Detection of Injury to Grape Leaf Surfaces Using Hybrid Mean Shift and Threshold Optimization Algorithm

In order to improve an efficiency of energy detection for a spectrum sensing in cognitive radio (CR), this paper proposes a dynamic threshold optimization algorithm. The traditional energy detection algorithm uses a fixed threshold, and can't guarantee always the optimal sensing performance in any environment. The improvement for sensing performance need to minimize the undetected probability and the probability of false alarm, and it is dissimilar for different CR users to accept these two errors. We improve the traditional energy detection algorithm, and firstly introduce a preference factor to characterize CR users’ different requirements for these two errors, then, propose a dynamic threshold optimization algorithm by minimizing integrated detection error for different signal-to-noise ratio (SNR). The simulation results show that the proposed algorithm effectively reduces the integrated spectrum sensing error, and increases the probability of detection, especially in low SNR.

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Threshold Optimization of Adaptive Template Filtering for MRI Based on Intelligent Optimization Algorithm

2006 International Conference of the IEEE Engineering in Medicine and Biology Society ◽

10.1109/iembs.2006.4398516 ◽

2006 ◽

Author(s):

Lei Guo ◽

Youxi Wu ◽

Xuena Liu ◽

Ying Li ◽

Guizhi Xu ◽

...

Keyword(s):

Optimization Algorithm ◽

Intelligent Optimization ◽

Threshold Optimization ◽

Intelligent Optimization Algorithm

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Object Tracking with Gradient Part-Based Models

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.490-495.905 ◽

2012 ◽

Vol 490-495 ◽

pp. 905-909 ◽

Cited By ~ 1

Author(s):

Yang Li ◽

Jia Bao Wang ◽

Jian Jiang Lu ◽

Zhuang Miao ◽

Peng Fei Fang

Keyword(s):

Object Tracking ◽

Optimization Algorithm ◽

Mean Shift ◽

Local Optimization ◽

Partial Occlusion ◽

Tracking Accuracy ◽

Tracking Algorithms ◽

Gradient Energy ◽

The Mean

Partial occlusion and non-rigid variation are challenging problems in object tracking. To address this problem, robust gradient part-based models are proposed for object tracking in this paper. Our models constructed multiple well-chosen parts based on the gradient energy map of the object. And the local optimization algorithm, mean shift, is used to search the best locations of the multiple parts, which can be used to rectify the location of the tracked object by weighted feedback. Meanwhile, the models of the root and parts are updated online, which can improve tracking accuracy and robustness. Further, our models are easy to be embedded into different tracking algorithms and we implement the mean shift based on gradient part-based models. Experiments results show that our gradient part-based models are robust enough for object tracking, even though the objects are non-rigid or occluded.

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