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Partial Shape Classification Using Contour Matching in Distance Transformation
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Robust Object Tracking Via Online Dynamic Spatial Bias Appearance Models
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Curve matching for open 2D curves
Pattern Recognition Letters
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Increasing Supply-Chain Visibility with Rule-Based RFID Data Analysis
IEEE Internet Computing
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RFID Infrastructure Design: A Case Study of Two Australian RFID Projects
IEEE Internet Computing
Transf-ID: Automatic ID and Data Capture for Rail Freight Asset Management
IEEE Internet Computing
Experiments with service-oriented architectures for industrial robotic cells programming
Robotics and Computer-Integrated Manufacturing
A Design of Sensor Web Registry for Wireless Sensor Networks with SOA Approach
CICSYN '09 Proceedings of the 2009 First International Conference on Computational Intelligence, Communication Systems and Networks
Scheduling of maintenance work: A constraint-based approach
Expert Systems with Applications: An International Journal
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Computers in Industry
Using graphical process modeling for realizing SOA programming paradigms in sensor networks
WONS'09 Proceedings of the Sixth international conference on Wireless On-Demand Network Systems and Services
Multimedia Tools and Applications
Optimizing Resource Conflicts in Workflow Management Systems
IEEE Transactions on Knowledge and Data Engineering
Modeling Background and Segmenting Moving Objects from Compressed Video
IEEE Transactions on Circuits and Systems for Video Technology
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Future Generation Computer Systems
Human tracking from a mobile agent: Optical flow and Kalman filter arbitration
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Event-driven video adaptation: A powerful tool for industrial video supervision
Multimedia Tools and Applications
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Service Oriented Architecture (SOA) is an evolution of distributed computing based on the request/reply design paradigm for synchronous and asynchronous applications. In such architectures, application developers or system integrators can build scenarios by composing one or more services without knowing the services' underlying implementations. In this paper, we adopt a SOA framework for on-line scheduling complex industrial workflows. The proposed SOA consists of three main layers; the input layer, the benchmarking layer, the prediction layer and the scheduler. The main innovation of our SOA architecture is the prediction layer that supports computer vision tools able to visually observe how an industrial workflow is actually executed. This way, we can automatically estimate the actual execution time for a process. In particular a new self-initialized visual tracker algorithm is proposed in this paper to robustly trace workers' trajectory in a plant via visual observations. Then, part-to-whole curve matching is presented so as to find correspondences among the traced curve and the ideal one and thus improving scheduler efficiency. The input layer interoperably describes industrial operations using the XPDL (an XML-based) format. The benchmarker evaluates much faster than real-time and in an off-line mode how long it takes for an industrial workflow to be executed on a given resource guaranteeing an almost real-time implementation of the video processing algorithms on the plant. The last component of the proposed SOA is the scheduler with the goal to assign the workflows to the available resources. In this paper, a Maximum Benefit First (MBF) scheduler is presented which maximizes the total gain received by the industry when completing execution of all the beneficial operations with minimum violations of their delivery deadlines (that is with the minimum compensations). Experiments have been conducted on a real-world industrial plant of Nissan Iberica automobile construction indicating the efficiency of the proposed system.