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Computer Vision and Pattern Recognition - Workshops, 2005. CVPR Workshops. IEEE Computer Society Conference on

Date 21-23 Sept. 2005

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Displaying Results 1 - 25 of 211
  • Workshop preface

    Page(s): i
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  • Object Tracking and Classification in and Beyond the Visible Spectrum - Table of contents

    Page(s): v
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  • Aims & Scope

    Page(s): ix
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  • Topics of Interest

    Page(s): x
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  • Welcome Message from the Chairs

    Page(s): xii
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  • Committee

    Page(s): xiii
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  • Sponsors

    Page(s): xiv
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  • Call for Papers

    Page(s): xv
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  • Open Call for Benchmark/Test Dataset

    Page(s): xvi
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  • Benchmark Dataset Collection

    Page(s): xvii
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  • Keynote Talk

    Page(s): xviii
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    Provides an abstract of the keynote presentation and a brief professional biography of the presenter. The complete presentation was not made available for publication as part of the conference proceedings. View full abstract»

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  • Acknowledgements

    Page(s): xix
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  • Call for Papers

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  • Model-based validation approaches and matching techniques for automotive vision based pedestrian detection

    Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (430 KB) |  | HTML iconHTML  

    Pedestrian detection is a challenging vision task, especially applied to the automotive field where the background changes as the vehicle moves. This paper presents an extensive study upon human body models and the techniques suitable for being used in a pedestrian detection system. Several different approaches for building model sets, such as synthetic, real, and dynamic sets are presented and discussed. Comparative results are reported with reference to a case study of a real system. Preliminary results of current research status are shown together with further developments. View full abstract»

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  • A Combinational Approach to the Fusion, De-noising and Enhancement of Dual-Energy X-Ray Luggage Images

    Page(s): 2
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    X-ray luggage inspection systems play an important role in ensuring air travelers’ security. However, the false alarm rate of commercial systems can be as high as 20% due to less than perfect image processing algorithms. In an effort to reduce the false alarm rate, this paper proposes a combinational scheme to fuse, de-noise and enhance dual-energy X-ray images for better object classification and threat detection. The fusion step is based on the wavelet transform. Fused images generally reveal more detail information; however, background noise often gets amplified during the fusion process. This paper applies a backgroundsubtraction- based noise reduction technique which is very efficient in removing background noise from fused X-ray images. The de-noised image is then processed using a new enhancement technique to reconstruct the final image. The final image not only contains complementary information from both source images, but is also background-noise-free and contrastenhanced, therefore easier to segment automatically or be interpreted by screeners, thus reducing the false alarm rate in X-ray luggage inspection. View full abstract»

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  • Multiperspective Thermal IR and Video Arrays for 3D Body Tracking and Driver Activity Analysis

    Page(s): 3
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1028 KB) |  | HTML iconHTML  

    This paper presents a multi-perspective (i.e., four camera views) multi-modal (i.e., thermal infrared and color) video based system for robust and real-time 3D tracking of important body parts.The multi-perspective characteristics of the system provides 3Dtrajectory of the body parts, while the multi-modal characteristics of the system provides robustness and reliability of feature detection and tracking. The application context for this research is that of intelligent vehicles and driver assistance systems. Experimental results demonstrate effectiveness of the proposed system. View full abstract»

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  • Tracking Humans using Multi-modal Fusion

    Page(s): 4
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    Human motion detection plays an important role in automated surveillance systems. However, it is challenging to detect non-rigid moving objects (e.g. human) robustly in a cluttered environment. In this paper, we compare two approaches for detecting walking humans using multi-modal measurements- video and audio sequences. The first approach is based on the Time-Delay Neural Network (TDNN), which fuses the audio and visual data at the feature level to detect the walking human. The second approach employs the Bayesian Network (BN) for jointly modeling the video and audio signals. Parameter estimation of the graphical models is executed using the Expectation-Maximization (EM) algorithm. And the location of the target is tracked by the Bayes inference. Experiments are performed in several indoor and outdoor scenarios: in the lab, more than one person walking, occlusion by bushes etc. The comparison of performance and efficiency of the two approaches are also presented. View full abstract»

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  • Improved Likelihood Function in Particle-based IR Eye Tracking

    Page(s): 5
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    In this paper we propose a log likelihood-ratio function of foreground and background models used in a particle filter to track the eye region in dark-bright pupil image sequences. This model fuses information from both dark and bright pupil images and their difference image into one model. Our enhanced tracker overcomes the issues of prior selection of static thresholds during the detection of feature observations in the bright-dark difference images. The auto-initialization process is performed using cascaded classifier trained using adaboost and adapted to IR eye images. Experiments show good performance in challenging sequences with test subjects showing large head movements and under significant light conditions. View full abstract»

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  • Kernel Matched Signal Detectors for Hyperspectral Target Detection

    Page(s): 6
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    In this paper, we compare several detection algorithms that are based on spectral matched (subspace) filters. Nonlinear (kernel) versions of these spectral matched (subspace) detectors are also discussed and their performance is compared with the linear versions. These kernel-based detectors exploit the nonlinear correlations between the spectral bands that are ignored by the conventional detectors. Several well-known matched detectors, such as matched subspace detector, orthogonal subspace detector, spectral matched filter and adaptive subspace detector (adaptive cosine estimator) are extended to their corresponding kernel versions by using the idea of kernel-based learning theory. In kernel-based detection algorithms the data is implicitly mapped into a high dimensional kernel feature space by a nonlinear mapping which is associated with a kernel function. The detection algorithm is then derived in the feature space which is kernelized in terms of the kernel functions in order to avoid explicit computation in the high dimensional feature space. Experimental results based on simulated toyexamples and real hyperspectral imagery show that the kernel versions of these detectors outperform the conventional linear detectors. View full abstract»

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  • Spaceborne Traffic Monitoring with Dual Channel Synthetic Aperture Radar Theory and Experiments

    Page(s): 7
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (486 KB) |  | HTML iconHTML  

    This paper revises the theoretical background for upcoming dual-channel Radar satellite missions to monitor traffic from space. As it is well-known, an object moving with a velocity deviating from the assumptions incorporated in the focusing process will generally appear both displaced and blurred in the azimuth direction. To study the impact of these (and related) distortions in focused SAR images, the analytic relations between an arbitrarily moving point scatterer and its conjugate in the SAR image have been reviewed and adapted to dual-channel satellite specifications. To be able to monitor traffic under these boundary conditions in real-life situations, a specific detection scheme is proposed. This scheme integrates complementary detection and velocity estimation algorithms with knowledge derived from external sources as, e.g., road databases. View full abstract»

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  • Comparative Image Fusion Analysais

    Page(s): 8
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    Image fusion is and will be an integral part of many existing and future surveillance systems. However, little or no systematic attempt has been made up to now on studying the relative merits of various fusion techniques and their effectiveness on real multi-sensor imagery. In this paper we provide a method for evaluating the performance of image fusion algorithms. We define a set of measures of effectiveness for comparative performance analysis and then use them on the output of a number of fusion algorithms that have been applied to a set of real passive infrared (IR) and visible band imagery. View full abstract»

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  • Performance Evaluation of Face Recognition using Visual and Thermal Imagery with Advanced Correlation Filters

    Page(s): 9
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (322 KB) |  | HTML iconHTML  

    This paper presents the face recognition performance evaluation using visual and thermal infrared (IR) face images with correlation filter methods. New correlation filter designs have shown to be distortion invariant and the advantages of using thermal IR images are due to their invariance to visible illumination variations. A combined use of thermal IR image data and correlation filters makes a viable means for improving the performance of face recognition techniques, especially beyond visual spectrum. Subset of Equinox databases are used for the performance evaluation. Among various advanced correlation filters, minimum average correlation energy (MACE) filters and optimum trade-off synthetic discriminant function (OTSDF) filters are used in our experiments. We show that correlation filters perform well when the size of face is of significantly low resolution (e.g. 20x20 pixels). Performing robust face recognition using low resolution images has many applications including performing human identification at a distance (HID). The eyeglass detection and removal in thermal images are processed to increase the performance in thermal face recognition. We show that we can outperform commercial face recognition algorithms such as FaceIt® based on Local Feature Analysis (LFA). View full abstract»

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  • Integrating LDV Audio and IR Video for Remote Multimodal Surveillance

    Page(s): 10
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (377 KB) |  | HTML iconHTML  

    This paper describes a multimodal surveillance system for human signature detection. The system consists of three types of sensors: infrared (IR) cameras, pan/tilt/zoom (PTZ) color cameras and laser Doppler vibrometers (LDVs). The LDV is explored as a new non-contact remote voice detector. We have found that voice energy vibrates most objects and the vibrations can be detected by an LDV. Since signals captured by the LDV are very noisy, we have designed algorithms with Gaussian bandpass filtering and adaptive volume scaling to enhance the LDV voice signals. The enhanced voice signals are intelligible from targets without retro-reflective finishes at short or medium distances (<100m). By using retroreflective tapes, the distance could be as far as 300 meters. However, the manual operation to search and focus the laser beam on a target with both vibration and reflection is very difficult at medium and large distances. Therefore, infrared (IR) imaging for target selection and localization is also discussed. Future work remains in automatic LDV targeting and intelligent refocusing for long range LDV listening. View full abstract»

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  • Fusion-Based Background-Subtraction using Contour Saliency

    Page(s): 11
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (377 KB) |  | HTML iconHTML  

    We present a new contour-based background-subtraction technique using thermal and visible imagery for persistent object detection in urban settings. Statistical backgroundsubtraction in the thermal domain is used to identify the initial regions-of-interest. Color and intensity information are used within these areas to obtain the corresponding regionsof- interest in the visible domain. Within each region, input and background gradient information are combined to form a Contour Saliency Map. The binary contour fragments, obtained from corresponding Contour Saliency Maps, are then combined. An A path-constrained search along watershed boundaries is used to complete and close any broken contour segments. Lastly, the contour image is flood- filled to produce silhouettes. Results of our approach are presented and compared against manually segmented data. View full abstract»

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  • Background Estimation under Rapid Gain Change in Thermal Imagery

    Page(s): 12
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    We consider detection of moving ground vehicles in airborne sequences recorded by a thermal sensor with automatic gain control, using an approach that integrates dense optic flow over time to maintain a model of background appearance and a foreground occlusion layer mask. However, the automatic gain control of the thermal sensor introduces rapid changes in intensity that makes this difficult. In this paper we show that an intensity-clipped affine model of sensor gain is suffi- cient to describe the behavior of our thermal sensor. We develop a method for gain estimation and compensation that uses sparse flow of corner features to compute the affine background scene motion that brings pairs of frames into alignment prior to estimating change in pixel brightness. Dense optic flow and background appearance modeling is then performed on these motioncompensated and brightness-compensated frames. Experimental results demonstrate that the resulting algorithm can segment ground vehicles from thermal airborne video while building a mosaic of the background layer, despite the presence of rapid gain changes. View full abstract»

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