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

2014 ◽  
pp. 24-33 ◽  
Author(s):  
Chen-Rui Chou ◽  
Stephen Pizer
Keyword(s):  
Deformable Registration ◽  
Local Regression ◽  
Forest Classification ◽  
Regression Learning

scholarly journals Brain Deformable Registration Using Global and Local Label-Driven Deep Regression Learning in the First Year of Life

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 25691-25705
Author(s):  
Shunbo Hu ◽  
Lintao Zhang ◽  
Guoqiang Li ◽  
Mingtao Liu ◽  
Deqian Fu ◽  
...  
Keyword(s):  
Deformable Registration ◽  
First Year ◽  
Regression Learning ◽  
First Year Of Life ◽  
Global And Local

A differentially private greedy decision forest classification algorithm with high utility

2020 ◽  
Vol 96 ◽  
pp. 101930
Author(s):  
Zhitao Guan ◽  
Xianwen Sun ◽  
Lingyun Shi ◽  
Longfei Wu ◽  
Xiaojiang Du
Keyword(s):  
Classification Algorithm ◽  
Forest Classification ◽  
High Utility ◽  
Decision Forest

Level-wise driving knowledge induction for embedded automatic train driving system

2021 ◽  
pp. 107754632110033
Author(s):  
Gang Xiao ◽  
Qinwen Yang ◽  
Fan Yang ◽  
Tao Liu ◽  
Tao Li ◽  
...  
Keyword(s):  
Embedded System ◽  
Fuzzy Rule ◽  
Operating Efficiency ◽  
Driving Experience ◽  
Driving System ◽  
Automatic Driving ◽  
Energy Consumption Reduction ◽  
Experience Levels ◽  
Consumption Reduction ◽  
Regression Learning

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.

scholarly journals Quantitative Assessment of Shape Deformation of Regional Cranial Bone for Evaluation of Surgical Effect in Patients with Craniosynostosis

2021 ◽  
Vol 11 (3) ◽  
pp. 990
Author(s):  
Min Jin Lee ◽  
Helen Hong ◽  
Kyu Won Shim
Keyword(s):  
Quantitative Assessment ◽  
Deformable Registration ◽  
Occipital Bone ◽  
Cranial Bone ◽  
Shape Deformation ◽  
Surface Model ◽  
Cephalic Index ◽  
Skull Shape ◽  
Quantification Method ◽  
Parietal Bones

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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