Showing papers by "Mongi A. Abidi published in 2004"

Fusion of Visual and Thermal Signatures with Eyeglass Removal for Robust Face Recognition

Abstract: This paper describes a fusion of visual and thermal infrared (IR) images for robust face recognition. Two types of fusion methods are discussed: data fusion and decision fusion. Data fusion produces an illumination-invariant face image by adaptively integrating registered visual and thermal face images. Decision fusion combines matching scores of individual face recognition modules. In the data fusion process, eyeglasses, which block thermal energy, are detected from thermal images and replaced with an eye template. Three fusion-based face recognition techniques are implemented and tested: Data fusion of visual and thermal images (Df), Decision fusion with highest matching score (Fh), and Decision fusion with average matching score (Fa). A commercial face recognition software FaceIt® is used as an individual recognition module. Comparison results show that fusion-based face recognition techniques outperformed individual visual and thermal face recognizers under illumination variations and facial expressions.

Fusion of visual, thermal, and range as a solution to illumination and pose restrictions in face recognition

Abstract: This paper summarizes the various components of face recognition research conducted at the IRIS Lab. First, fusion of visual and thermal infrared (IR) images for robust face recognition is discussed. Two techniques are implemented: data fusion and decision fusion. With the knowledge that eyeglasses block the emission of thermal energy, an algorithm is designed to detect and replace eyeglasses with an eye template in thermal images. A commercial face recognition software (FaceIt/spl reg/) is used in the evaluation of the various fusion algorithms. Comparison results show that fusion-based face recognition outperforms individual visual or thermal face recognizers under illumination variations and facial expressions. Efforts in the 3D arena are also described. Results of high resolution stereo-based 3D reconstruction of faces are shown and analyzed, in a first approach, then in a second approach, a warping technique is applied to overlay color and thermal textures on 3D mannequin head models, obtained using a laser range scanner.

Improving the detection of low-density weapons in x-ray luggage scans using image enhancement and novel scene-decluttering techniques

Abstract: Very few image processing applications have dealt with x-ray luggage scenes in the past. Concealed threats in general, and low-density items in particular, pose a major challenge to airport screeners. A simple enhancement method for data decluttering is introduced. Initially, the method is applied using manually selected thresholds to progressively generate decluttered slices. Further au- tomation of the algorithm, using a novel metric based on the Radon transform, is conducted to determine the optimum number and val- ues of thresholds and to generate a single optimum slice for screener interpretation. A comparison of the newly developed metric to other known metrics demonstrates the merits of the new ap- proach. On-site quantitative and qualitative evaluations of the vari- ous decluttered images by airport screeners further establishes that the single slice from the image hashing algorithm outperforms tradi- tional enhancement techniques with a noted increase of 58% in low- density threat detection rates. © 2004 SPIE and IS&T.

Research article SAFER under vehicle inspection through video mosaic building

Abstract: The current threats to US security, both military and civilian, have led to an increased interest in the development of technologies to safeguard national facilities such as military bases, federal buildings, nuclear power plants, and national laboratories. As a result, the imaging, robotics, and intelligent systems (IRIS) laboratory at the University of Tennessee has established a research consortium, known as security automation and future electromotive robotics (SAFER), to develop, test, and deploy sensing and imaging systems. In this paper, we describe efforts made to build multi-perspective mosaics of infrared and color video data for the purpose of under vehicle inspection. It is desired to create a large, high-resolution mosaic that may be used to quickly visualize the entire scene shot by a camera making a single pass underneath the vehicle. Several constraints are placed on the video data in order to facilitate the assumption that the entire scene in the sequence exists on a single plane. Therefore, a single mosaic is used to represent a single video sequence.

SAFER under vehicle inspection through video mosaic building

Abstract: The current threats to US security, both military and civilian, have led to an increased interest in the development of technologies to safeguard national facilities such as military bases, federal buildings, nuclear power plants, and national laboratories. As a result, the imaging, robotics, and intelligent systems (IRIS) laboratory at the University of Tennessee has established a research consortium, known as security automation and future electromotive robotics (SAFER), to develop, test, and deploy sensing and imaging systems. In this paper, we describe efforts made to build multi‐perspective mosaics of infrared and color video data for the purpose of under vehicle inspection. It is desired to create a large, high‐resolution mosaic that may be used to quickly visualize the entire scene shot by a camera making a single pass underneath the vehicle. Several constraints are placed on the video data in order to facilitate the assumption that the entire scene in the sequence exists on a single plane. Therefore, a single mosaic is used to represent a single video sequence.

SAFER Vehicle Inspection: A Multimodal Robotic Sensing Platform

Abstract: The current threats to U.S. security both military and civilian have led to an increased interest in the development of technologies to safeguard national facilities such as military bases, federal buildings, nuclear power plants, and national laboratories. As a result, the Imaging, Robotics, and Intelligent Systems (IRIS) Laboratory at The University of Tennessee (UT) has established a research consortium, known as SAFER (Security Automation and Future Electromotive Robotics), to develop, test, and deploy sensing and imaging systems for unmanned ground vehicles (UGV). The targeted missions for these UGV systems include—but are not limited to—under vehicle threat assessment, stand-off check-point inspections, scout surveillance, intruder detection, obstaclebreach situations, and render-safe scenarios. This paper presents a general overview of the SAFER project. Beyond this general overview, we further focus on a specific problem where we collect 3D range scans of under vehicle carriages. These scans require appropriate segmentation and representation algorithms to facilitate the vehicle inspection process. We discuss the theory for these algorithms and present results from applying them to actual vehicle scans.

Stereo Matching with Energy Minimizing Snake Grid for 3D Face Modeling

Abstract: An energy minimizing snake algorithm that runs over a grid is designed and used to reconstruct high resolution 3D human faces from pairs of stereo images. The accuracy of reconstructed 3D data from stereo depends highly on how well stereo correspondences are established during the feature matching step. Establishing stereo correspondences on human faces is often ill posed and hard to achieve because of uniform texture, slow changes in depth, occlusion, and lack of gradient. We designed an energy minimizing algorithm that accurately finds correspondences on face images despite the aforementioned characteristics. The algorithm helps establish stereo correspondences unambiguously by applying a coarse-to-fine energy minimizing snake in grid format and yields a high resolution reconstruction at nearly every point of the image. Initially, the grid is stabilized using matches at a few selected high confidence edge points. The grid then gradually and consistently spreads over the low gradient regions of the image to reveal the accurate depths of object points. The grid applies its internal energy to approximate mismatches in occluded and noisy regions and to maintain smoothness of the reconstructed surfaces. The grid works in such a way that with every increment in reconstruction resolution, less time is required to establish correspondences. The snake used the curvature of the grid and gradient of image regions to automatically select its energy parameters and approximate the unmatched points using matched points from previous iterations, which also accelerates the overall matching process. The algorithm has been applied for the reconstruction of 3D human faces, and experimental results demonstrate the effectiveness and accuracy of the reconstruction.

Superquadric representation of automotive parts applying part decomposition

Abstract: Superquadrics are able to represent a large variety of objects with only a few parameters and a single equation. We present a superquadric representation strategy for automotive parts composed of 3-D triangle meshes. Our strategy consists of two ma- jor steps of part decomposition and superquadric fitting. The origi- nalities of this approach include the following two features. First, our approach can represent multipart objects with superquadrics suc- cessfully by applying part decomposition. Second, superquadrics re- covered from our approach have the highest confidence and accu- racy due to the 3-D watertight surfaces utilized. A novel, generic 3-D part decomposition algorithm based on curvature analysis is also proposed. Experimental results demonstrate that the proposed part decomposition algorithm is able to segment multipart objects into meaningful single parts efficiently. The proposed superquadric rep- resentation strategy can then represent each individual part of the original objects with a superquadric model successfully. © 2004

Multiresolution Analysis for Irregular Meshes

Abstract: The concept of multiresolution analysis applied to irregular meshes has become more and more important. Previous contributions proposed a variety of methods using simplification and/or subdivision algorithms to build a mesh pyramid. In this paper, we propose a multiresolution analysis framework for irregular meshes with attributes. Our framework is based on simplification and subdivision algorithms to build a mesh pyramid. We introduce a surface relaxation operator that allows to build a non-uniform subdivision for a low computational cost. Furthermore, we generalize the relaxation operator to attributes such as color, texture, temperature, etc. The attribute analysis gives more information on the analysed models allowing more complete processing. We show the efficiency of our framework through a number of applications including filtering, denoising and adaptive simplification.

Integration of color and shape for detecting and tracking security breaches in airports

Abstract: We present an integrated surveillance system for detecting and tracking security breaches in airports. The first part of the system uses an overhead camera to detect security breaches in exit lanes using a motion-based segmentation algorithm, and the second part of the system tracks the detected target with a pan/tilt/zoom (PTZ) camera using color and shape information. If an object comes into the exit lane from the wrong direction, the first subsystem detects a security breach and sends information to the second subsystem located inside the airport. The second subsystem then extracts the target that caused the security breach using motion-based segmentation and collects features for tracking. Color is initially used as the only feature and shape incorporated in a second step for a more robust tracking.

Developing detailed a priori 3D models of large environments to aid in robotic navigation tasks

Abstract: In order to effectively navigate any environment, a robotic vehicle needs to understand the terrain and obstacles native to that environment. Knowledge of its own location and orientation, and knowledge of the region of operation, can greatly improve the robot’s performance. To this end, we have developed a mobile system for the fast digitization of large-scale environments to develop the a priori information needed for prediction and optimization of the robot’s performance. The system collects ground-level video and laser range information, fusing them together to develop accurate 3D models of the target environment. In addition, the system carries a differential Global Positioning System (GPS) as well as an Inertial Navigation System (INS) for determining the position and orientation of the various scanners as they acquire data. Issues involved in the fusion of these various data modalities include: Integration of the position and orientation (pose) sensors’ data at varying sampling rates and availability; Selection of “best” geometry in overlapping data cases; Efficient representation of large 3D datasets for real-time processing techniques. Once the models have been created, this data can be used to provide a priori information about negative obstacles, obstructed fields of view, navigation constraints, and focused feature detection.

Gaussian energy functions for registration without correspondences

Abstract: A new criterion based on Gaussian fields is introduced and applied to the task of automatic rigid registration of point-sets. The method defines a simple energy function, which is always differentiable and convex in a large neighborhood of the alignment parameters; allowing for the use of powerful standard optimization techniques. We show that the size of the region of convergence can be extended so that no close initialization is needed, thus overcoming local convergence problems of iterative closest point algorithms. Furthermore, the Gaussian energy function can be evaluated with the linear complexity using the fast Gauss transform, which permits efficient implementation of the registration algorithm. Analysis through several experimental results on real world datasets shows the practicality and points out the limits of the approach.

Optical flow-based tracking of deformable objects using a non-prior training active feature model

Abstract: This paper presents a feature point tracking algorithm using optical flow under the non-prior training active feature model (NPT-AFM) framework. The proposed algorithm mainly focuses on analysis of deformable objects, and provides real-time, robust tracking. The proposed object tracking procedure can be divided into two steps: (i) optical flow-based tracking of feature points and (ii) NPT-AFM for robust tracking. In order to handle occlusion problems in object tracking, feature points inside an object are estimated instead of its shape boundary of the conventional active contour model (ACM) or active shape model (ASM), and are updated as an element of the training set for the AFM. The proposed NPT-AFM framework enables the tracking of occluded objects in complicated background. Experimental results show that the proposed NPT-AFM-based algorithm can track deformable objects in real-time.

Video Surveillance of High Security Facilities

Abstract: An integrated system for automatic detection, handover and tracking of an intruder using one fixed camera and one Pan/Tilt/Zoom(PTZ) camera is presented. This system includes automatic detection using an overhead static camera of an intruder who walks into a crowded secure area, intruder handover from the static to the nearest PTZ camera, and tracking using the PTZ camera. The intruder is detected by applying optical flow algorithms to video from the overhead camera. A PTZ camera extracts the intruder detected by the overhead camera and builds a color histogram model of the target. A novel region segmentation based on the color histogram and the algorithm for tracking are presented with real experimental results.

Multiperspective mosaics for under-vehicle inspection

Abstract: In this paper, we describe efforts made to implement multiperspective mosaicking of infrared and color video data for the purpose of under vehicle inspection. It is desired to create a large, high-resolution mosaic that may be used to quickly visualize the entire scene shot by a camera making a single pass underneath the vehicle. Several constraints are placed on the video data in order to facilitate the assumption that the entire scene in the sequence exists on a single plane. Therefore, a single mosaic is used to represent a single video sequence. Phase correlation is used to perform motion analysis in this case.

Automatic Detection and Tracking of Security Breaches in Airports

Abstract: A surveillance system that detects and tracks security breaches in airports is presented The system consists of two subsystems with one overhead static and one Pan/Tilt/Zoom (PTZ) camera to first acquire and then follow an intruder who illegally walks into a crowded secure area of an airport The overhead camera detects the intruder using a motionbased segmentation and an optical flow algorithm Intruder handover from the overhead camera to the PTZ camera is then performed A novel approach for intruder handover and feature extraction using color is presented for continuous tracking with the PTZ camera when the intruder moves out of the view of the overhead camera We also use a mean shift filter with a newly designed non-rectangular search window which will be automatically updated to accurately localize the target Real experimental results from a local airport are given and discussed

Real-time digital autofocusing using a priori estimated set of PSFs

Abstract: This paper proposes a real-time digital auto-focusing algorithm using a priori estimated set of point spread functions (PSFs). A priori set of PSFs are estimated by establishing the relation between two-dimensional PSF and onedimensional step response whose elements are samples of profile of degraded step edge. From the priori estimated set, the proposed auto-focusing algorithm can select the optimal PSF by the focusing criterion based on the frequency domain analysis. We then use the constrained least square (CLS) filter to obtain the in-focused image with the estimated optimal PSF. The proposed algorithm can be implemented in real-time because the set of PSFs are already estimated and the filtering is performed in the frequency domain.

Multi-sensor Registration and Integration for Inspection and Navigation

Abstract: This paper describes a unified framework for addressing the problem of multi-sensor registration and integration in the context of inspection by autonomous platforms. The task is approached from an optimization angle by designing an effective general- purpose registration criterion that can be employed for both aligning 3D and 2D datasets. The resulting pipeline reconstructs full 3D representations of the scenes and objects of interest, including multi-spectral texture overlay. The obtained information rich representation is very useful in automating decision and reducing the cognitive load on human operators

Towards computer-aided reverse engineering of heavy vehicle parts using laser range imaging techniques

Abstract: We present an integrated system to automatically generate computer-aided design (CAD) models of existing vehicle parts using laser range imaging techniques. The proposed system integrates data acquisition, model reconstruction, and post processing to generate a set of models from real-world automotive parts. This range image-based, computer-aided reverse engineering (CARE) system has potential for faster model reconstruction over traditional reverse engineering technologies. As part of this system, we also propose a novel crease detection algorithm, which segments the surfaces of reconstructed models along smoothness discontinuities. We present results for both the CARE system and the proposed crease detection algorithm for a set of automotive parts.

Fast Digitization of Large-Scale Hazardous Facilities

Abstract: Robotics technology has become a major interest for groups that deal with hazardous environments Of special interest is the conversion from telepresence robotics to more automated approaches In order to facilitate this conversion, we have developed a system for the fast acquisition of large-scale environments to develop the a priori information needed for prediction and performance optimization This system collects ground-level range scans and combines them with high-resolution digital imagery and positioning and orientation information to provide detailed 3D models of the target environment The fusion of these data provides models suitable for a variety of robotics-oriented tasks, such as visualization, sensor placement, and robotic path planning

Using a Non-prior Training Active Feature Model

Abstract: This paper presents a feature point tracking algorithm using optical flow under the non-prior training active feature model (NPT- AFM) framework. The proposed algorithm mainly focuses on analysis of deformable objects, and provides real-time, robust tracking. The pro- posed object tracking procedure can be divided into two steps: (i) opti- cal flow-based tracking of feature points and (ii) NPT-AFM for robust tracking. In order to handle occlusion problems in object tracking, feature points inside an object are estimated instead of its shape boundary of the conventional active contour model (ACM) or active shape model (ASM), and are updated as an element of the training set for the AFM. The pro- posed NPT-AFM framework enables the tracking of occluded objects in complicated background. Experimental results show that the proposed NPT-AFM-based algorithm can track deformable objects in real-time.

Object-based image restoration for multilayer autofocusing

Abstract: This paper proposes a fully digital auto-focusing algorithm for restoring the image with differently out-of-focused objects, which can restore background as well as all objects. In this paper, we assume that out-of-focus blur is isotropic such as circle of confusion (COC) or two-dimensional Gaussian blur. Therefore, the proposed algorithm can segment and estimate the point spread function (PSF) by using the size of ramp in the one-dimensional step response. The proposed algorithm can be developed by object-based image segmentation and restoration algorithm. Experimental results show that the proposed object-based image restoration algorithm can efficiently remove the space-variant out of focus blur from the image with multiple blurred objects.

Direct search of time delay in beamforming applications

Abstract: In this research work we explore the application of heuristic direct search methods in the determination of the time delay between two discrete time series. Since each time series can be thought as an n-dimensional vector, the delay determination can be expressed in terms of the amount of delay which minimizes the Euclidean distance between these vectors. The paper starts with a brief review of the basic concepts of beamforming in the time domain. Next, we introduce our formulation of beamforming problem as an optimization problem in terms of the Euclidean distance. To solve the optimization problem, genetic search and pattern search algorithms are presented. We conclude with the results of preliminary experiments performed using a two microphone array (microphone pair). Our experiments were performed under reverberant conditions.

Registering multisensor datasets from a robotic inspection platform

Abstract: In this paper we present a new method for the registration of multiple sensors applied to a mobile robotic inspection platform. Our main technical challenge is automating the integration process for various multimodal inputs, such as depth maps, and multi-spectral images. This task is approached through a unified framework based on a new registration criterion that can be employed for both 3D and 2D datasets. The system embedding this technology reconstructs 3D models of scenes and objects that are inspected by an au tonomous platform in high security areas. The models are processed and rendered with corresponding multi-spectral textures, which greatly enhances both human and machine identification of threat objects.

Rapid and Brief Communication Gaussian %elds: a new criterion for 3D rigid registration

Abstract: This paper introduces a new and simple criterion for rigid registration based on Gaussian %elds The criterion is always di2erentiable and convex in a large neighborhood of the alignment parameters; allowing for the use of well-proven optimization techniques Using this method we can extend the size of the region of convergence so that no close initialization is needed, thus overcoming local convergence problems of Iterative Closest Point algorithms Furthermore, the Gaussian energy function can be evaluated with linear complexity using the fast Gauss transform, which permits e9cient implementation of the registration algorithm Experimental analysis on real-world data sets shows the usefulness and points the limits of the approach ? 2004 Pattern Recognition Society Published by Elsevier Ltd All rights reserved