Mohamed-Adel Slamani

On registration of regions of interest (ROI) in video sequences

The paper addresses the problem of registering regions of interest in two video sequences. Potential applications include blob fusion and target tracking in blurry sequences. It is assumed that the moving target is tracked successfully in one of the two sequences and is represented by a bounding box in each frame of the first sequence. The goal is to find the corresponding bounding box for each frame of the second video sequence. The registration algorithm developed is based on mutual information. To facilitate the registration process, the two cameras are assumed to be calibrated such that the geometrical transformation required to register the corresponding bounding boxes is a 2D rigid body transformation without rotation. Visual and IR video sequences are used to test the proposed approach.

Sensors for military special operations and law enforcement applications

Improvement in the capabilities of infrared, millimeter- wave, acoustic, and x-ray, sensors has provided means to detect weapons concealed beneath clothing and to provide wide-area surveillance capability in darkness and poor light for military special operations and law enforcement application. In this paper we provide an update on this technology, which we have discussed in previous papers on this subject. We present new data showing simultaneously obtained infrared and millimeter-wave images which are especially relevant because a fusion of these two sensors has been proposed as the best solution to the problem of concealed weapon detection. We conclude that the use of these various sensors has the potential for solving this problem and that progress is being made toward this goal.

Noise reduction and object enhancement in passive millimeter wave concealed weapon detection

Passive MM wave offers the advantage of penetration for concealed weapon detection. Its ability to penetrate through fog, smoke, clothing etc. makes it an attractive candidate to look for weapons concealed underneath a persons clothing. The sensor technology has advanced to a point where it is possible to generate real time video sequences. However, noise and blur are still severe problems. The work reported here investigates the problem of simultaneous noise suppression and object enhancement in passive millimeter wave video sequences. The basis for the approach is provided by undecimated wavelet transforms in the spatial dimension and motion compensated filtering in the temporal dimension. The paper presents the underlying principles of the approach as well as experimental results.

On the modeling of the sensor fusion process for concealed weapons detection

Summary form only given. Multisensor information fusion is one of the key technologies required to make significant advances in information processing and utilization in a distributed information environment. The data fusion process model proposed by the US Joint Directors of Laboratories (JDL) is accepted widely for military applications. There have been other models such as a three level model proposed by the Defense Evaluation and Research Agency (DERA) of the United Kingdom. There is an ongoing research and development program in the area of concealed weapons detection sponsored by Air Force Research Laboratory (AFRL) and National Institute of Justice (NIJ). The main objectives of this program are twofold: development of efficient sensors and signal processing technologies for the detection of civilian applications. Data collected by different sensors needs to be combined intelligently to enhance detection performance for concealed weapons. Toward this goal we have developed several novel image/signal processing algorithms. These include algorithms for image registration, image enhancement, image fusion, denoising, and object extraction. While great strides in the CWD program have been made, its relationship with the data fusion process models has not been investigated. The goal of this presentation is to examine this relationship.

Integrated platform for the enhancement of concealed weapon detection sensors

A number of technologies are being developed for concealed weapon detection (CWD), and use of the appropriate processing techniques will be very important to the success of such technologies. In this paper, signal processing procedures used t enhance the detection of weapons concealed underneath clothing are described and illustrated.

Shape-descriptor-based detection of concealed weapons in millimeter-wave data

Shape parameters based on circularity, Fourier descriptors, and invariant moments are studied for the automatic detection of weapons in millimeter-wave data. The data is collected by a 30-frames-per-second millimeter-wave (MMW) imager manufactured by Trex Enterprises for the detection of weapons concealed underneath a persons clothing. Results are illustrated through processing real MMW data.

Wavelet-based denoising and baseline correction for enhancing chemical detection

Various chemical agents have been known to provide unique Raman spectrum signatures. Practical methods for chemical detection have to deal with cluttered data where the desired agents signature is mixed with those of other chemicals in the immediate environment. It has been found that unmixing is affected by strong background signatures, such as those from the substrate, and noise. This work investigates use of wavelet transform based techniques for denoising and baseline correction for the purpose of enhancing the probability of detection of a desired agent.

New statistical procedure for the segmentation of contiguous nonhomogeneous regions based on the Ozturk algorithm

Using thresholding techniques it is possible to separate between contiguous non-homogeneous patches with different power levels. When the power levels of the patches are similar if not equal, the global histogram of the patches is unimodal and the thresholding approach becomes very difficult if not impossible. In this paper, we propose to use a statistical procedure to separate between contiguous non-homogeneous patches with similar power levels but different data statistics. The procedure separates different regions by distinguishing between their data probability distributions. The procedure is based on the Ozturk algorithm which uses the sample order statistics for the approximation of univariate distributions.

Performance of shape descriptors applied to the recognition of weapons in CWD data

Different types of shape parameters based on circularity, Fourier descriptors, and invariant moments are studied for the automatic detection of weapons in Millimeter-wave data. First, performance of the shape descriptors is evaluated on simulated objects. The best performing shape descriptors are then tested on the automatic recognition of weapons in real data.

Identification of weapons in concealed weapon detection data

A MMW sensor developed by Trex Enterprises generates image data of a person hiding a gun under his clothing at a distance of 27 feet. The goal of this research was to develop an algorithm that would automatically recognize the weapon. Tracking, segmentation, and recognition procedures were designed and successfully applied to the data.