Local Regression Learning via Forest Classification for 2D/3D Deformable Registration

Author(s):  
Chen-Rui Chou ◽  
Stephen Pizer
IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 25691-25705
Author(s):  
Shunbo Hu ◽  
Lintao Zhang ◽  
Guoqiang Li ◽  
Mingtao Liu ◽  
Deqian Fu ◽  
...  

2020 ◽  
Vol 96 ◽  
pp. 101930
Author(s):  
Zhitao Guan ◽  
Xianwen Sun ◽  
Lingyun Shi ◽  
Longfei Wu ◽  
Xiaojiang Du

2021 ◽  
pp. 107754632110033
Author(s):  
Gang Xiao ◽  
Qinwen Yang ◽  
Fan Yang ◽  
Tao Liu ◽  
Tao Li ◽  
...  

Automatic driving of trains can significantly reduce the energy cost and enhance the operating efficiency and safety. The automatic train driving system has to be an embedded system that can run onboard with low power, which necessitates an efficient inference model. In this article, a level-wise driving knowledge induction approach is proposed for embedded automatic train driving systems. The coincident driving patterns in the records of drivers with different experience levels suggest the suitability of a driving experience knowledge rule induction approach. We design a two-level learning approach to obtain both the driving experience pattern in fuzzy rule-based knowledge form and the detailed parameters of velocity and gear by regression learning methods. With 8.93% energy consumption reduction compared with average human drivers, the experiments indicate the effectiveness of our approach.


2021 ◽  
Vol 11 (3) ◽  
pp. 990
Author(s):  
Min Jin Lee ◽  
Helen Hong ◽  
Kyu Won Shim

Surgery in patients with craniosynostosis is a common treatment to correct the deformed skull shape, and it is necessary to verify the surgical effect of correction on the regional cranial bone. We propose a quantification method for evaluating surgical effects on regional cranial bones by comparing preoperative and postoperative skull shapes. To divide preoperative and postoperative skulls into two frontal bones, two parietal bones, and the occipital bone, and to estimate the shape deformation of regional cranial bones between the preoperative and postoperative skulls, an age-matched mean-normal skull surface model already divided into five bones is deformed into a preoperative skull, and a deformed mean-normal skull surface model is redeformed into a postoperative skull. To quantify the degree of the expansion and reduction of regional cranial bones after surgery, expansion and reduction indices of the five cranial bones are calculated using the deformable registration as deformation information. The proposed quantification method overcomes the quantification difficulty when using the traditional cephalic index(CI) by analyzing regional cranial bones and provides useful information for quantifying the surgical effects of craniosynostosis patients with symmetric and asymmetric deformities.


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