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Scale-Invariant Feature Transform (SIFT) [1] has lately attracted attention in computer vision as a robust feature point detection algorithm which is invariant for scale, rotation and illumination change. However, its computational complexity is too high to apply on practical real-time applications. The iterated Gaussian blurred operations on images lead to long computational latency and high memory...
Software Defined Networking (SDN) is a promising networking paradigm that decouples the network control plane from the data forwarding plane. This separation makes it possible for network administrators to overcome the complexity caused by modern networking environment. OpenFlow is a great concept to realize SDN architecture. It simplifies the network and traffic management in enterprise and data...
Based on the steepest descent method, back-propagation neural networks (BPNNs) minimize an energy function for errors occurring between desired and actual outputs. Therefore, conventional BPNNs obtain local optimum weights. Stochastic search optimization methods, such as genetic algorithms, particle swarm optimization methods and artificial immune system (AIS) algorithms, have been extensively used...
Decision making is an important task for enterprise managers, and is typically based on various data sources derived from information systems, such as enterprise resource planning, supply chain management and customer relationship management. Numerous business intelligence tools (BI) thus have been developed to support decision making. Some existing BI tools have several limitations, for example lacking...
Among the advantages of the cerebellar model articulation controller neural network (CMAC NN) include very fast learning, reasonable generalization capability and robust noise resistance, explaining why CMAC NNs are conventionally used in robot control. This study considers the feasibility of CMAC NN as an efficient data mining (DM) method, indicating that the CMAC NN can extend its network topology...
Although applied to classification, neural network (NN) classifiers have certain limitations, including slow training time, complex interpretation and difficult implementation in terms of optimal network topology. To overcome these disadvantages, this study presents an efficient and simple classifier based on the cerebellar model articulation controller NN (CMAC NN), which has the advantages of very...
Background subtraction is often one of the first tasks and a critical part of the machine vision systems. Background modeling methods that only utilize image blocks or pixels suffer from unacceptable false negative detecting rate. A novel layered background modeling method is proposed in objects detecting. First, every block on the first layer is modeled via texture based on local binary pattern (LBP)...
Differential evolution (DE) and particle swarm optimization (PSO) are the evolutionary computation paradigms, and both have shown superior performance on complex nonlinear function optimization problems. This paper detects the underlying relationship between them and then qualitatively proves that the two heuristic approaches from different theoretical background are consistent in form. Within the...
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