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Sensor resource management (or process refinement) is a element of any information fusion system. Common level 4 sensor management (SM) inter-relations to level 1 target tracking and identification have been developed in the literature. During Fusion06, a panel discussion was held to explore the challenges and issues pertaining to the interaction between SM and situation and threat assessment. This...
We propose a hybrid method of seeded region growing and region hue-area information fusion for object segmentation under patterned background. At first, image is segmented into many small regions according to hue homogeneity by seeded region growing algorithm, then background texture mode is discovered by the regions' hue-area information fusion, finally, the background texture is removed according...
In a net-centric world, systems will be required to fuse data from geographically dispersed, heterogeneous information sources operating asynchronously, to produce up-to-date, mission-relevant knowledge to inform commanders. Realizing this vision requires overcoming a number of technical challenges. Among these is the need for semantic interoperability among systems with different internal data models...
In this paper, several classification methods are presented and a fusion structure is included to improve the final classification performance. The definition of "layer" and the method to create it are then introduced. Based on "layer", a multiple level change detection algorithm is proposed, which gives the details of the changes in each region and is demonstrated to be an easy,...
It is a critical consideration to collect and fuse sensed information in an energy efficient manner for obtaining a long lifetime of the sensor network. Based on our findings that the conventional methods of direct transmission, shortest path routing, and Dempster-Shafer tool may not be optimal for data fusion of sensor networks, we propose LEECF (low-energy event centric fusion), a event-centric-based...
The hybrid SIR joint particle filter has been developed as an effective approximation of the exact Bayesian filter for maintaining tracks of multiple maneuvering targets from unassociated measurements. This paper further develops this approach for the situation of limited sensor resolution and two maneuvering targets. For this problem the exact Bayesian filter recursion is characterized, and is subsequently...
In safety automotive applications the system must me capable of early recognizing the maneuvers performed by the driver and the intention associated with them in order to take preventive measures or trigger warning alarms. This is done by the situation refinement level in the fusion system that processes the data provided by the on-board sensors. By recognizing relationships between entities of the...
Naive-Bayes and k-NN classifiers are two machine learning approaches for text classification. Rocchio is the classic method for text classification in information retrieval. Based on these three approaches and using classifier fusion methods, we propose a novel approach in text classification. Our approach is a supervised method, meaning that the list of categories should be defined and a set of training...
This paper introduces a common method, based on the cross-entropy method, in order to solve a variety of search problems when search resources are scarce compared to the size of the space of search. In particular, we solve: detection and information search problems, a detection search game, and a two-targets detection search problem. Our approach is built of two steps: first, decompose a problem in...
The simultaneous exploitation of multi- sensor imagery for geospatial intelligence applications is a challenging problem, and Image Analysts would benefit from tools that introduce automation and fusion into the exploitation process in a suitable manner. These tools should take advantage of the human cognitive ability to fuse and assimilate multiple sources and types of information; image fusion tools...
Most predictive modeling in information fusion is performed using ensembles. When designing ensembles, the prevailing opinion is that base classifier diversity is vital for how well the ensemble will generalize to new observations. Unfortunately, the key term diversity is not uniquely defined, leading to several diversity measures and many methods for diversity creation. In addition, no specific diversity...
Vision-guided autonomous platforms require inertial stabilization of the imaging sensor. This is typically achieved by using a gimbaled system with inertial rate sensors, such as gyros. Using low-cost gyros requires estimation of their error parameters, such as bias and scale-factor. This paper presents a motion model-based method for robust estimation of these parameters via fusing the inertial measurements...
The main goal of this paper is to investigate the relationship between two theories widely applied to explain the success of classifiers fusion: diversity measures and margin theory. In order to achieve this, we realized an empirical study which evaluates some classical measures related to these two theories with respect to ensembles accuracy. In particular, this study revealed valuable insights on...
The motion imagery community will benefit from the availability of standard measures for assessing image interpretability. The national imagery interpretability rating scale (NIIRS) has served as a community standard for still imagery, but no comparable scale exists for motion imagery. We conducted a series of user evaluations to understand and quantify the effects of critical factors affecting the...
This paper details and deepens a previous work where the Interpreted Systems semantics was proposed as a general framework for situation analysis (SA). This framework is particularly efficient for representing and reasoning about knowledge and uncertainty when performing situation analysis tasks. Our approach of SA is to base our analysis on the production of state transition systems consisting in...
The overall goal of the research presented in this paper is to design an intelligent system to aid geologists in processing complex rock characteristics for interpreting eruption patterns, and thereby to aid eruption forecasting for volcanic chains and fields. The objective of this paper is to describe application of data fusion techniques to designing an intelligent system. The processing of geological...
The fusion of data from different sensorial sources is today the most promising method to increase robustness and reliability of environmental perception. The paper presents an approach for using fuzzy operators for the hierarchical fusion of processing results in a multi sensor data processing system for the detection of vehicles in road environments. Tracking and fusion of intermediate results is...
A reasonable starting place for developing decision fusion rules of families of classification systems is using the logical AND and OR rules. These two rules, along with the unary rule NOT, can lead to a Boolean algebra when a number of properties are shown to exist. This paper examines how these rules for classification system families comprise a Boolean algebra of systems. This Boolean algebra of...
This paper reviews multiple criteria classification methods (or multi-criteria classifiers), particularly those based on concordance/discordance concepts. The concordance refers to an aggregated metric indicating the truthfulness of a proposition according to a coalition of criteria. The discordance is an aggregated metric representing the strength of the opposition coalition to the truthfulness of...
Over-the-horizon (OTH) radar and automatic identification system (AIS) are commonly used in the surveillance of maritime areas. This paper presents a method, which includes tracking and association algorithms, for fusing the information from these two types of systems into an overall maritime picture. Data to be fused consists of asynchronous track estimates from the OTH system and measurements obtained...
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