scholarly journals An automatic method for removing empty camera trap images using ensemble learning

2021 ◽  
Author(s):  
Deng‐Qi Yang ◽  
Kun Tan ◽  
Zhi‐Pang Huang ◽  
Xiao‐Wei Li ◽  
Ben‐Hui Chen ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Camera Trap ◽  
Automatic Method

Automatic measurement of SAED patterns from asbestos minerals

1988 ◽  
Vol 46 ◽  
pp. 846-847
Author(s):  
J. C. Russ ◽  
T. Taguchi ◽  
P. M. Peters ◽  
E. Chatfield ◽  
J. C. Russ ◽  
...  
Keyword(s):  
Diffraction Pattern ◽  
High Precision ◽  
Diffraction Data ◽  
Automatic Measurement ◽  
Automatic Method ◽  
Important Method ◽  
X Ray ◽  
Integrated Intensity ◽  
Asbestiform Minerals ◽  
True Center

Conventional SAD patterns as obtained in the TEM present difficulties for identification of materials such as asbestiform minerals, although diffraction data is considered to be an important method for making this purpose. The preferred orientation of the fibers and the spotty patterns that are obtained do not readily lend themselves to measurement of the integrated intensity values for each d-spacing, and even the d-spacings may be hard to determine precisely because the true center location for the broken rings requires estimation. We have implemented an automatic method for diffraction pattern measurement to overcome these problems. It automatically locates the center of patterns with high precision, measures the radius of each ring of spots in the pattern, and integrates the density of spots in that ring. The resulting spectrum of intensity vs. radius is then used just as a conventional X-ray diffractometer scan would be, to locate peaks and produce a list of d,I values suitable for search/match comparison to known or expected phases.

Automated software supported versus manual aorto-iliac diameter measurements in CT angiography of patients with abdominal aortic aneurysms: Assessment of inter- and intraobserver variation

VASA ◽  
2005 ◽  
Vol 34 (4) ◽  
pp. 255-261 ◽  
Author(s):  
Diehm ◽  
Baumgartner ◽  
Silvestro ◽  
Herrmann ◽  
Triller ◽  
...  
Keyword(s):  
Ct Angiography ◽  
Iliac Artery ◽  
Automatic Measurement ◽  
Random Order ◽  
Aortic Aneurysms ◽  
Intraobserver Variability ◽  
Automatic Method ◽  
Left Common Iliac Artery ◽  
Abdominal Aortic ◽  
Intraobserver Variation

Background: Open surgical or endovascular abdominal aortic aneurysm (AAA) relies on precise preprocedual imaging. Purpose of this study was to assess inter- and intraobserver variation of software-supported automated and manual multi row detector CT angiography (MDCTA) in aortoiliac diameter measurements before AAA repair. Patients and methods: Thirty original MDCTA data sets (4 times 2mm collimation) of patients scheduled for endovascular AAA repair were studied on a dedicated software capable of creating two-dimensional reformatted planes orthogonal to the aortoiliac center-line. Measurements were performed twice with a four-week interval between readings. Data were analysed by two blinded readers at random order. Two different measurement methods were performed: reader-assisted freehand wall-to-wall measurement and semi-automatic measurement. Results: Aortoiliac diameters were significantly underestimated by the semi-automatic method as compared to reader-assisted measurements (p < 0.0031). Intraobserver variability of AAA diameter calculation was not significant (p > 0.15) for reader-assisted measurements except for the diameter of the left common iliac artery in reader 2 (p = 0.0045) and it was not significant (p > 0.14) using the semi-automatic method. Interobserver variability was not significant for AAA diameter measurements using the reader-assisted method and for proximal neck analysis with the semi-automatic method (p > 0.27). Relevant interobserver variation was observed for semi-automatic measurement of maximum AAA (p = 0.0007) and iliac artery diameters (p = 0.024). Conclusions: Dedicated MDCTA software provides a useful tool to minimize aortoiliac diameter measurement variation and to improve imaging precision before AAA repair. For reliable AAA diameter analysis the reader-assisted freehand measurement method is recommended to be applied to a set of reformatted CT data as provided by the software used in this study.

A semi-automatic method for the production of primary cell suspensions

1972 ◽  
Vol 22 (3) ◽  
pp. 335-341
Author(s):  
F. C. Belton ◽  
D. J. Garriock
Keyword(s):  
Cell Suspensions ◽  
Primary Cell ◽  
Automatic Method

A Two-Stage Approach for Flight Departure Delay Forecasting Using Ensemble Learning

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Feng Guan ◽  
Mengyan Hao ◽  
Zhen Guo
Keyword(s):  
Ensemble Learning ◽  
Two Stage

The Automatic Method of EEG State Classification by Using Self-Organizing Map

2010 ◽  
Vol 130 (3) ◽  
pp. 420-427 ◽  
Author(s):  
Kazuhiro Tamura ◽  
Takamasa Shimada ◽  
Yoichi Saito
Keyword(s):  
Self Organizing Map ◽  
Automatic Method ◽  
State Classification ◽  
Self Organizing

Software Defect Prediction Incremental Model using Ensemble Learning

2020 ◽  
Vol 16 (11) ◽  
pp. 1771
Author(s):  
Wang Shibo ◽  
Li Yong ◽  
Mi Wenbo ◽  
Liu Ying
Keyword(s):  
Ensemble Learning ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Incremental Model ◽  
Software Defect

Bayesian network ensemble learning with inadequate information

2014 ◽  
Author(s):  
Ruijie Du ◽  
Shuangcheng Wang ◽  
Cuiping Leng ◽  
Yunbin Fu
Keyword(s):  
Bayesian Network ◽  
Ensemble Learning ◽  
Inadequate Information
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