Automatic identification of biological microorganisms using three-dimensional complex morphology

2006 ◽  
Vol 11 (2) ◽  
pp. 024017 ◽  
Author(s):  
Seokwon Yeom ◽  
Bahram Javidi
Keyword(s):  
Three Dimensional ◽  
Automatic Identification ◽  
Complex Morphology

3D Digital Measurement of Large Scale Objects Based on Viscous Target

2005 ◽  
Vol 295-296 ◽  
pp. 687-692 ◽  
Author(s):  
B. Wu ◽  
Ji Gui Zhu ◽  
Xue You Yang ◽  
T. Xue ◽  
S.H. Ye
Keyword(s):  
Large Scale ◽  
Screw Theory ◽  
Three Dimensional ◽  
Automatic Identification ◽  
Digital Measurement ◽  
Image Mosaic ◽  
Space Length ◽  
Dimensional Image ◽  
Spatial Image ◽  
Measuring Field

For 3D digital measurement of large scale objects, image mosaic is the key technology to achieve whole measurement for a small measuring field of the sensor unit. A viscous-target-based three-dimensional image mosaic technology is proposed. The screw theory is introduced to realize the spatial image mosaic. The method permits an automatic identification of targets and a better matching for the feature coded technology. In experiments, the method was proved to be valid, with a relatively high precision on three-dimensional image mosaic. The relative error of the space length measurement is less than 0.6%.

scholarly journals Automatic Identification of First-Order Veins and Corolla Contours in Three-Dimensional Floral Images

2020 ◽  
Vol 11 ◽  
Author(s):  
Yi-Hsiang Wang ◽  
Hao-Chun Hsu ◽  
Wen-Chieh Chou ◽  
Chia-Hao Liang ◽  
Yan-Fu Kuo
Keyword(s):  
Three Dimensional ◽  
Automatic Identification ◽  
First Order

scholarly journals Development and Verification of the Capacity Curve for Two Dimensional Reinforced Concrete Moment-Resisting Frames System under Earthquake Loading

2021 ◽  
Vol 27 (6) ◽  
pp. 73-96
Author(s):  
Haider A Abass ◽  
Husain Khalaf Jarallah
Keyword(s):  
Reinforced Concrete ◽  
Plastic Hinge ◽  
Pushover Analysis ◽  
Three Dimensional ◽  
Automatic Identification ◽  
Two Dimensional ◽  
Seismic Evaluation ◽  
Concrete Frames ◽  
Capacity Curves ◽  
Comparison Of The Results

Pushover analysis is an efficient method for the seismic evaluation of buildings under severe earthquakes. This paper aims to develop and verify the pushover analysis methodology for reinforced concrete frames. This technique depends on a nonlinear representation of the structure by using SAP2000 software. The properties of plastic hinges will be defined by generating the moment-curvature analysis for all the frame sections (beams and columns). The verification of the technique above was compared with the previous study for two-dimensional frames (4-and 7-story frames). The former study leaned on automatic identification of positive and negative moments, where the concrete sections and steel reinforcement quantities the source of these moments. The comparison of the results between the two methodologies was carried out in terms of capacity curves. The results of the conducted comparison highlighted essential points. It was included the potential differences between default and user-defined hinge properties in modeling. The effect of the plastic hinge length and the transverse of shear reinforcement on the capacity curves was also observed. Accordingly, it can be considered that the current methodology in this paper more logistic in the representation of two and three-dimensional structures.  

scholarly journals Proposition of local automatic algorithm for landmark detection in 3D cephalometry

2021 ◽  
Vol 10 (2) ◽  
pp. 707-715
Author(s):  
Mohammed Ed-dhahraouy ◽  
Hicham Riri ◽  
Manal Ezzahmouly ◽  
Abdelmajid El Moutaouakkil ◽  
Hakima Aghoutan ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Automatic Identification ◽  
Local Geometry ◽  
3D Images ◽  
Mean Error ◽  
Landmark Identification ◽  
Volume Of Interest ◽  
Intensity Information ◽  
3D Cephalometry ◽  
Geometrical Information

This study proposes a new contribution to solve the problem of automatic landmarks detection in three-dimensional cephalometry. 3D images obtained from CBCT (cone beam computed tomography) equipment were used for automatic identification of twelve landmarks. The proposed method is based on a local geometry and intensity criteria of skull structures. After the step of preprocessing and binarization, the algorithm segments the skull into three structures using the geometry information of nasal cavity and intensity information of the teeth. Each targeted landmark was detected using local geometrical information of the volume of interest containing this landmark. The ICC and confidence interval (95% CI) for each direction were 0, 91 (0.75 to 0.96) for x- direction; 0.92 (0.83 to 0.97) for y-direction; 0.92 (0.79 to 0.97) for z-direction. The mean error of detection was calculated using the Euclidian distance between the 3D coordinates of manually and automatically detected landmarks. The overall mean error of the algorithm was 2.76 mm with a standard deviation of 1.43 mm. Our proposed approach for automatic landmark identification in 3D cephalometric was capable of detecting 12 landmarks on 3D CBCT images which can be facilitate the use of 3D cephalometry to orthodontists.

Automatic Identification and Validation of Planar Collagen Organization in the Aorta Wall with Application to Abdominal Aortic Aneurysm

2013 ◽  
Vol 19 (6) ◽  
pp. 1395-1404 ◽  
Author(s):  
Stanislav Polzer ◽  
T. Christian Gasser ◽  
Caroline Forsell ◽  
Hana Druckmüllerova ◽  
Michal Tichy ◽  
...  
Keyword(s):  
Arterial Wall ◽  
Three Dimensional ◽  
Polarized Light ◽  
Vascular Diseases ◽  
Aortic Aneurysms ◽  
Fast Fourier Transformation ◽  
Automatic Identification ◽  
Collagen Organization ◽  
Aorta Wall ◽  
Abdominal Aortic

AbstractArterial physiology relies on a delicate three-dimensional (3D) organization of cells and extracellular matrix, which is remarkably altered by vascular diseases like abdominal aortic aneurysms (AAA). The ability to explore the micro-histology of the aorta wall is important in the study of vascular pathologies and in the development of vascular constitutive models, i.e., mathematical descriptions of biomechanical properties of the wall. The present study reports and validates a fast image processing sequence capable of quantifying collagen fiber organization from histological stains. Powering and re-normalizing the histogram of the classical fast Fourier transformation (FFT) is a key step in the proposed analysis sequence. This modification introduces a powering parameterw, which was calibrated to best fit the reference data obtained using classical FFT and polarized light microscopy (PLM) of stained histological slices of AAA wall samples. The values ofw= 3 and 7 give the best correlation (Pearson's correlation coefficient larger than 0.7,R2about 0.7) with the classical FFT approach and PLM measurements. A fast and operator independent method to identify collagen organization in the arterial wall was developed and validated. This overcomes severe limitations of currently applied methods like PLM to identify collagen organization in the arterial wall.

scholarly journals PHOTOGRAMMETRIC TECHNOLOGY FOR REMOTE HIGH-PRECISION 3D MONITORING OF CRACKS AND DEFORMATION JOINTS OF BUILDINGS AND CONSTRUCTIONS

2019 ◽  
Vol XLII-5/W2 ◽  
pp. 95-101
Author(s):  
A. E. Wojnarowski ◽  
A. B. Leonteva ◽  
S. V. Tyurin ◽  
S. G. Tikhonov ◽  
O. V. Artemeva
Keyword(s):  
Three Dimensional ◽  
Digital Camera ◽  
Distortion Correction ◽  
Three Dimensions ◽  
Automatic Identification ◽  
Single Image ◽  
Monitoring Equipment ◽  
Photographic Images ◽  
Special Knowledge ◽  
Building Operation

<p><strong>Abstract.</strong> Monitoring of cracks and deformation joints of buildings and engineering constructions can be performed effectively using contemporary methods of photogrammetry. Our study allowed us to design the technology for such a monitoring. This technology is adapted for use by building operation and building inspection specialists and does not require special knowledge in photogrammetry. The monitoring equipment includes two blocks of photogrammetric deformation marks, a digital camera and processing software. Each block of deformation marks is designed as a plate of 60 by 40&amp;thinsp;mm size where several dozens of marks are fixed (size of the plate and number of marks may vary). The relative positions of the marks on the plate are determined while block calibration with an accuracy of several microns. While monitoring is performed, two blocks of deformation marks are fixed on both sides of the crack or deformation join. Then marks are photographed. Almost any digital camera is suitable, beginning with smartphone camera and ending with specialized photogrammetric camera. Further processing of collected imagery is performed on the basis of rigorous methods of photogrammetry (specialized software were developed). The processing assumes automatic identification and measurement of marks on digital photographic images with sub-pixel accuracy. Additionally, the photogrammetric calibration and distortion correction are performed for each image. Three-dimensional spatial solution is possible both in the case of single image processing, and in the case of stereopair processing. The dynamics of crack development in three dimensions is determined by the results of several cycles of observations collected over period. Our technology allows to ensure the accuracy of the coordinates and deformations at the level of 0.005&amp;ndash;0.020&amp;thinsp;mm for the photographing distances from 0.1 to 40&amp;thinsp;m.</p>

scholarly journals The Influence of Pool-Riffle Morphological Features on River Mixing

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1145
Author(s):  
Patricio Fuentes-Aguilera ◽  
Diego Caamaño ◽  
Hernán Alcayaga ◽  
Andrew Tranmer
Keyword(s):  
Three Dimensional ◽  
Predictive Equation ◽  
Dispersion Models ◽  
Complex Morphology ◽  
Bed Morphology ◽  
Pollutant Concentrations ◽  
Natural Streams ◽  
Spatial Fluctuations ◽  
Longitudinal Mixing ◽  
Tracer Experiments

Accurate prediction of pollutant concentrations in a river course is of great importance in environmental management. Mathematical dispersion models are often used to predict the spatial distribution of substances to help achieve these objectives. In practice, these models use a dispersion coefficient as a calibration parameter that is calculated through either expensive field tracer experiments or through empirical equations available in the scientific literature. The latter are based on reach-averaged values obtained from laboratory flumes or simple river reaches, which often show great variability when applied to natural streams. These equations cannot directly account for mixing that relates specifically to spatial fluctuations of channel geometry and complex bed morphology. This study isolated the influence of mixing related to bed morphology and presented a means of calculating a predictive longitudinal mixing equation that directly accounted for pool-riffle sequences. As an example, a predictive equation was developed by means of a three-dimensional numerical model based on synthetically generated pool-riffle bathymetries. The predictive equation was validated with numerical experiments and field tracer studies. The resulting equation was shown to more accurately represent mixing across complex morphology than those relations selected from the literature.

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