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Titlebook: Non-Cooperative Target Tracking, Fusion and Control; Algorithms and Advan Zhongliang Jing,Han Pan,Peng Dong Book 2018 Springer Internationa

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發(fā)表于 2025-3-25 05:14:33 | 只看該作者
22#
發(fā)表于 2025-3-25 11:32:28 | 只看該作者
Constrained Image Deblurring with Sparse Proximal Newton Splitting Methodalization of proximal splitting method, which provides a common update strategy by exploiting second derivative information. This is achieved through utilizing the sparse pattern of inverse Hessian matrix. To alleviate the difficulties of the weighted least squares problem, an approximate solution i
23#
發(fā)表于 2025-3-25 14:27:27 | 只看該作者
Simultaneous Visual Recognition and Tracking Based on Joint Decision and Estimations separate steps, whereas tracking and recognition are closely interrelated and can help each other potentially and significantly. To tackle this problem, based on the joint decision and estimation (JDE) model which guarantees the general decision (recognition) and estimation (tracking) arriving at
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發(fā)表于 2025-3-25 19:11:08 | 只看該作者
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發(fā)表于 2025-3-25 21:07:38 | 只看該作者
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發(fā)表于 2025-3-26 02:55:02 | 只看該作者
Multi-Focus Image Fusion Using Pulse Coupled Neural Networkti-focus image fusion method based on image blocks and pulse coupled neural network (PCNN). First, registered source images are divided into blocks. Then energy of image Laplacian is used to generate feature maps. The feature maps are used as external stimulus to be inputs of PCNN. Finally, the fuse
27#
發(fā)表于 2025-3-26 05:17:55 | 只看該作者
Evaluation of Focus Measures in Multi-Focus Image Fusiond status and identity of the observed object or scene. Multi-focus image fusion plays an important role on the improvement of the perceptual quality, especially within spatial and temporal textures. In this chapter, several focus measures for multi-focus image fusion were reviewed. These measures co
28#
發(fā)表于 2025-3-26 11:31:20 | 只看該作者
29#
發(fā)表于 2025-3-26 14:31:28 | 只看該作者
30#
發(fā)表于 2025-3-26 17:25:00 | 只看該作者
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