An efficient algorithm using maximum a posteriori-Markov random field (MAP-MRF) based approach for recovering a high-resolution image from multiple sub-pixel shifted low-resolution images is proposed. The algorithm ca...
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An efficient algorithm using maximum a posteriori-Markov random field (MAP-MRF) based approach for recovering a high-resolution image from multiple sub-pixel shifted low-resolution images is proposed. The algorithm can be used for super-resolution of both space-invariant and space-variant blurred images. We prove an important theorem that the posterior is also Markov and derive the exact posterior neighborhood structure in the presence of warping, blurring and down-sampling operations. The posterior being Markov enables us to perform all matrix operations as local image domain operations thereby resulting in a considerable speedup. Experimental results are given to demonstrate the effectiveness of our method
Shape from focus (SFF) method determines the degree of focus in a sequence of observations to estimate the shape of a 3-D object. Existing SFF algorithms use an ad hoc interpolation strategy to account for the error d...
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ISBN:
(纸本)0769525210
Shape from focus (SFF) method determines the degree of focus in a sequence of observations to estimate the shape of a 3-D object. Existing SFF algorithms use an ad hoc interpolation strategy to account for the error due to the finite step-size by which the translational table is moved while capturing the images. We propose an improved SFF method that uses relative defocus blur derived from actual image data to arrive at the final estimates of the shape of the object. A space-variant image restoration scheme is also proposed to obtain a focused image of the 3-D object. The shape estimates as well as the quality of the restored image using the proposed method are superior to that of traditional SFF
This paper presents a novel line-based affine invariant object location methodology. Our algorithm employs a new line-based transformation space decomposition technique to exploit intrinsic structural information prov...
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This paper presents a novel line-based affine invariant object location methodology. Our algorithm employs a new line-based transformation space decomposition technique to exploit intrinsic structural information provided by line features. Furthermore, we propose a new line-based distance transform to integrate with our algorithm to provide efficient transformation cell evaluation and subdivision in a coarse to fine manner. The algorithm is able to rapidly accelerate the searching process while maintaining high discriminative power and minimal storage requirement. The efficiency and discriminative power of this methodology are demonstrated using real-world examples with promising results
One possible solution for pose- and illumination-invariant face recognition is to employ appearance-based approaches, which rely greatly on correct facial textures. However, existing facial texture analysis algorithms...
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ISBN:
(纸本)0769525210
One possible solution for pose- and illumination-invariant face recognition is to employ appearance-based approaches, which rely greatly on correct facial textures. However, existing facial texture analysis algorithms are suboptimal, because they usually neglect specular reflections and require numerous training images for virtual view synthesis. This paper presents a novel texture synthesis approach from a single frontal view for face recognition. Using a generic 3D face shape, facial textures are analyzed with consideration of all of the ambient, diffuse, and specular reflections. Virtual views are synthesized under different poses and illuminations. The proposed approach was evaluated using the CMU-PIE face database. Encouraging results show that the proposed approach improves face recognition performances across pose and illumination variations
作者:
J.S. ShaikM. YeasinComputer Vision
Pattern and Image Analysis Laboratory Electrical and Computer Engineering University of Memphis Memphis TN USA
This paper presents a 3D star coordinate-based visualization technique for exploratory data analysis. To improve the data visualization and reveal the hidden patterns in complex high dimensional data sets, first the 2...
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This paper presents a 3D star coordinate-based visualization technique for exploratory data analysis. To improve the data visualization and reveal the hidden patterns in complex high dimensional data sets, first the 2D star coordinate system is extended to the 3D star coordinate system. An autonomous procedure is defined to find the best configuration for the 3D star coordinate system based on cluster validation measures. To illustrate the efficacy of the proposed techniques, empirical analysis were conducted on a number of synthetic (Five dimensional Gaussian distribution with three classes) and real (Fisher's IRIS, Leukemia, Gastric cancer and Petroleum datasets) databases. Empirical analyses shows that automated 3D star coordinate system helps in better visualization of the complex high dimensional data when compared to 2D star coordinate system and also other projection-based visualization techniques. Also the automated configuration for 3D star coordinate system reveals the hidden patterns in the complex datasets without human intervention.
作者:
J.S. ShaikM. YeasinComputer Vision
Pattern and Image Analysis Laboratory Department of Electrical and Computer Engineering University of Memphis Memphis TN USA
This paper presents an adaptive subspace based two-way clustering of microarray data. To analyze the data at various scales a "Progressive" framework is introduced. The goals are to functionally classify gen...
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This paper presents an adaptive subspace based two-way clustering of microarray data. To analyze the data at various scales a "Progressive" framework is introduced. The goals are to functionally classify genes and also to find differentially expressed genes in microarray expression profiles. Empirical analysis on Colon Cancer dataset shows that ASI performs favorably in grouping genes with similar functions and finding genes that may have been involved in the formation of colon cancer. It was also observed that the proposed algorithm is robust against ordering of samples and yield results consistent with ground truth information.
Tolerance to pose variations is one of the key remaining problems in face recognition. It is of great interest in airport surveillance systems using mugshot databases to screen travellers' faces. This paper presen...
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ISBN:
(纸本)0769525210
Tolerance to pose variations is one of the key remaining problems in face recognition. It is of great interest in airport surveillance systems using mugshot databases to screen travellers' faces. This paper presents a novel pose-invariant face recognition approach using two orthogonal face images from mugshot databases. Virtual views under different poses are generated in two steps: shape modeling and texture synthesis. In the shape modeling step, a feature-based multilevel quadratic variation minimization approach is applied to generate smooth 3D face shapes. In the texture synthesis step, a non-Lambertian reflectance model is explored to synthesize facial textures taking into account both diffuse and specular reflections. A view-based face recognizer is used to examine the feasibility and effectiveness of the proposed pose-invariant face recognition. The experimental results show that the proposed method provides a new solution to the problem of recognizing rotated faces
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