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conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2010

作者： van der Wal, Gooitzen S. Sarnoff Corp Princeton NJ 08540 USA

ISBN: (纸本)9780819481740

The Sarnoff Acadia (R) II is a powerful vision processing SoC (System-on-a-Chip) that was specifically developed to support advanced vision applications where system size, weight and/or power are severely constrained. This paper, targeted at vision system developers, presents a detailed technical overview of the Acadia (R) II, highlighting its architecture, processing capabilities, memory and peripheral interfaces. All major subsystems will be covered, including: video preprocessing, specialized vision processing cores for multi-spectral image fusion, multi-resolution contrast normalization, noise coring, image warping, and motion estimation. Application processing via the MPCore (R), an integrated set of four ARM (R) 11 floating point processors with associated peripheral interfaces is presented in detail. The paper will emphasize the programmability of the Acadia (R) II, while describing its ability to provide state-of-the-art real-time image processing in a small, power optimized package.*

关键词： Real-time Multiresolution Vision processing Image fusion Stabilization Embedded Processing Multicore multi-spectral

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sensor deployment in detection networks—a control theoretic approach

Sensor deployment in detection networks—a control theoretic...

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作者： Ababnah, Ahmad A. Kansas State University

学位级别：Ph.D.

For any automated surveillance operation to be successful, it is critical to have sensing resources strategically positioned to observe, interpret, react and maybe even predict events. In many practical scenarios, it is also expected that different zones within a surveillance area may have different probability of event detection (or false alarm) requirements. The operational objective in such surveillance systems is to optimize resources (number of sensors and the associated cost) and their deployment while guaranteeing a certain assured level of detection/false alarm performance. In this dissertation, we study two major challenges related to sensor deployment in distributed sensor networks (DSNs) for detection applications. The first problem we study is the sensor deployment problem in which we ask the following question: Given a finite number of sensors (with a known sensing profile), how can we deploy these sensors such that we best meet the detection and false alarm requirements in a DSN employing a specific information fusion rule? Even though sensor deployment has garnered significant interest in the past, a unified, analytical framework to model and study sensor deployment is lacking. Additionally, the algorithms proposed in literature are typically heuristic in nature and are limited to (1) simplistic DSN fusion architectures, and (2) DSNs with uniform detection/false alarm requirements. In this dissertation, we propose a novel treatment of the sensor deployment problem using concepts from optimal control theory. Specifically, the deployment problem is formulated as a linear quadratic regulator (LQR) problem which provides a rigorous and analytical framework to study the deployment problem. We develop new sensor deployment algorithms that are applicable to a wide range of DSN architectures employing different fusion rules such as (1) logical OR fusion; (2) value fusion; (3) majority decision fusion, and (4) optimal decision fusion. In all these cases, we de

关键词： Optimal control sensor networks sensor deployment Linear quadratic regulator

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An Orientation-based fusion Algorithm for Multisensor Image fusion

An Orientation-based Fusion Algorithm for Multisensor Image ...

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conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2010

作者： Zheng, Yufeng Alcorn State Univ Alcorn State MS 39096 USA

ISBN: (纸本)9780819481740

Multiscale fusion algorithms (wavelet or pyramid) can generally satisfy multisensory image fusion. However, those algorithms are not ideal to fuse visible images and infrared images whose intensities appear inverted. Therefore, a novel orientation-based fusion algorithm is proposed in this paper to address this problem. Specifically, a set of MxN Gabor wavelet transforms (GWT) are performed with two input images (I-A and I-B). At each frequency band (b = 1, 2, ..., M), the index of maximal GWT magnitude between two images is selected pixel by pixel;and then two index frequencies, H-A(b) and H-B(b), are calculated as its index accumulation along N orientations, respectively. The final HA and HB are the weighted summations through M bands, where the band weights (W-b) are given empirically. Eventually, the fused image is computed as I-F = (I-A.* H-A + I-B.* H-B)/(H-A + H-B), where '.*' denotes element-by-element product of two arrays. The orientation-based fusion algorithm can be further varied by either keeping DC (direct current) or suppressing DC in GWT. "Keeping DC" will produce a contrast-smooth image;while "suppressing DC" will result a sharpened fusion. Color fusion is achieved by replacing the red channel of a color image with the fused image, which is suitable for poorly illuminated color images. Not only are the fused images of visible and infrared images satisfied, but the fusions of other image sets are also comparable to the results of multiscale fusion algorithms. The proposed algorithm can be applied to multiple (more than two) image fusion.

关键词： Gabor wavelet transforms (GWT) multiscale image fusion multisensory image fusion

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Global evaluation of focussed Bayesian fusion

Global evaluation of focussed Bayesian fusion

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conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2010

作者： Sander, Jennifer Heizmann, Michael Goussev, Igor Beyerer, Juergen Karlsruhe Inst Technol KIT Inst Anthropomat IFA Vis & Fus Lab Adenauerring 4 D-76131 Karlsruhe Germany Fraunhofer Inst Optron Syst Technol & Image Explo D-76131 Karlsruhe Germany

ISBN: (纸本)9780819481740

Information fusion is essential for the retrieval of desired information in a sufficiently precise, complete, and robust manner. The Bayesian approach provides a powerful and mathematically funded framework for information fusion. By local Bayesian fusion approaches, the computational complexity of Bayesian fusion gets drastically reduced. This is done by a concentration of the actual fusion task on its probably most task relevant aspects. In this contribution, further research results on a special local Bayesian fusion technique called focussed Bayesian fusion are reported. At focussed Bayesian fusion, the actual Bayesian fusion task gets completely restricted to the probably most relevant parts of the range of values of the Properties of Interest. The practical usefulness of focussed Bayesian fusion is shown by the use of an example from the field of reconnaissance. Within this example, final decisions are based on local significance considerations and consistency arguments. As shown in previous publications, the absolute values of focussed probability statements represent upper bounds for their global values. Now, lower bounds which are obtained from the knowledge about the construction of the focussed Bayesian model are proven additionally. The usefulness of the resulting probability interval scheme is discussed.

关键词： information fusion Bayesian fusion local Bayesian fusion focussed Bayesian fusion Maximum Entropy principle probability interval scheme probability bounds restricted model qualitative analysis quantitative analysis

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A Modular and Mobile System for Indoor Localization

A Modular and Mobile System for Indoor Localization

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International conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Klingbeil, Lasse Romanovas, Michailas Schneider, Patrick Traechtler, Martin Manoli, Yiannos Hahn Schickard Gesell Inst Microsyst & Informat Technol Villingen Schwenningen Germany

ISBN: (纸本)9781424458646

The work presents a system for sensor data and complementary information fusion for localization in indoor environments. The system is based on modular sensor units, which can be attached to a person and contains various sensors, such as range sensors, inertial and magnetic sensors, a GPS receiver and a barometer. The measurements are processed using Bayesian Recursive Estimation algorithms and combined with available a priori knowledge such as map information or human motion models and constraints. The processing can be done locally, since all necessary data are available on the mobile unit. This system provides a platform for implementation, combination and evaluation of various localization principles and can be used for a variety of applications, such as indoor and outdoor pedestrian navigation, localization of other objects such as vehicles as well as robotics applications.

关键词： Bayesian recursive estimation GPS receiver a priori knowledge barometer barometers complementary information fusion human motion models indoor communication indoor localization inertial sensors magnetic sensors mobile communication mobile system modular sensor units sensor data fusion sensor fusion

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Fast Color-Transfer-Based Image fusion Method for Merging Infrared and Visible Images

Fast Color-Transfer-Based Image Fusion Method for Merging In...

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conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2010

作者： Li, Guangxin Xu, Shuyan Zhao, Xin Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Changchun Univ Changchun 130022 Peoples R China

ISBN: (纸本)9780819481740

We present a computationally efficient color image fusion algorithm for merging infrared and visible images. At the core of the proposed method is the color transfer technique based on the linear YCBCR space. The method directly uses the grayscale fused image and the difference signals of the input images to construct the source YCBCR components, then uses the statistical color transfer technique to form a color fused image that takes the target image's color characteristics. Two different strategies, which respectively employ the pixel averaging fusion scheme and the multiresolution fusion scheme as the grayscale image fusion solution, are proposed to fulfill different user needs. The simple strategy using the pixel averaging fusion scheme answers to a need of easy implementation and speed of use. And the complex strategy using the multiresolution fusion scheme answers to the high quality need of the fused products. In addition, we also describe some useful theories about color-transfer-based image fusion. Experimental results show that the proposed color image fusion algorithm can effectively produce a natural appearing "daytime-like" color fused image, and even using the pixel averaging fusion scheme to implement the grayscale fusion can also provide a pleasing result.

关键词： color image fusion color transfer YCBCR transformation infrared image visible image night vision

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Decentralized multi-sensor data fusion algorithm using information filter

Decentralized multi-sensor data fusion algorithm using infor...

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International conference on Measuring Technology and Mechatronics Automation, ICMTMA 2010

作者： Zhang, Chaokun Wang, Huiying Department of Computer Science and Technology Hebei Normal University Shijiazhuang Hebei 050016 China College of Science Agricultural University of Hebei Baoding Hebei 071001 China

ISBN: (纸本)9780769539621

Data fusion algorithms have a very wide range of applications in some fields. But, with the growing sensor numbers in multi-sensor target tracking systems, data fusion algorithms using conventional Kalman filter meet problems such as heavy computational burden and poor robustness. Decentralized data fusion algorithms using information filter provide a way of avoiding traditional fusion algorithms' limitations. The work described in this paper aims to develop a decentralized fusion algorithm for multi-sensor target tracking problems. The basic principle of the information filter is introduced. A decentralized data fusion algorithm using information filter is developed. This algorithm is then demonstrated on a multi-senor tracking example. © 2010 IEEE.

关键词： Target tracking

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Joint Manifolds for Data fusion

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2010年第10期19卷 2580-2594页

作者： Davenport, Mark A. Hegde, Chinmay Duarte, Marco F. Baraniuk, Richard G. Stanford Univ Dept Stat Stanford CA 94305 USA Rice Univ Dept Elect & Comp Engn Houston TX 77005 USA Princeton Univ Program Appl & Computat Math Princeton NJ 08544 USA

The emergence of low-cost sensing architectures for diverse modalities has made it possible to deploy sensor networks that capture a single event from a large number of vantage points and using multiple modalities. In many scenarios, these networks acquire large amounts of very high-dimensional data. For example, even a relatively small network of cameras can generate massive amounts of high-dimensional image and video data. One way to cope with this data deluge is to exploit low-dimensional data models. Manifold models provide a particularly powerful theoretical and algorithmic framework for capturing the structure of data governed by a small number of parameters, as is often the case in a sensor network. However, these models do not typically take into account dependencies among multiple sensors. We thus propose a new joint manifold framework for data ensembles that exploits such dependencies. We show that joint manifold structure can lead to improved performance for a variety of signal processing algorithms for applications including classification and manifold learning. Additionally, recent results concerning random projections of manifolds enable us to formulate a scalable and universal dimensionality reduction scheme that efficiently fuses the data from all sensors.

关键词： Camera networks classification data fusion manifold learning random projections sensor networks

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A Study of Bias error Estimation Method by KGBE

A Study of Bias error Estimation Method by KGBE

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IEEE International conference on Control applications Part of 2010 IEEE Multi-conference on Systems and Control

作者： Matsuzaki, Takashi Kameda, Hiroshi Uchida, Junichi Hiroshima, Fumiya Mitsubishi Electric Corporation Information Technology R&D Center Japan

ISBN: (纸本)9781424453634

Data fusion uses observations from networked multiple sensors and generates an integrated track. It achieves wide surveillance area and high accuracy, by minimizing error covariance matrix. In ideal environment, a fundamental assumption is that sensor biases are zero. However, the bias errors are not zero in real environment. As a result, the accuracy of integrated tracks deteriorate, even if the all sensors observe the same target. In this paper, we propose a new bias estimation algorithm is based on kalman filter bias estimator with grid search method. It is called the KGBE method (Kalman filter with Grid search Bias Estimator). As the result, we confirmed that the KGBE achieves higher accuracy than conventional algorithms.

关键词： Data fusion Kalman Filter Grid Search Target Tracking Bias Error Alignment Registration sensor Radar

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Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2010

Multisensor, Multisource Information Fusion: Architectures, ...

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Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2010

ISBN: (纸本)9780819481740

The proceedings contain 23 papers. The topics discussed include: capturing dynamics on multiple time scales: a multilevel fusion approach for cluttered electromagnetic data;classification of terrain using multi-resolution satellite/aerial imagery and lidar data;data fusion and classification using a hybrid intrinsic cellular inference network;fusion of ESM reports through Dempster-Shafer and Dezert-Smarandache theories;level 0-2 fusion model for ATR using fuzzy logic;multi-layered context impact modulation for enhanced focus of attention of situational awareness in persistent surveillance systems;global evaluation of focused Bayesian fusion;multiple hypothesis tracking of two persons using a network of lidar sensors with stationary and directional beams;a copula-based semi-parametric approach for footstep detection using seismic sensor networks;and integrating perception and problem solving to predict complex object behaviors.

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