving the accuracy of recognition. To perform recognition of color images, we use the characteristics of a 3D color tensor to generate a color LDA subspace, which in turn can be used to recognize a new probe image. To test the accuracy of our methodology, we computed the recognition rate across two color face databases. We observe that the use of the LDA color subspace significantly improves recognition accuracy over the standard gray scale approach without sacrificing computational efficiency.

关键词： Face recognition Linear discriminant analysis Character recognition image recognition Color Tensile stress Character generation Probes Testing image databases

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Role of ice dynamics in the sea ice mass balance

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Eos 2008年第50期89卷 515-516页

作者： Hutchings, Jennifer Geiger, Cathleen Roberts, Andrew Richter-Menge, Jacqueline Doble, Martin Forsberg, Rene Giles, Katharine Haas, Christian Hendricks, Stefan Khambhamettu, Chandra Laxon, Seymour Martin, Torge Preis, Matthew Thomas, Mani Wadhams, Peter Zwally, H. Jay International Arctic Research Center University of Alaska Fairbanks Fairbanks AK United States Department of Geography University of Delaware Newark DE United States Artic Region Supercomputing Center International Arctic Research Center UAF Fairbanks AK United States Cold Regions Research and Engineering Laboratory U.S. Army Corps of Engineers Hanover NH United States Department of Applied Mathematics and Theoretical Physics (DAMTP) University of Cambridge Cambridge United Kingdom Danish National Space Center Copenhagen Denmark Centre for Polar Observation and Modelling (CPOM) University College London London United Kingdom Department of Earth and Atmospheric Sciences University of Alberta Edmonton AB Canada Alfred Wegener Institute for Polar and Marine Research Bremerhaven Germany Video Image Modeling and Synthesis Laboratory University of Delaware Newark DE United States CPOM NOAA Geophysical Fluid Dynamics Laboratory Princeton NJ United States NorthWest Research Associates Seattle WA United States DAMTP NASA Goddard Space Flight Center Greenbelt MD United States

Over the past decade, the Arctic Ocean and Beaufort Sea ice pack has been less extensive and thinner than has been observed during the previous 35 years [e.g., Wadhams and Davis, 2000; Tucker et al., 2001; Rothrock et al., 1999; Parkinson and Cavalieri, 2002; Comiso, 2002]. During the summers of 2007 and 2008, the ice extents for both the Beaufort Sea and the Northern Hemisphere were the lowest on record. Mechanisms causing recent sea ice change in the Pacific Arctic and the Beaufort Sea are under investigation on many fronts [e.g., Drobot and Maslanik, 2003; Shimada et al., 2006]; the mechanisms include increased ocean surface warming due to Pacific Ocean water inflow to the region and variability in meteorological and surface conditions. However, in most studies addressing these events, the impact of sea ice dynamics, specifically deformation, has not been measured in detail.

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Vector field characterization in ERS-1 imagery of sea ice

Vector field characterization in ERS-1 imagery of sea ice

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7th IEEE Workshop on Applications of Computer Vision, WACV 2007

作者： Thomas, M. Geiger, C.A. Kambhamettu, C. Video/Image Modeling and Synthesis Lab. University of Delaware Newark DE 19711 Snow and Ice Branch Cold Regions Research and Engineering Laboratory Hanover NH 03755 Center for Climatic Research University of Delaware

ISBN: (纸本)0769527949

The non rigid motion of sea ice is an essential component when describing global climatology models. With the availability of sequential ERS-1 satellite imagery, we estimate high resolution motion and describe the differential characteristics of motion. This characterization is subsequently used to locate critical points, also known as coherent structures, in the flow, thereby reducing the large vector field into a collection of important feature points which essentially describe the flow pattern. In this paper, we build on previous work in an attempt to enhance the characterization of the flow field via visualization and quantification of these coherent structures. We show that the statistical quantification of these structures can provide a greater clarity in our understanding of the physical dynamics taking place within the sea ice. © 2007 IEEE.

关键词： Satellite imagery

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Vector field characterization in ERS-1 imagery of sea ice

Vector field characterization in ERS-1 imagery of sea ice

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： M. Thomas C. A. Geiger C. Kambhamettu Video/Image Modeling and Synthesis Laboratory University of Delaware Newark DE USA Snow and Ice Branch Cold Regions Research and Engineering Laboratory Hanover NH USA

The nonrigid motion of sea ice is an essential component when describing global climatology models. With the availability of sequential ERS-1 satellite imagery, we estimate high resolution motion and describe the differential characteristics of motion. This characterization is subsequently used to locate critical points, also known as coherent structures, in the flow, thereby reducing the large vector field into a collection of important feature points which essentially describe the flow pattern. In this paper, we build on previous work in an attempt to enhance the characterization of the flow field via visualization and quantification of these coherent structures. We show that the statistical quantification of these structures can provide a greater clarity in our understanding of the physical dynamics taking place within the sea ice

关键词： Sea ice Motion estimation Satellites image resolution Availability Motion analysis Synthetic aperture radar image analysis Uncertainty Snow

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