Finally, some experiment results are given based on our proposed approach, and compared to that of the EM algorithm. The results show that our algorithm gives better results than the EM algorithm both in the quality of the segmented image and the computational time.doi:10.1007/978-3-540-37258-5_86Xian-Bin WenTianjin University ...
Instead of using training data as in traditional approaches, we model the alignment as unobserved latent variables and employ EM-fashioned algorithm for the learning, leading to an unsupervised approach. For scalability, we achieve time complexity of O(N D2), which can be considered as linear ...
A consensus-based decentralized training algorithm for deep neural networks with communi- cation compression. Neurocomputing, 440:287–296, 2021. 1 [19] Bo Liu, Zhengtao Ding, and Chen Lv. Distributed training for multi-layer neural networks...
2. Related Works Tracking by Detection Predominant approaches [6, 44] mainly follow the tracking-by-detection pipeline: an object detector first predicts the object bounding boxes for each frame, and a separate algorithm is then used to associate the...
It is an implementation of the RProp algorithm for multilayer feed forward networks [167]. MLP has the capacity to learn nonlinear models in real time. MLP can have one or more nonlinear hidden layers between the input and output layers. For each hidden layer, different numbers of hidden neur...
A valid Bootstrapping stochastic annealing Expectation Maximization (BSAEM) algorithm is proposed for unsupervised and multiscale segmentation of synthetic aperture radar (SAR) imagery. Given an original SAR images, we construct multiscale sequence of SAR imagery and randomly select a small ...
Bootstrapping Stochastic Annealing EM Algorithm for Multiscale Segmentation of SAR ImageryWen, X.-B.Tian, Z.Zhang, H.LECTURE NOTES IN CONTROL AND INFORMATION SCIENCES
In the future when we have larger numbers of matrix elements to scan, the advanced OPE search algorithm will be a useful tool. – 19 – Figure 6. Upper bound on the gap of the (1 + 1)-dimensional transverse field Ising model as a function of field strength h. The colors of the ...
To gain even more evidence for the self-consistency of this approach, one could try to implement this bootstrap algorithm and push the computer to iteratively produce higher and higher orders. Mathematically, one could also ask for a proof that our algorithm always works. When considering NC-YM...
In addition to the above three processing steps, the framework applies one of the most popular line simplifications—the Douglas–Peucker algorithm [42]—to finalize the semi-ready data. The algorithm needs to set a tolerance parameter, ε. The first point ps and the last point pe are ...