A New Secure Image Transmission Technique via Secret-Fragment-Visible Mosaic Images by Nearly Reversible Color Transformations

Abstract

            A new secure image transmission technique is proposed, which transforms automatically a given large-volume secret image into a so-called secret-fragment-visible mosaic image of the same size. The mosaic image, which looks similar to an arbitrarily selected target image and may be used as a camouflage of the secret image, is yielded by dividing the secret image into fragments and transforming their color characteristics to be those of the corresponding blocks of the target image. Skillful techniques are designed to conduct the color transformation process so that the secret image may be recovered nearly losslessly. A scheme of handling the overflows/underflows in the converted pixels’ color values by recording the color differences in the untransformed color space is also proposed. The information required for recovering the secret image is embedded into the created mosaic image by a lossless data hiding scheme using a key. Good experimental results show the feasibility of the proposed method.

Domain : Image Processing

Contact Detail

Ph.no : 7200555526

e-mail: info.nanosoftwares@gmail.com

Robust Face Recognition from Multi-View Videos

Abstract

Multiview face recognition has become an active research area in the last few years. In this paper, we present an approach for video-based face recognition in camera networks. Our goal is to handle pose variations by exploiting the redundancy in the multiview video data. However, unlike traditional approaches that explicitly estimate the pose of the face, we propose a novel feature for robust face recognition in the presence of diffuse lighting and pose variations. The proposed feature is developed using the spherical harmonic representation of the face texture-mapped onto a sphere; the texture map itself is generated by back-projecting the multiview video data. Video plays an important role in this scenario. First, it provides an automatic and efficient way for feature extraction. Second, the data redundancy renders the recognition algorithm more robust. We measure the similarity between feature sets from different videos using the reproducing kernel Hilbert space. We demonstrate that the proposed approach outperforms traditional algorithms on a multiview video database.

Domain : Image Processing

Contact Detail

Ph.no : 7200555526

e-mail id : info.nanosoftwares@gmail.com

P-FAD: Real-Time Face Detection Scheme on Embedded Smart Camera

Abstract

            Face detection on general embedded devices is fundamentally different from the conventional approach on personal computer or consumer digital camera due to the limited computation and power capacity. The resource-limited characteristic gives rise to new challenges for implementing a real-time video surveillance system with smart cameras. In this work, we present the design and implementation of Pyramid-like FAce Detection (P-FAD), a real-time face detection system constructed on general embedded devices. Motivated by the observation that the computation overhead increases proportionally to its pixel manipulation, P-FAD proposes a hierarchical approach to shift the complex computation to the promising regions. More specifically, P-FAD present a three-stage coarse, shift, and refine procedure, to construct a pyramid-like detection framework for reducing the computation overhead significantly. This framework also strikes a balance between the detection speed and accuracy. We have implemented P-FAD on notebook, Android phone and our embedded smart camera platform. An extensive system evaluation in terms of detailed experimental and simulation results is provided. Our empirical evaluation shows that P-FAD outperforms V-J detector calibrated color detector (VJ-CD) and color detector followed by a V-J detector (CD-VJ), the state of the art real-time face detection techniques by 4.7 –8.6 on notebook and by up to 8.2 on smart phone in terms of the detection speed.

Domain : Image Processing

Contact Details

Ph.no : 7200555526

e-mail id : info.nanosoftwares@gmail.com

An Unbiased Risk Estimator for Image Denoising in the Presence of Mixed Poisson–Gaussian Noise

Abstract

            The behavior and performance of denoising algorithms are governed by one or several parameters, whose optimal settings depend on the content of the processed image and the characteristics of the noise, and are generally designed to minimize the mean squared error (MSE) between the denoised image returned by the algorithm and a virtual ground truth. In this paper, we introduce a new Poisson-Gaussian unblased risk estimator (PG-URE) of the MSE applicable to a mixed Poisson-Gaussian noise model that unifies the widely used Gaussian and Poisson noise model in fluorescence bioimaging applications. We propose a stochastic methodology to evaluate this estimator in the case when little is known about the internal machinery of the considered denoising algorithm, and we analyze both theoretically and empirically the characteristics of the PG-URE estimator. Finally, we evaluate the PG-URE-driven parametrization for three standard denoising algorithms, with and without variance stabilizing tranforms, and different characteristics of the Poisson-Gaussian noise mixture.

Domain : Image Processing

Contact Details

Ph.no : 7200555526

e-mail id : info.nanosoftwares@gmail.com