Behzad Kamgar-Parsi

Toward Development of a Face Recognition System for Watchlist Surveillance

The interest in face recognition is moving toward real-world applications and uncontrolled sensing environments. An important application of interest is automated surveillance, where the objective is to recognize and track people who are on a watchlist. For this open world application, a large number of cameras that are increasingly being installed at many locations in shopping malls, metro systems, airports, etc., will be utilized. While a very large number of people will approach or pass by these surveillance cameras, only a small set of individuals must be recognized. That is, the system must reject every subject unless the subject happens to be on the watchlist. While humans routinely reject previously unseen faces as strangers, rejection of previously unseen faces has remained a difficult aspect of automated face recognition. In this paper, we propose an approach motivated by human perceptual ability of face recognition which can handle previously unseen faces. Our approach is based on identifying the decision region(s) in the face space which belong to the target person(s). This is done by generating two large sets of borderline images, projecting just inside and outside of the decision region. For each person on the watchlist, a dedicated classifier is trained. Results of extensive experiments support the effectiveness of our approach. In addition to extensive experiments using our algorithm and prerecorded images, we have conducted considerable live system experiments with people in realistic environments.

Underwater imaging with a moving acoustic lens

The acoustic lens is a high-resolution, forward-looking sonar for three dimensional (3-D) underwater imaging. We discuss processing the lens data for recreating and visualizing the scene. Acoustical imaging, compared to optical imaging, is sparse and low resolution. To achieve higher resolution, we obtain a denser sample by mounting the lens on a moving platform and passing over the scene. This introduces the problem of data fusion from multiple overlapping views for scene formation, which we discuss. We also discuss the improvements in object reconstruction by combining data from several passes over an object. We present algorithms for pass registration and show that this process can be done with enough accuracy to improve the image and provide greater detail about the object. The results of in-water experiments show the degree to which size and shape can be obtained under (nearly) ideal conditions.

Distribution and moments of the weighted sum of uniforms random variables, with applications in reducing monte carlo simulations

We derive analytical expressions for the distribution function and the moments of the weighted sum where Xi are independent random variables with non-identical uniform distributions, for an arbitrary number of variables N and arbitrary coefficient values ai These results are the generalizations of those for the regular sum of uniform random variables. Using the results, we examine the inadequacy of the central limit approximation for finite N We also discuss the savings in the cost of computing properties of the weighted sum using these results vs Monte Carlo simulations. We give an example of the application of the weighted sum to analyzing the effects of digitization error in computer vision.

Matching sets of 3D line segments with application to polygonal arc matching

In this paper, we consider two sets of corresponding 3D line segments of equal length. We derive a closed-form solution for the coordinate transform (rotation and translation) that gives the best match between the two sets; best in the sense of a least-squares distance measure between the sets. We use these results as the basis to construct efficient algorithms for solving other problems in computer vision. Specifically, we address the problem of matching polygonal arcs, that is, the problem of finding a match between a short arc and a piece of long arc.

Aircraft detection: a case study in using human similarity measure

After the most prominent signal in an infrared image of the sky is extracted, the question is whether the signal corresponds to an aircraft. We present a new approach that avoids metric similarity measures and the use of thresholds, and instead attempts to learn similarity measures like those used by humans. In the absence of sufficient real data, the approach allows one to specifically generate an arbitrarily large number of training exemplars projecting near the classification boundary. Once trained on such a training set, the performance of our neural network-based system is comparable to that of a human expert and far better than a network trained only on the available real data. Furthermore, the results obtained are considerably better than those obtained using an Euclidean discriminator.

Vehicle localization on gravity maps

Efforts are underway to develop the capability for small unmanned underwater vehicles to use the Earths gravitational field for autonomous navigation. A main aspect of navigation is vehicle localization on an existing gravity map. We have developed machine vision-like algorithms that match the onboard gravimeter measurements to the map values. In gravity maps there are typically a dearth of distinctive topographic features such as peaks, ridges, ravines, etc. Moreover, because the gravity field can only be measured in-place, probing for such features is infeasible as it would require extensive surveys. These factors, make the commonly used feature matching approach impractical. The localization algorithms we have developed are based on matching with contours of constant field value. These algorithms are tested on simulated data with encouraging results. Although these algorithms are developed for underwater navigation using gravity maps, they are equally applicable to other domains, for example vehicle localization on an existing terrain map.

Simultaneous fitting of several planes to point sets using neural networks

It is a simple problem to fit one line to a collection of points in the plane. But when the problem is generalized to two or more lines then the problem complexity becomes exponential in the number of points because we must decide on a partitioning of the points among the lines they are to fit. The same is true for fitting lines to points in three-dimensional space or hyperplanes to data points of high dimensions. We show that this problem despite its exponential complexity can be formulated as an optimization problem for which very good, but not necessarily optimal, solutions can be found by using an artificial neural network. Furthermore, we show that given a tolerance one can determine the number of lines (or planes) that should be fitted to a given point configuration. This problem is prototypical of a class of problems in computer vision, pattern recognition, and data fitting. For example, the method we propose can be used in reconstructing a planar world from range data or in recognizing point patterns in an image.

High‐resolution underwater acoustic imaging with lens‐based systems

In recent years, several sonars designed for high‐resolution, short‐range underwater imaging have been developed. These imaging systems use an acoustic lens to focus the incoming waves on an array of transducers. In this article we describe three prototype systems that use a line‐focus or a point‐focus lens and operate at a frequency of 300 kHz or 3 MHz. The line‐focus lens produces two‐dimensional (2D) intensity images, while the point‐focus lens produces 3D intensity images. We present sample images taken from moving and stationary platforms, and discuss the techniques used for processing the acoustic backscatter data to reconstruct and visualize the scene. The images, particularly those taken with a point‐focus lens, show a remarkable degree of detail.

NASA Mars rover: a testbed for evaluating applications of covariance intersection

The Naval Research Laboratory (NRL) has spearheaded the development and application of Covariance Intersection (CI) for a variety of decentralized data fusion problems. Such problems include distributed control, onboard sensor fusion, and dynamic map building and localization. In this paper we describe NRLs development of a CI-based navigation system for the NASA Mars rover that stresses almost all aspects of decentralized data fusion. We also describe how this project relates to NRLs augmented reality, advanced visualization, and REBOT projects.

Quantization error in hexagonal sensory configurations

The authors develop mathematical tools for estimating quantization error in hexagonal sensory configurations. These include analytic expressions for the average error and the error distribution of a function of an arbitrary number of independently quantized variables. These two quantities are essential for assessing the reliability of a given algorithm. They can also be used to compare the relative sensitivity of a particular algorithm to quantization error for hexagonal and other spatial samplings, e.g., square, and can have an impact on sensor design. Furthermore, it is shown that the ratio of hexagonal error to square error is bounded between 0.90 and 1.05. >