For multi-sensor data fusionapplications the accurate alignment of different sensor data is essential for the proper combination of matching features. In food inspection system the boxing often is in a rectangular sh...
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ISBN:
(纸本)9788362065271
For multi-sensor data fusionapplications the accurate alignment of different sensor data is essential for the proper combination of matching features. In food inspection system the boxing often is in a rectangular shape. This knowledge can be used to rectify the image data, an important step in the alignment stage. In case of low contrast between boxing and background, the detected contour may differ significantly from the actual values. In this paper the performance of the Hough transform and the RANdom SAmple Consensus (RANSAC)-algorithm are evaluated relating to the correct extraction of the boxing contour out of contour data distorted by position errors of the outer shape. The evaluation results indicate the superiority of the RANSAC algorithm with respect to scalability, robustness and execution time.
In a multi-node distributed decision system under some conditions there are few or none permitted information exchange between the nodes, this makes the information fusion and final decision difficult. However if we t...
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ISBN:
(纸本)0819444812
In a multi-node distributed decision system under some conditions there are few or none permitted information exchange between the nodes, this makes the information fusion and final decision difficult. However if we treat this distributed system as a multi-agent system, and each node acts as an agent, it has some other node's historical experiences or knowledge for resolving problems and stored in additional case bases, so it can uses case based reasoning (CBR) and transposition reasoning to obtain the possible viewpoints or decisions of other nodes and then makes information fusion by itself This approach may reduce the subjectivism which is the weakness of pure transposition reasoning.
This paper describes a preliminary approach to the fusion of multi-spectral image data for the analysis of cervical cancer. The long-term goal of this research is to define spectral signatures and automatically detect...
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ISBN:
(纸本)0819431931
This paper describes a preliminary approach to the fusion of multi-spectral image data for the analysis of cervical cancer. The long-term goal of this research is to define spectral signatures and automatically detect cancer cell structures. The approach combines a multi-spectral microscope with an image analysis tool suite, MathWeb. The tool suite incorporates a concurrent Principal Component Transform (PCT) that is used to fuse the multi-spectral data. This paper describes the general approach and the concurrent PCT algorithm. The algorithm is evaluated from both the perspective of image quality and performance scalability.
This paper describes two practical fusion techniques (hybrid fusion and cued fusion) for automatic target cueing that combine features derived from each sensor data at the object-level. In the hybrid fusion method eac...
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ISBN:
(纸本)0819431931
This paper describes two practical fusion techniques (hybrid fusion and cued fusion) for automatic target cueing that combine features derived from each sensor data at the object-level. In the hybrid fusion method each of the input sensor data is prescreened (i.e. Automatic Target Cueing (ATC) is performed) before the fusion stage. The cued fusion method assumes that one of the sensors is designated as a primary sensor, and thus ATC is only applied to its input data. If one of the sensors exhibits a higher Pd and/or a lower false alarm rate, it can be selected as the primary sensor, However, if the ground coverage can be segmented to regions in which one of the sensors is known to exhibit better performance, then the cued fusion can be applied locally/adaptively by switching the choice of a primary sensor. Otherwise, the cued fusion is applied both ways (each sensor as primary) and the outputs of each cued mode are combined. Both fusion approaches use a back-end discrimination stage that is applied to a combined feature vector to reduce false alarms. The two fusion processes were applied to spectral and radar sensor data and were shown to provide substantial False alarm reduction. The approaches are easily extendable to more than two sensors.
This paper reports a metamaterial inspired combined inductive-capacitive sensing method for detecting and distinguishing metallic and non-metallic objects. Metallic and non-metallic objects can be distinguished by mea...
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ISBN:
(纸本)9781628410587
This paper reports a metamaterial inspired combined inductive-capacitive sensing method for detecting and distinguishing metallic and non-metallic objects. Metallic and non-metallic objects can be distinguished by measuring both of their inductive and capacitive responses based on the fact that they respond differently to inductive and capacitive sensing. The proposed method is inspired by metamaterial structures. Both inductive and capacitive sensing are simultaneously realized when the sensor is operating at off-resonant frequencies. The proposed method is demonstrated with typical printed circuit board (PCB) technology. The designed sensor can distinguish the metallic and dielectric objects with a sensing range about 10 mm, showing a competitive performance compared with commercially available proximity sensors.
The potential problem of deterioration in recognition system performance because of imprecise, incomplete, or imperfect training is a serious challenge inherent to most real-world applications. This problem is often r...
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The potential problem of deterioration in recognition system performance because of imprecise, incomplete, or imperfect training is a serious challenge inherent to most real-world applications. This problem is often referred to in certain applications as degradation of performance under off-nominal conditions. This study presents the results of an investigation carried out to illustrate the scope and benefits of information fusion in such off-nominal scenarios. The research covers features in - decision out (FEI-DEO) fusion as well as decisions in - decision out (DEI-DEO) fusion. The latter spans across both information sources (sensors) and multiple processing tools (classifiers). The investigation delineates the corresponding fusion benefit domains using as an example, real-world data from an audio-visual system for the recognition of French oral vowels embedded in various levels of acoustical noise.
The Common Object Request Broker Architecture (COBRA) has been proven to be effective for application in the Data fusion domain. However, the benefits of this system have not yet been fully realized because of unresol...
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The Common Object Request Broker Architecture (COBRA) has been proven to be effective for application in the Data fusion domain. However, the benefits of this system have not yet been fully realized because of unresolved issues concerning reliability, fault-tolerance and real-time/fast enough QoS behavior of the system. In view of this, an attempt has been made to develop a domain specific environment with the commercially available standard products. The result is a COBRA based infrastructure (CORBIS) that provide interfaces and mechanisms for various applications and services.
The paper presents the concept and initial tests from the hardware implementation of a low-power, high-speed reconfigurable sensorfusion processor. The Extended Logic Intelligent Processing System (ELIPS) processor i...
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ISBN:
(纸本)0819431931
The paper presents the concept and initial tests from the hardware implementation of a low-power, high-speed reconfigurable sensorfusion processor. The Extended Logic Intelligent Processing System (ELIPS) processor is developed to seamlessly combine rule-based systems, fuzzy logic, and neural networks to achieve parallel fusion of sensor in compact low power VLSI. The first demonstration of the FLIPS concept targets interceptor functionality;other applications, mainly in robotics and autonomous systems are considered for the future. The main assumption behind ELIPS is that fuzzy, rule-based and neural forms of computation can serve as the main primitives of an "intelligent" processor. Thus, in the same way classic processors are designed to optimize the hardware implementation of a set of fundamental operations, ELIPS is developed as an efficient implementation of computational intelligence primitives, and relies on a set of fuzzy set, fuzzy inference and neural modules, built in programmable analog hardware. The hardware programmability allows the processor to reconfigure into different machines, taking the most efficient hardware implementation during each phase of information processing. Following software demonstrations on several interceptor data three important ELIPS building blocks (a fuzzy set preprocessor, a rule-based fuzzy system and a neural network) have been fabricated in analog VLSI hardware and demonstrated microsecond-processing times.
Effective detection of road objects in diverse environmental conditions is a critical requirement for autonomous driving systems. Multi-modal sensorfusion is a promising approach for improving perception, as it enabl...
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Effective detection of road objects in diverse environmental conditions is a critical requirement for autonomous driving systems. Multi-modal sensorfusion is a promising approach for improving perception, as it enables the combination of information from multiple sensor streams in order to optimize the integration of their respective data. fusion operators are employed within fully convolutional architectures to combine features derived from different modalities. In this research, we present a framework that utilizes early fusion mechanisms to train and evaluate 2D object detection algorithms. Our evaluation shows that sensorfusion outperforms RGB-only detection methods, yielding a boost of +15.07% for car detection, +10.81% for pedestrian detection, and +19.86% for cyclist detection. In our comparative study, we evaluated three arithmetic-based fusion operators and two learnable fusion operators. Furthermore, we conducted a performance comparison between early- and mid-level fusion techniques and investigated the effects of early fusion on state-of-the-art 3D object detectors. Lastly, we provide a comprehensive analysis of the computational complexity of our proposed framework, along with an ablation study.
We propose an unbiased multifeature fusion Pulse Coupled Neural Network (PCNN) algorithm. The method shares linking between several PCNNs running in parallel. We illustrate the PCNN fusion technique with a clean and n...
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We propose an unbiased multifeature fusion Pulse Coupled Neural Network (PCNN) algorithm. The method shares linking between several PCNNs running in parallel. We illustrate the PCNN fusion technique with a clean and noisy three-band color image example.
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