In this paper, we propose a new learning based approach for super-resolving an image captured at low spatial resolution. Given the low spatial resolution test image and a training set consisting of low and high spatia...
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In this paper, we propose a new learning based approach for super-resolving an image captured at low spatial resolution. Given the low spatial resolution test image and a training set consisting of low and high spatial resolution images, all captured using the same camera, we obtain super-resolution for the test image. We propose a new wavelet based learning technique that learns the high frequency details for the test image from the training set and thus obtain an initial high resolution estimate. Since super-resolution is an ill-posed problem we solve it using regularization framework. We model the low resolution image as the aliased and noisy version of the corresponding high resolution image and estimate the aliasing matrix using the test image and the initial high resolution (HR) estimate. The super-resolved image is modeled as an inhomogeneous Gaussian Markov Random Field (IGMRF) and the IGMRF prior model parameters are estimated using the initial HR estimate. Finally, the cost function formed is minimized using simple gradient descent approach. We demonstrate the effectiveness of the proposed approach by conducting experiments on gray scale as well as on color images. The method is compared with another existing learning-based approach which uses training set consisting of HR images only and employs autoregressive (AR) and wavelet priors. The advantage of the our approach when compared to motion-based methods is that there is no need of multiple observations and also registration. The proposed approach can be used in applications such as wildlife sensor network where memory, transmission bandwidth and camera cost are main constraints.
The modern remote sensing imaging sensors, like those in the IKONOS and QuickBird satellites, are capable of generating panchromatic images with one meter spatial resolution and multispectral images with good spectral...
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The modern remote sensing imaging sensors, like those in the IKONOS and QuickBird satellites, are capable of generating panchromatic images with one meter spatial resolution and multispectral images with good spectral information. The principal objective of fusion in remote sensing is to obtain high-resolution multispectral images that can combine the spectral characteristic of the low-resolution multispectral images with the spatial information of the high-resolution panchromatic images. Traditional fusion methods, such as IHS, PCA and Brovey, can reach good spatial resolution results, but often cause spectral distortion problems. In the literature, it is possible to find some image fusion methods using frequency domain processing, like wavelet-based fusion methods. Although they preserve good spectral information, their spatial visual effects are not satisfactory. IHS fusion method enhanced by Fourier transform presents good spectral and spatial resolution results, but limits the number of spectral bands used in the fusion process to three. In this paper, a method based on Fourier transform is proposed in order to obtain good spatial and spectral resolutions, without limiting the number of bands. In order to compare the spatial and spectral effects of this new method with those of IHS, IHS enhanced by Fourier transform and wavelet-based methods, IKONOS panchromatic and multispectral images were used as the test data. Quantitative measurements such as correlation coefficient, discrepancy and Mean Structural Similarity index were applied to evaluate the quality of the fused images. The results have shown that the new method can keep almost the same spatial resolution as the panchromatic images, and its spectral effect is well preserved.
The light field rendering method is an interesting variation on achieving realism. Once authentic imagery has been acquired using a camera gantry, or a handheld camera, detailed novel views can be synthetically genera...
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The light field rendering method is an interesting variation on achieving realism. Once authentic imagery has been acquired using a camera gantry, or a handheld camera, detailed novel views can be synthetically generated from various viewpoints. One common application of this technique is when a user "walks" through a virtual world. In this situation, only a subset of the previously stored light field is required, and considerable computation burden is encountered in processing the input light field to obtain this subset. In this paper, we show that appropriate portions of the light field can be cached at select "nodal points" that depend on the camera walk. Once spartanly and quickly cached, scenes can be rendered from any point on the walk efficiently. (c) 2006 Elsevier B.V. All rights reserved.
We propose a face recognition method that fuses information acquired from global and local features of the face for improving performance. Principle components analysis followed by Fisher analysis is used for dimensio...
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We propose a face recognition method that fuses information acquired from global and local features of the face for improving performance. Principle components analysis followed by Fisher analysis is used for dimensionality reduction and construction of individual feature spaces. Recognition is done by probabilistically fusing the confidence weights derived from each feature space. The performance of the method is validated on FERET and AR databases. (c) 2006 Elsevier B.V. All rights reserved.
We present a novel eigenspace-based framework to model a dynamic hand gesture that incorporates both hand shape as well as trajectory information. We address the problem of choosing a gesture set that models an upper ...
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We present a novel eigenspace-based framework to model a dynamic hand gesture that incorporates both hand shape as well as trajectory information. We address the problem of choosing a gesture set that models an upper bound on gesture recognition efficiency. We show encouraging experimental results on a such a representative set. (c) 2006 Elsevier B.V. All rights reserved.
We have attempted the problem of novel view synthesis of scenes containing man-made objects from images taken by arbitrary, uncalibrated cameras. Under the assumption of availability of the correspondence of three van...
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We have attempted the problem of novel view synthesis of scenes containing man-made objects from images taken by arbitrary, uncalibrated cameras. Under the assumption of availability of the correspondence of three vanishing points, in general position, we propose two techniques. The first is a transfer-based scheme which synthesizes new views with only a translation of the virtual camera and computes z-buffer values for handling occlusions in synthesized views. The second is a reconstruction-based scheme which synthesizes arbitrary new views in which the camera can undergo rotation as well as translation. We present experimental results to establish the validity of both formulations. (c) 2006 Published by Elsevier B.V.
An intrinsic property of real aperture imaging has been that the observations tend to be defocused. This artifact has been used in an innovative manner by researchers for depth estimation, since the amount of defocus ...
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An intrinsic property of real aperture imaging has been that the observations tend to be defocused. This artifact has been used in an innovative manner by researchers for depth estimation, since the amount of defocus varies with varying depth in the scene. There have been various methods to model the defocus blur. We model the defocus process using the model of diffusion of heat. The diffusion process has been traditionally used in low level vision problems like smoothing, segmentation and edge detection. In this paper a novel application of the diffusion principle is made for generating the defocus space of the scene. The defocus space is the set of all possible observations for a given scene that can be captured using a physical lens system. Using the notion of defocus space we estimate the depth in the scene and also generate the corresponding fully focused equivalent pin-hole image. The algorithm described here also brings out the equivalence of the two modalities, viz. depth from focus and depth from defocus for structure recovery. (c) 2006 Elsevier B.V. All rights reserved.
In content based image retrieval (CBIR) system, search engine retrieves the images similar to the query image according to a similarity measure. It should be fast enough and must have a high precision of retrieval. In...
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In content based image retrieval (CBIR) system, search engine retrieves the images similar to the query image according to a similarity measure. It should be fast enough and must have a high precision of retrieval. Indexing scheme is used to achieve a fast response and relevance feedback helps in improving the retrieval precision. In this paper, a human perception based similarity measure is presented and based on it a simple yet novel indexing scheme with relevance feedback is discussed. The indexing scheme is designed based on the primary and secondary keys which are selected by analysing the entropy of features. A relevance feedback method is proposed based on Mann-Whitney test. The test is used to identify the discriminating features from the relevant and irrelevant images in a retrieved set. Then emphasis of the discriminating features are updated to improve the retrieval performance. The relevance feedback scheme is implemented for two different similarity measure (Euclidean distance based and human perception based). The experiment justifies the effectiveness of the proposed methodologies. Finally, the indexing scheme and relevance feedback mechanism are combined to build up the search engine. (c) 2006 Elsevier B.V. All rights reserved.
Robustness is a key attribute of spread spectrum (SS) watermarking scheme. It is significantly deteriorated if one tries to achieve high embedding rate keeping other parameters unaltered. In literatures, typically var...
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Robustness is a key attribute of spread spectrum (SS) watermarking scheme. It is significantly deteriorated if one tries to achieve high embedding rate keeping other parameters unaltered. In literatures, typically various transformations like DFT, DCT, Fourier-Mellin and wavelet are used for SS multimedia watermarking but little studies have been attempted so far to see what are the possible factors which can improve robustness. The current paper has critically analyzed few such factors namely design of code pattern, proper signal decomposition suitable for data embedding, direction of decomposition, selection of regions for data embedding, signaling scheme, choice of modulation functions and embedding strength. Based on the observation, wavelet based SS watermarking scheme is proposed and improvement in robustness performance is verified through experimental results as well by mathematical analysis. (c) 2006 Elsevier B.V. All rights reserved.
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