Tamer Rabie

Animat vision: Active vision in artificial animals

We propose and demonstrate a new paradigm for active vision research that draws upon recent advances in the fields of artificial life and computer graphics. A software alternative to the prevailing hardware vision mindset, animat vision prescribes artificial animals, or animats, situated in physics-based virtual worlds as autonomous virtual robots possessing active perception systems. To be operative in its world, an animat must autonomously control its eyes and muscle-actuated body, applying computer vision algorithms to continuously analyze the retinal image streams acquired by its eyes in order to locomote purposefully through its world. We describe an initial animat vision implementation within lifelike artificial fishes inhabiting a physics-based, virtual marine world. Emulating the appearance, motion, and behavior of real fishes in their natural habitats, these animats are capable of spatially nonuniform retinal imaging, foveation, retinal image stabilization, color object recognition, and perceptually-guided navigation. These capabilities allow them to pursue moving targets such as fellow artificial fishes. Animat vision offers a fertile approach to the development, implementation, and evaluation of computational theories that profess sensorimotor competence for animal or robotic situated agents.<<ETX>>

Robust estimation approach for blind denoising

This work develops a new robust statistical framework for blind image denoising. Robust statistics addresses the problem of estimation when the idealized assumptions about a system are occasionally violated. The contaminating noise in an image is considered as a violation of the assumption of spatial coherence of the image intensities and is treated as an outlier random variable. A denoised image is estimated by fitting a spatially coherent stationary image model to the available noisy data using a robust estimator-based regression method within an optimal-size adaptive window. The robust formulation aims at eliminating the noise outliers while preserving the edge structures in the restored image. Several examples demonstrating the effectiveness of this robust denoising technique are reported and a comparison with other standard denoising filters is presented.

Image restoration by adaptive-neighborhood noise subtraction.

A new algorithm for image restoration in the presence of additive white Gaussian noise is presented. This algorithm is based on a new, adaptive method to estimate the additive noise. The basic idea in this technique is to identify uniform structures or objects in the image by use of an adaptive neighborhood and to estimate the noise and the signal content in these areas separately. The noise is then subtracted selectively from the seed pixel of the adaptive neighborhood, and the process is repeated at every pixel in the image. The algorithm is compared with the adaptive two-dimensional least-mean-squares and the adaptive rectangular-window least-mean-squares algorithms for noise suppression. The results from the application of these algorithms to synthesized images and natural scenes are presented along with mean-squared-error measures. The new algorithm performs better than the other two methods both in terms of visual presentation and mean-squared error.

Mobile vision-based vehicle tracking and traffic control

This paper discusses work-in-progress to develop a mobile, bus-mounted machine vision system for transit and traffic monitoring in urban corridors, as required by Intelligent Transportation Systems. In contrast to earlier machine vision technologies used for traffic management, which mainly rely on simple algorithms to detect certain traffic characteristics, the new proposed approach makes use of a recent trend in computer vision research: namely the active vision paradigm. Active vision systems have mechanisms that can actively control camera parameters, such as orientation, focus, zoom, and convergence, in response to the requirements of the task and external stimuli. Mounting active vision systems on buses will have the advantage of providing real-time feedback of the current traffic conditions while possessing the intelligence and visual skills which allow them to interact with a rapidly changing dynamic environment such as moving traffic.

A novel covert channel based on the IP header record route option

In this paper we propose a novel covert channel for exchanging secret information, based on the IP header record route options. Instead of encrypting a secret message or embedding it into a multimedia object, as in traditional steganography, we process the entire message and generate several IP packets with different types to carry the secret information. Thereby we foil an eavesdropper who is primarily applying statistical tests to detect encrypted channels. We show that our approach provides more protection against steganalysis and sniffing attacks, and gives a covert channel capacity which is an order of magnitude higher than traditional methods.

Adaptive-neighborhood image deblurring

A new technique is presented for the restoration of images degraded by a linear, shift-invariant blurring point-spread function in the presence of additive white Gaussian noise. The algorithm uses overlapping variable-size, variable-shape adaptive neighborhoods (ANs) to define stationary regions in the input image and obtains a spectral estimate of the noise in each AN region. This estimate is then used to obtain a spectral estimate of the original undegraded AN region, which is inverse Fourier transformed to obtain the space-domain deblurred AN region. The regions are then combined to form the final restored image. Mathematical derivation and implementation of the adaptive-neighborhood deblurring (AND) filter is discussed, and experimental results are presented with an analysis of the performance of the AND filter as compared to the fixed-neighborhood sectioned deblurring (FNSD) Wiener and power spectrum equalization filters. It is shown that using the AND algorithm for image deblurring enables the identification of relatively stationary regions. This improves the restoration process and produces results that are superior to those obtained using the FNSD method both visually and in terms of quantitative error measures.

FaceBots: robots utilizing and publishing social information in facebook

Our project aims at supporting the creation of sustainable and meaningful longer-term human-robot relationships through the creation of embodied robots with face recognition and natural language dialogue capabilities, which exploit and publish social information available on the web (Facebook). Our main underlying experimental hypothesis is that such relationships can be significantly enhanced if the human and the robot are gradually creating a pool of shared episodic memories that they can co-refer to (“shared memories”), and if they are both embedded in a social web of other humans and robots they both know and encounter (“shared friends”). In this paper, we are presenting such a robot, which as we will see achieves two significant novelties.

Magnitude Spectrum Speech Hiding

This paper presents a speech-in-speech hiding framework for the purpose of reducing the storage and transmission overhead in electronic voice mail applications, as well as for steganography applications of hiding secret speech messages for voice mail security. The technique used exploits the low-pass spectral properties of the Fourier magnitude of a host speech signal to embed another speech signal in the low-amplitude-high-frequency region of the host speech signals spectral magnitude. Experimental evaluations on real male and female voice segments show that our technique is capable of hiding one speech message inside another host speech segment to produce a stego speech segment that is indistinguishable from the original host speech, while being able to extract the hidden speech message without any degradations in quality.

Traceroute based IP channel for sending hidden short messages

The paper proposes a novel IP channel for sending hidden short messages, based mainly on the use of the “traceroute” command and the IP header Record route options. Instead of encrypting a hidden message or embedding it into a multimedia object, as in traditional multimedia steganography, we process the entire message and generate several IP packets with different types to carry the secret message. Thereby we foil an eavesdropper who is primarily applying statistical tests to detect encrypted communication channels. We show that our approach provides more protection against Steganalysis and sniffing attacks. A friendly graphical tool has been implemented to demonstrate the proposed secret IP channel.

Digital Image Steganography: An FFT Approach

This work describes a framework for image hiding that exploits spatial domain color properties of natural images combined with spectral properties of the Fourier magnitude and phase of these images. The theory is that as long as the Fourier phase of an image is maintained intact, the overall appearance of an image remains specious if the Fourier magnitude of the image is slightly modified. This hypothesis leads to a data hiding technique that promises high fidelity, double the capacity of previous methods, higher security, and robustness to tampering. Experimental results are presented throughout the paper which demonstrate the effectiveness of this novel approach.