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Advances in image registration and fusion

Advances in image registration and fusion

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conference on Multisensor, Multisource Information fusion

作者： Steer, Chris Rogers, Jeremy Smith, Moira Heather, Jamie Bernhardt, Mark Hickman, Duncan Waterfall Solut Ltd Surrey GU2 9JX England

ISBN: (纸本)9780819471659

Many image fusion systems involving passive sensors require the accurate registration of the sensor data prior to performing fusion. Since depth information is not readily available in such systems, all registration algorithms are intrinsically approximations based upon various assumption about the depth field. Although often overlooked, many registration algorithms can break down in certain situations and this may adversely affect the image fusion performance. In this paper, we discuss a framework for quantifying the accuracy and robustness of image registration algorithms which allows a more precise understanding of their shortcomings. In addition, some novel algorithms have been investigated that overcome some of these limitations. A second aspect of this work has considered the treatment of images from multiple sensors whose angular and spatial separation is large and where conventional registration algorithms break down (typically greater than a few degrees of separation). A range of novel approaches is reported which exploit the use of parallax to estimate depth information and reconstruct a geometrical model of the scene. The imagery can then be combined with this geometrical model to render a variety of useful representations of the data. These techniques (which we term Volume Registration) show great promise as a means of gathering and presenting 3D and 4D scene information for both military and civilian applications.

关键词： model generation registration fusion

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Reliable sources and uncertain decisions in multisensor systems

Reliable sources and uncertain decisions in multisensor syst...

引用

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

作者： Minor, Christian Johnson, Kevin Nova Res Inc Alexandria VA 22308 USA Naval Res Lab Washington DC 20375 USA

ISBN: (纸本)9781628416145

Conflict among information sources is a feature of fused multisource and multisensor systems. Accordingly, the subject of conflict resolution has a long history in the literature of data fusion algorithms such as that of Dempster-Shafer theory (DS). Most conflict resolution strategies focus on distributing the conflict among the elements of the frame of discernment (the set of hypotheses that describe the possible decisions for which evidence is obtained) through rescaling of the evidence. These "closed-world" strategies imply that conflict is due to the uncertainty in evidence sources stemming from their reliability. An alternative approach is the "open-world" hypothesis, which allows for the presence of "unknown" elements not included in the original frame of discernment. Here, conflict must be considered as a result of uncertainty in the frame of the discernment, rather than solely the province of evidence sources. Uncertainty in the operating environment of a fused system is likely to appear as an open-world scenario. Understanding the origin of conflict (source versus frame of discernment uncertainty) is a challenging area for research in fused systems. Determining the ratio of these uncertainties provides useful insights into the operation of fused systems and confidence in their decisions for a variety of operating environments. Results and discussion for the computation of these uncertainties are presented for several combination rules with simulated data sets.

关键词： multisensor systems data fusion sensor reliability interferants open-world sensing Dempster-Shafer theory combination rules

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Optimal Rotation Sequences for Active Perception

Optimal Rotation Sequences for Active Perception

引用

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

作者： Nakath, David Rachuy, Carsten Clemens, Joachim Schill, Kerstin Univ Bremen Cognit Neuroinformat D-28359 Bremen Germany

ISBN: (纸本)9781510601130

One major objective of autonomous systems navigating in dynamic environments is gathering information needed for self localization, decision making, and path planning. To account for this, such systems are usually equipped with multiple types of sensors. As these sensors often have a limited field of view and a fixed orientation, the task of active perception breaks down to the problem of calculating alignment sequences which maximize the information gain regarding expected measurements. Action sequences that rotate the system according to the calculated optimal patterns then have to be generated. In this paper we present an approach for calculating these sequences for an autonomous system equipped with multiple sensors. We use a particle filter for multi sensor fusion and state estimation. The planning task is modeled as a Markov decision process (MDP), where the system decides in each step, what actions to perform next. The optimal control policy, which provides the best action depending on the current estimated state, maximizes the expected cumulative reward. The latter is computed from the expected information gain of all sensors over time using value iteration. The algorithm is applied to a manifold representation of the joint space of rotation and time. We show the performance of the approach in a spacecraft navigation scenario where the information gain is changing over time, caused by the dynamic environment and the continuous movement of the spacecraft.

关键词： Markov decision process manifold representation particle filter value iteration multi-sensor fusion optimal decision making active perception information gain

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Visual localization using Bayesian decision fusion on omnidirectional sensing

Visual localization using Bayesian decision fusion on omnidi...

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conference on sensor fusion - architectures, algorithms, and applications V

作者： Paletta, L Frintrop, S Hertzberg, J Inst Digital Image Proc Joanneum Res A-8010 Graz Austria

ISBN: (纸本)0819440809

Omnidirectional visual sensors have been successfully introduced recently to robot navigation, providing improved localization performances and a more stable path following behavior. As a consequence of the sensor characteristics, occlusion of the entire panoramic visual field becomes very unlikely. The presented work exploits these characteristics providing a Bayesian framework to gain even partial evidence about a current location by applying decision fusion on the multi-directional visual context. The panoramic image is first partitioned into a fixed number of overlapping unidirectional camera views, i,e., appearance sectors. For each sector image one learns then a posterior distribution over potential locations within a predefined environment. The ambiguity in a local sector interpretation is then resolved by Bayesian reasoning over the spatial context of the current position, discriminating occlusions which do not fit to the appearance model of subsequent sector views. The results from navigation experiments in an office using a robot equipped with an omnidirectional camera demonstrate that the Bayesian reasoning allows highly occlusion tolerant localization to enable visual navigation of autonomous robots even at crowded places such as offices, factories, and urban environments.

关键词： Omnidirectional vision Bayesian decision fusion robot navigation occlusion tolerant localization

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Genetic algorithms for multi-agent fusion-system learning

Genetic algorithms for multi-agent fusion-system learning

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conference on sensor fusion - architectures, algorithms, and applications V

作者： Pigeon, L Inglada, J Solaiman, B Def Res Estab Valcartier Informat Syst Technol Sect Val Belair PQ G3J 1X5 Canada

ISBN: (纸本)0819440809

The development of efficient semi-automatic systems for heterogeneous information fusion is actually a great challenge. The efficiency can be represented as the system openness, the system evolution capabilities and the system performance. Multi-agent architecture can be designed in order to respect the first two efficiency constraints. As for third constraint, which is the performance, the key point is the interaction between each information component of the system. The context of this study is the development of a semi-automatic information fusion system for cartographic features interpretation. Combining heterogeneous sources of information such as expert rules and strategies, domain models, image processing tools, interpolation techniques, etc. completes the system development task The information modeling and fusion is performed within the evidential theory concepts. The purpose of this article is to propose a learning approach for interaction-oriented multi-agent systems. The optimization of the interaction weight is tackled with genetic algorithms technique because it provides solution for the whole set of weights at once. In this paper, the context of the multi-agent system development is presented first The need for such system and its parameters is explained. A brief review of learning techniques leads to genetic algorithms as a choice for the learning of the developed multi-agent system. After a brief introduction to genetic algorithms, these are adapted to the particularity of this study. Two approaches are designed to measure the system's fitness based on either binary or fuzzy decisions. The conclusion presents suggestions for further research in the area of multi-agent system-learning with genetic algorithms.

关键词： information fusion multi-agent genetic algorithms intelligent system learning interaction oriented-programming

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A scalable, portable, object-oriented framework for parallel multi-sensor data-fusion applications in HPC systems

A scalable, portable, object-oriented framework for parallel...

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conference on Multisensor, Multisource Information fusion

作者： Gupta, P Prasad, G Univ Cent Florida Sch Comp Sci Orlando FL 32816 USA

ISBN: (纸本)0819453579

Multi-sensor Data fusion is synergistic integration of multiple data sets. Data fusion includes processes for aligning, associating and combining data and information in estimating and predicting the state of objects, their relationships, and characterizing situations and their significance. The combination of complex data sets and the need for real-time data storage and retrieval compounds the data fusion problem. The systematic development and use of data fusion techniques are particularly critical in applications requiring massive, diverse, ambiguous, and time-critical data. Such conditions are characteristic of new emerging requirements;e.g., network-centric and information-centric warfare, low intensity conflicts such as special operations, counter narcotics, antiterrorism, information operations and CALOW (Conventional Arms, Limited Objectives Warfare), economic and political intelligence. In this paper, Aximetric presents a novel, scalable, object-oriented, metamodel framework for parallel, cluster-based data-fusion engine on High Performance Computing (HPC) Systems. The data-clustering algorithms provide a fast, scalable technique to sift through massive, complex data sets coming through multiple streams in real-time. The load-balancing algorithm provides the capability to evenly distribute the workload among processors on-the-fly and achieve real-time scalability. The proposed data-fusion engine exploits unique data-structures for fast storage, retrieval and interactive visualization of the multiple data streams.

关键词： sensor fusion HPC systems meta-modefing object-oriented simulation data fusion

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Investigation of automated feature extraction using multiple data sources

Investigation of automated feature extraction using multiple...

引用

conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2003

作者： Harvey, NR Perkins, SJ Pope, PA Theiler, J David, NA Porter, RB Los Alamos Natl Lab Space & Remote Sensing Sci Grp Los Alamos NM 87545 USA

ISBN: (纸本)0819449598

An increasing number and variety of platforms are now capable of collecting remote sensing data over a particular scene. For many applications, the information available from any individual sensor may be incomplete, inconsistent or imprecise. However, other sources may provide complementary and/or additional data. Thus, for an application such as image feature extraction or classification, it may be that fusing the mulitple data sources can lead to more consistent and reliable results. Unfortunately, with the increased complexity of the fused data, the search space of feature-extraction or classification algorithms also greatly increases. With a single data source, the determination of a suitable algorithm may be a significant challenge for an image analyst. With the fused data, the search for suitable algorithms can go far beyond the capabilities of a human in a realistic time frame, and becomes the realm of machine learning, where the computational power of modern computers can be harnessed to the task at hand. We describe experiments in which we investigate the ability of a suite of automated feature extraction tools developed at Los Alamos National Laboratory to make use. of multiple data sources for various feature extraction tasks. We compare and contrast this software's capabilities on 1) individual data sets from different data sources 2) fused data sets from multiple data sources and 3) fusion of results from multiple individual data sources.

关键词： supervised classification data fusion support vector machines feature extraction machine learning multispectral

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Implementing Real-Time Imaging Systems Using the Sarnoff Acadia II Vision Processor

Implementing Real-Time Imaging Systems Using the Sarnoff Aca...

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

作者： Berends, David van der Wal, Gooitzen Sarnoff Corp Princeton NJ 08543 USA

ISBN: (纸本)9780819481740

Vision system designers often face the daunting challenge of implementing powerful image processing capabilities in severely size, weight and power constrained systems. Multi-sensor fusion, image stabilization, image enhancement, target detection and object tracking are fundamental processing techniques required by UAVs (Unmanned Aerial Vehicles), smart cameras, weapon sights, and vehicle situational awareness systems. All of these systems also process non-vision data while communicating large amounts of information elsewhere. To meet their demanding requirements, Sarnoff developed the Acadia (R) II System-on-a- Chip, combining dedicated image processing cores, four ARM (R) 11 processors and an abundance of peripherals in a single Integrated Circuit. This paper will describe how to best use the power of the Acadia (R) II as both an all-in-one image processor and as a general purpose computer for performing other critical non-vision tasks, such as flight control and system-to-system communication.*

关键词： fusion stabilization image enhancement size weight and power constrained systems target detection object tracking system-on-a-chip situational awareness

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Centralized fusion for Fast People Detection in Dense Environment

Centralized Fusion for Fast People Detection in Dense Enviro...

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IEEE International conference on Robotics and Automation

作者： Gate, Gwennael Breheret, Amaury Nashashibi, Fawzi Mines ParisTech Robot Lab 60 Blvd St Michel F-75273 Paris France Inst Natl Rech Informat & Automat IMARA Team Mines ParisTech Robot Lab F-78153 Le Chesnay France

ISBN: (纸本)9781424427888

Human beings do not have well defined shapes neither well defined behaviors. In dense outdoor environments, they are as a consequence hard to detect and algorithms based on a single sensor tend to produce lot of wrong detections. Moreover, many applications require algorithms that work very fast on CPU limited mobile architectures while remaining able to detect, track and classify objects as people with a very high precision. We present an algorithm based on the contribution of a range finder and a vision based algorithm that addresses these three constraints: efficiency, velocity and robustness and that we believe is scalable to a large variety of applications.

关键词： People detection Target tracking Boosting Data fusion

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Multiple hypothesis tracking of two persons using a network of LIDAR sensors with stationary and directional beams

Multiple hypothesis tracking of two persons using a network ...

引用

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

作者： Wenzl, Konrad Ruser, Heinrich Kargel, Christian Bundeswehr Univ Munich Inst Measurement & Automat Div Sensor Technol & Measurement Syst D-85577 Neubiberg Munich Germany

ISBN: (纸本)9780819481740

A sensor network based on the LIDAR (LIght Detection And Ranging) principle is investigated in order to track persons inside a surveillance area and be able to identify security-relevant behavior. In order to minimize the overall sensor network complexity, power consumption and costs, we recently investigated the network topology based on a quality measure in terms of the number of nodes, measurement distance, width of the LIDAR beams and localization as well as classification performance. As a result, stationary beams with rather small opening angles of up to a few degrees are a good compromise. Since certain regions of the surveillance area are not directly assessed by the LIDAR beams, the accurate tracking of a target throughout the entire area of surveillance is challenging. We demonstrated that tracking based on a Kalman filter approach can nevertheless deliver satisfactory results for a single person inside the surveillance area. In this paper we focus on the task of reliably tracking two persons inside the surveillance area at the same time. The tracking of multiple moving targets is carried out by applying a Multiple Hypothesis Tracking filter approach. The localization and tracking performances are derived from simulations and experiments carried out with commercial laser scanners. In the near future, the rather expensive laser scanners will be replaced by appropriate LIDAR range finders.

关键词： sensor Network Surveillance Multiple Target Tracking Multiple Hypothesis Tracking Kalman Tracking

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