Catálogo de publicaciones - libros

An aspect ratio invariant visual secret sharing (ARIVSS) scheme is a perfectly secure method for sharing secret images, by expanding a secret pixel to sub pixels in shadow images, with being the pixel expansion; meantime the aspect ratio of the recovered secret image is fixed. The advantage of ARIVSS is that there is no loss of information when the shape of the secret image is our information; for example, a secret image of a circle is compromised to an ellipse, if does not have a square value. Two ARIVSS schemes, based on processing one and four pixel blocks, respectively, were previously proposed. In this paper, we have generalized the square block-wise approach, to further reduce pixel expansion.

This paper explores the statistics of least-significant bit (LSB) steganography. The problem of encoding a bit sequence (message) to match the statistics of a random bit-sequence (cover) is considered. A method of hiding information in the least significant bits (LSBs) of JPEG coefficients is described; the method mimics either the chi-square statistic of JPEG coefficients or their distribution. The method uses two-bit codes to encode the message bits. It is shown to be very effective on JPEG images of natural scenes.

An extended visual secret sharing (EVSS) scheme with innocent looking (unsuspicious) cover images was firstly proposed by Naor and Shamir. Most recent papers about EVSS schemes are dedicated to get a higher contrast of the concealed secret or a less size of shadow image. The conventional EVSS scheme uses the whiteness of black and white sub pixels to represent the gray level of the covered image while we use the gray sub pixels instead to achieve the high-quality shadow image. The term “high-quality” means that the shadow has high-quality image such as a photo picture.

Digital images are increasingly being used as steganographic covers for secret communication. The Least Significant Bit (LSB) encoding is one of the most widely used methods for embedding a message in a digital image. However, the direct application of LSB encoding is vulnerable to steganalysis. For example, RS steganalysis is very efficient in detecting the presence of a message in a digital image and to estimate its approximate size. This paper presents a method robust to RS steganalysis, that makes the presence of a message unnoticeable. The method is based on the application of reversible histogram transformation functions to the image, before and after embedding the secret message. The method was tested on 4 greyscale images, with messages of 10%, 30% and 90% of the maximum embedding size. The proposed method proved to be effective in eluding RS steganalysis for all cases tested.

In this paper, a new Target Density Function(TDF) is theorized to image the radar targets by a new estimation algorithm. TDF is represented in a specified manner. This method is developed by inspiring of ambiguity functions. TDF is obtained in the range and scanning angle plane different from Fowle-Naparst’s methods. Target density function is produced via a linear phased array radar system. This is another gain of the method. In addition to scanning, targeting and imaging properties, by this way, the problem associated with beamforming is bypassed.

In this paper, an adaptive scene-based nonuniformity and ghosting artifacts correction algorithm for infrared image sequences is presented. The method simultaneously estimates detector parameters and carry out the non-uniformity and ghosting artifacts correction based on the retina-like neural network approach. The method incorporates the use of a new adaptive learning rate rule into the estimation of the gain and the offset of each detector. This learning rule, together with the consideration of the dependence of the detector’s parameters on the retinomorphic assumption used for parameter estimation, may sustain an efficient method that could not only increase the original method’s ability for estimating the non-uniformity noise, but also increase the capability of mitigating ghosting artifacts. The ability of the method to compensate for nonuniformity and reducing ghosting artifacts is demonstrated by employing several infrared video sequences obtained using two infrared cameras.

Programmable graphics hardware have proven to be a powerful resource for general computing. Previous research has shown that using a GPU for local image processing operations can be much faster than using a CPU. The actual speedup obtained is influenced by many factors. In this paper, we quantify the performance gain that can be achieved by using the GPU for different image processing operations under different conditions. We also compare the strengths and weaknesses of two of the current leaders in mainstream GPUs – ATI’s Radeon and nVidia’s GeForce FX. Many interesting observations are obtained through the evaluation.

Background updating is an important aspect of dynamic scene analysis. Two critical problems: sudden camera perturbation and the problem, which arise frequently in real-world surveillance and monitoring systems, are addressed in the proposed scheme. The paper presents a multi-color model where multiple color clusters are used to represent the background at each pixel location. In the proposed background updating scheme, the updates to the mean and variance of each color cluster at each pixel location incorporate the most recently observed color values. Each cluster is assigned a weight which measures the time duration and temporal recurrence frequency of the cluster. The problem is tackled by virtue of the observation that at a given pixel location, the time durations and recurrence frequencies of the color clusters representing temporarily static objects are smaller compared to those of color clusters representing the true background colors when measured over a sufficiently long history. The camera perturbation problem is solved using a fast camera motion detection algorithm, allowing the current background image to be registered with the background model maintained in memory. The background updating scheme is shown to be robust even when the scene is very busy and also computationally efficient, making it suitable for real-time surveillance and monitoring systems. Experimental results on real traffic monitoring and surveillance videos are presented.

In a video surveillance system, moving object detection is the most challenging problem especially if the system is applied in complex environments with variable lighting, dynamic and articulate scenes, etc.. Furthermore, a video surveillance system is a real-time application, so discouraging the use of good, but computationally expensive, solutions. This paper presents a set of improvements of a basic background subtraction algorithm that are suitable for video surveillance applications. Besides we present a new evaluation scheme never used in the context of moving object detection algorithms.

A hardware-oriented block matching algorithm and its area-efficient VLSI implementation are presented. The proposed technique benefits from the simplicity of the Pixel Difference Classification algorithm (PDC) , further exploits the inherence of the characteristics of the data being processed, and the goal of an area-efficient implementation is reached. A quality investigation based on processing video sequences confirms the stability and performance of the proposed algorithm when compared with the conventional full-search as well as low-complexity techniques. Realized in TSMC 0.18-micron CMOS technology the chip has a core area of 1.01. For a comparable video quality, the proposed implementation has shown a significant silicon area deduction compared with the recently published conventional implementations.