This paper proposes a new method for the exact reconstruction of gray-scale images from projections. The image projections construct an accumulator array, which is used afterwards to reconstruct the original grayscale...
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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.
Given an FIR filter, this paper addresses a time-domain means of arriving at its inverse filter in FIR form. To this end, the original FIR filter should lack frequency nulls so that an inverse filter of reasonable sup...
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ISBN:
(纸本)9781932415957
Given an FIR filter, this paper addresses a time-domain means of arriving at its inverse filter in FIR form. To this end, the original FIR filter should lack frequency nulls so that an inverse filter of reasonable support size can be established. With this approach we provide an alternative to the more commonly employed frequency design methods and also provide insight to the significance of the time domain operations being performed. A detailed description of the approach is provided and a designed inverse filter is used for the purposes of image deconvolution.
An approach to the deconvolution of blurred images in additive noise is presented. This approach is based on the use of noise moment and range constraints within a Lagrange optimization framework. Two types of noise m...
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ISBN:
(纸本)9781932415957
An approach to the deconvolution of blurred images in additive noise is presented. This approach is based on the use of noise moment and range constraints within a Lagrange optimization framework. Two types of noise moment constraints are examined: standard moments and probability weighted moments. In addition, the range constraints are enforced via an auxiliary mapping such that the optimization can be performed in an unconstrained manner. We report results on several deconvolution experiments and compare them against the Wiener filter so as to make clear the benefits and utility of our approach.
In night time surveillance, there is a possibility of having extremely bright and dark regions in some image frames of a video sequence. A novel non linear image enhancement algorithm for digital images captured under...
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作者：
J.S. ShaikM. YeasinComputational Vision
Pattern and Image Analysis Laboratory Department of Electrical and Computer Engineering University of Memphis Memphis TN USA
This paper presents the implementation and evaluation of subspace-based clustering algorithm for robust selection of differentially expressed genes as well as the classification of tissue types from microarray data. T...
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This paper presents the implementation and evaluation of subspace-based clustering algorithm for robust selection of differentially expressed genes as well as the classification of tissue types from microarray data. The performance of the proposed algorithm is compared against other well known clustering algorithms and the quality of clusters is evaluated using a number of cluster validation indices. Empirical analyses on a number of synthetic and real microarray data sets suggest that the proposed subspace-based algorithm is robust in selecting differentially expressed genes and performs significantly better compared to popular clustering algorithms in selecting differentially expressed genes and classifying different tissue types.
In this poster, we present an approach to contex-tualized semantic image annotation as an optimization problem. Ontologies are used to capture general and contextual knowledge of the domain considered, and a genetic a...
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In this poster, we present an approach to contex-tualized semantic image annotation as an optimization problem. Ontologies are used to capture general and contextual knowledge of the domain considered, and a genetic algorithm is applied to realize the final annotation. Experiments with images from the beach vacation domain demonstrate the performance of the proposed approach and illustrate the added value of utilizing contextual information.
Although the presence of local minima is one of the major problems in high-dimensional image registration, only a few experimental works have been carried out to address this problem. In this study, a 3D-2D vascular i...
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ISBN:
(纸本)1904410146
Although the presence of local minima is one of the major problems in high-dimensional image registration, only a few experimental works have been carried out to address this problem. In this study, a 3D-2D vascular image feature-based registration is done by producing Digital Reconstructed Radiographs (DRRs) of 3D images to match against the target 2D images. In addition, we propose a global optimization method based on the use of Powell's method at different resolution levels. To search the global minimum as effectively as possible, a large set of sample test points are systematically generated. The values of dissimilarity to the registered images in lower resolution environment are calculated. Powell's method is then applied to those test points with the lowest values for further minimization in the higher resolution. It is experimentally shown that our method can identify the global optimum in a normal clinical setting. The findings can have potential usage in the reconstruction of 3D models (e.g. guide wires) based on 2D medical visual information.
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
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