In this paper a new robust watermarking scheme is proposed in multiresolution fractional Fourier transform domain using singular value decomposition. the watermark is embedded in the high frequency sub-band of the hos...
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ISBN:
(纸本)9781424442195
In this paper a new robust watermarking scheme is proposed in multiresolution fractional Fourier transform domain using singular value decomposition. the watermark is embedded in the high frequency sub-band of the host image at coarsest level. Although the schemes based on SVD are robust but fail under ambiguity attacks. In this attack, boththe owner and attacker can extract their watermark from the watermarked image. To prevent ambiguity, the normalized mass matrix is formed and embedded in the host image. In extraction, normalized mass matrix is extracted first and compared with original one. If the similarity is found then the singular values are extracted to construct the watermark. Experimental evaluation demonstrates that the proposed scheme is able to withstand a variety of attacks.
In point clouds obtained from airborne data, the ground points have traditionally been identified as local minima of the altitude. Subsequently, the 2.5D digital terrain models have been computed by approximation of a...
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ISBN:
(纸本)9789897584022
In point clouds obtained from airborne data, the ground points have traditionally been identified as local minima of the altitude. Subsequently, the 2.5D digital terrain models have been computed by approximation of a smooth surfaces from the ground points. But how can we handle purely 3D surfaces of cultural heritage monuments covered by vegetation or Alpine overhangs, where trees are not necessarily growing in bottom-to-top direction? We suggest a new approach based on a combination of superpoints and RANSAC implemented as a filtering procedure, which allows efficient handling of large, challenging point clouds without necessity of training data. If training data is available, covariance-based features, point histogram features, and dataset-dependent features as well as combinations thereof are applied to classify points. Results achieved with a Random Forest classifier and non-local optimization using Markov Random Fields are analyzed for two challenging datasets: an airborne laser scan and a photogrammetrically reconstructed point cloud. As an application, surface reconstruction from the thus cleaned point sets is demonstrated.
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 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.
In this paper, a novel approach for the verification of offline handwritten signatures is proposed. Despite tremendous growth of digital technologies in the last 4 decades, the most used authentication method today re...
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ISBN:
(纸本)9781467385640
In this paper, a novel approach for the verification of offline handwritten signatures is proposed. Despite tremendous growth of digital technologies in the last 4 decades, the most used authentication method today remains to be handwritten signature. It is the most natural method of authenticating a person's identity as compared to other biometric and cryptographic forms of authentication. We propose a method for verifying the signatory's identity by using Zernike Moments as global shape descriptors. Zernike Moments are image moments that are rotation invariant. the moments are also orthogonal on a unit circle which ensures minimum redundancy between the features representing the object shape. the features extracted in our approach have a relatively low dimensionality as compared to other studies, while retaining high representation power of the moments. Moreover, the module developed using our approach was able to demonstrate high performance coupled with low computation times in testing phase, making it suitable for real time applications. Experiments show high overall performance of our approach with an equal error rate EER of 13.42% and area under the curve A(z) equal to 0.84 using 1564 images from the NFI SigComp2009 dataset.
the goal of this article is twofold. First, it deals with color image segmentation in hue-saturation space. A model for circular data is provided by the vM-Gauss distribution, which is a joint distribution of von-Mise...
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ISBN:
(纸本)9781424442195
the goal of this article is twofold. First, it deals with color image segmentation in hue-saturation space. A model for circular data is provided by the vM-Gauss distribution, which is a joint distribution of von-Mises and Gaussian distributions. the mixture of W-Gauss distributions is used to model hue-saturation data. After segmentation, a post processing based on both spectral and spatial similarity of clusters is applied to separate such identifiable objects in the image. the results and comparisons are shown on Berkeley segmentation dataset. the problem of text extraction from a color image is taken as an application of the proposed method. We use a laboratory made text image dataset to test the method.
We describe a low memory, parallelizable implementation of graph cut based MRF energy minimization that solves pixel labeling problems. We first solve the problem on a low resolution version of the image and make use ...
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ISBN:
(纸本)9781424442195
We describe a low memory, parallelizable implementation of graph cut based MRF energy minimization that solves pixel labeling problems. We first solve the problem on a low resolution version of the image and make use of the technique of hierarchical graph cuts [2],[9] to obtain a narrow band of uncertainty in the high resolution image within which the labeling needs to be solved. We then sub-divide the narrow band into overlapping regions and solve the labeling problem for each of the regions separately. the overlapping regions are used to provide boundary conditions that force the labeling to be continuous. the key advantages of the method are low memory usage, cache friendliness and the Potential, for parallel execution. the solutions obtained are close to the global optimum. We demonstrate our method on the bi-label image segmentation and the multi-label image stitching problems.
In this paper, a robust image hashing framework is presented using discrete cosine transformation and singular value decomposition. Firstly, the input image is normalized using geometric moment and normalized coeffici...
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ISBN:
(纸本)9781450366151
In this paper, a robust image hashing framework is presented using discrete cosine transformation and singular value decomposition. Firstly, the input image is normalized using geometric moment and normalized coefficients are divided into non-overlapping blocks. the selected blocks based on a peace-wise non-linear chaotic map are transformed using discrete cosine transom followed by singular value decomposition. then a feature matrix is constructed in reliance on Hessian matrix and the final hash values are obtained. the proposed hashing system is resilient to different content-preserving image distortions such as geometric and filtering operations. the simulated results demonstrate the efficiency proposed framework in terms of security and robustness.
One of the common image forgery techniques is the splicing, where parts from different images are copied and pasted onto a single image. this paper proposes a new forensics method for detecting splicing forgeries in i...
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ISBN:
(纸本)9781450347532
One of the common image forgery techniques is the splicing, where parts from different images are copied and pasted onto a single image. this paper proposes a new forensics method for detecting splicing forgeries in images containing human faces. Our approach is based on extracting an illumination-signature from the faces of people present in an image using the dichromatic reflection model (DRM). the dichromatic plane histogram (DPH), which is calculated by applying the 2D Hough Transform on the face images, is used as the illumination-signature. the correlation measure is employed to compute the similarity between the DPHs obtained from different faces present in an image. Finally, a simple threshold on this similarity measure exposes splicing forgeries in the image. Experimental results show the efficacy of the proposed method.
Using the edge detection techniques we propose a new enhancement scheme for noisy digital images. this uses inhomogeneous anisotropic diffusion scheme via the edge indicator provided by well known edge detection metho...
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ISBN:
(纸本)9781424442195
Using the edge detection techniques we propose a new enhancement scheme for noisy digital images. this uses inhomogeneous anisotropic diffusion scheme via the edge indicator provided by well known edge detection methods. Addition of a fidelity term facilitates the proposed scheme to remove the noise while preserving edges. this method is general in the sense that it can be incorporated into any of the nonlinear anisotropic diffusion methods. Numerical results show the promise of this hybrid technique on real and noisy images.
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