Spatial information on a polymer gel as studied by proton NMR imaging. 1. Image analysis of stress-strain

1992 ◽  
Vol 25 (24) ◽  
pp. 6505-6509 ◽  
Author(s):  
Hidekazu Yasunaga ◽  
Hiromichi Kurosu ◽  
Isao Ando
Keyword(s):  
Image Analysis ◽  
Spatial Information ◽  
Proton Nmr ◽  
Nmr Imaging ◽  
Polymer Gel ◽  
Stress Strain

Spatial information on a polymer gel as studied by 1H-NMR imaging

1998 ◽  
Vol 6 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Yoshio Hotta ◽  
Tomohiro Shibuya ◽  
Hidekazu Yasunaga ◽  
Hiromichi Kurosu ◽  
Isao Ando
Keyword(s):  
1H Nmr ◽  
Spatial Information ◽  
Nmr Imaging ◽  
Polymer Gel

Spatial Information on a Polymer Gel As Studied by 1H NMR Imaging. 2. Shrinkage by the Application of an Electric Field to a Polymer Gel

1995 ◽  
Vol 28 (13) ◽  
pp. 4377-4382 ◽  
Author(s):  
Tomohiro Shibuya ◽  
Hidekazu Yasunaga ◽  
Hiromichi Kurosu ◽  
Isao Ando
Keyword(s):  
Electric Field ◽  
1H Nmr ◽  
Spatial Information ◽  
Nmr Imaging ◽  
Polymer Gel

Image analysis of the spatial distribution of paramagnetic Pr3+ ions in a polymer gel by a 1 H-chemical shift NMR imaging method

2004 ◽  
Vol 207 (1) ◽  
pp. 105-110 ◽  
Author(s):  
Satomi Yokota ◽  
Akitsugu Sasaki ◽  
Yoshio Hotta ◽  
Yuji Yamane ◽  
Hideaki Kimura ◽  
...  
Keyword(s):  
Image Analysis ◽  
Spatial Distribution ◽  
Chemical Shift ◽  
Nmr Imaging ◽  
Imaging Method ◽  
Polymer Gel

Stress–strain behavior in uniaxial compression of polymer gel beads

2006 ◽  
Vol 102 (3) ◽  
pp. 3037-3047 ◽  
Author(s):  
Runi D. Egholm ◽  
Søren F. Christensen ◽  
Peter Szabo
Keyword(s):  
Uniaxial Compression ◽  
Polymer Gel ◽  
Stress Strain ◽  
Strain Behavior ◽  
Gel Beads

scholarly journals BRIDGE CRACK DETECTION USING MULTI-ROTARY UAV AND OBJECT-BASE IMAGE ANALYSIS

2017 ◽  
Vol XLII-2/W6 ◽  
pp. 311-318 ◽  
Author(s):  
J. Y. Rau ◽  
K. W. Hsiao ◽  
J. P. Jhan ◽  
S. H. Wang ◽  
W. C. Fang ◽  
...  
Keyword(s):  
Image Analysis ◽  
Crack Detection ◽  
Spatial Information ◽  
Local Coordinate System ◽  
Image Processing Technique ◽  
Classification Rule ◽  
Control Points ◽  
Surface Geometry ◽  
Bridge Inspection ◽  
Volume Calculation

Bridge is an important infrastructure for human life. Thus, the bridge safety monitoring and maintaining is an important issue to the government. Conventionally, bridge inspection were conducted by human in-situ visual examination. This procedure sometimes require under bridge inspection vehicle or climbing under the bridge personally. Thus, its cost and risk is high as well as labor intensive and time consuming. Particularly, its documentation procedure is subjective without 3D spatial information. In order cope with these challenges, this paper propose the use of a multi-rotary UAV that equipped with a SONY A7r2 high resolution digital camera, 50 mm fixed focus length lens, 135 degrees up-down rotating gimbal. The target bridge contains three spans with a total of 60 meters long, 20 meters width and 8 meters height above the water level. In the end, we took about 10,000 images, but some of them were acquired by hand held method taken on the ground using a pole with 2–8 meters long. Those images were processed by Agisoft PhotoscanPro to obtain exterior and interior orientation parameters. A local coordinate system was defined by using 12 ground control points measured by a total station. After triangulation and camera self-calibration, the RMS of control points is less than 3 cm. A 3D CAD model that describe the bridge surface geometry was manually measured by PhotoscanPro. They were composed of planar polygons and will be used for searching related UAV images. Additionally, a photorealistic 3D model can be produced for 3D visualization. In order to detect cracks on the bridge surface, we utilize object-based image analysis (OBIA) technique to segment the image into objects. Later, we derive several object features, such as density, area/bounding box ratio, length/width ratio, length, etc. Then, we can setup a classification rule set to distinguish cracks. Further, we apply semi-global-matching (SGM) to obtain 3D crack information and based on image scale we can calculate the width of a crack object. For spalling volume calculation, we also apply SGM to obtain dense surface geometry. Assuming the background is a planar surface, we can fit a planar function and convert the surface geometry into a DSM. Thus, for spalling area its height will be lower than the plane and its value will be negative. We can thus apply several image processing technique to segment the spalling area and calculate the spalling volume as well. For bridge inspection and UAV image management within a laboratory, we develop a graphic user interface. The major functions include crack auto-detection using OBIA, crack editing, i.e. delete and add cracks, crack attributing, 3D crack visualization, spalling area/volume calculation, bridge defects documentation, etc.

Fuzzy Clustering-Based Approaches in Automatic Lip Segmentation from Color Images

2006 ◽  
pp. 292-317
Author(s):  
Shilin Wang ◽  
Wing Hong Lau ◽  
Alan Wee-Chung Liew ◽  
Shu Hung Leung
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Visual Information ◽  
Spatial Information ◽  
Noisy Environments ◽  
Low Contrast ◽  
Speaker Authentication ◽  
Lip Segmentation ◽  
Traditional Image

Recently, lip image analysis has received much attention because the visual information extracted has been shown to provide significant improvement for speech recognition and speaker authentication, especially in noisy environments. Lip image segmentation plays an important role in lip image analysis. This chapter will describe different lip image segmentation techniques, with emphasis on segmenting color lip images. In addition to providing a review of different approaches, we will describe in detail the state-of-the-art classification-based techniques recently proposed by our group for color lip segmentation: “Spatial fuzzy c-mean clustering” (SFCM) and “fuzzy c-means with shape function” (FCMS). These methods integrate the color information along with different kinds of spatial information into a fuzzy clustering structure and demonstrate superiority in segmenting color lip images with natural low contrast in comparison with many traditional image segmentation techniques.

scholarly journals Proton NMR imaging in experimental ischemic infarction.

Stroke ◽  
1983 ◽  
Vol 14 (2) ◽  
pp. 178-184 ◽  
Author(s):  
F S Buonanno ◽  
I L Pykett ◽  
T J Brady ◽  
J Vielma ◽  
C T Burt ◽  
...  
Keyword(s):  
Proton Nmr ◽  
Nmr Imaging ◽  
Ischemic Infarction

{13C}-polarization transfer proton NMR imaging of a sodium [13C]formate phantom at 4.7 tesla

1988 ◽  
Vol 76 (2) ◽  
pp. 380-385 ◽  
Author(s):  
Laurel O Sillerud ◽  
David B Van Hulsteyn ◽  
Richard H Griffey
Keyword(s):  
Proton Nmr ◽  
Polarization Transfer ◽  
Nmr Imaging

Elastomeric Polymers. 1. Application of Proton NMR Imaging to the Morphological Study of a Silicone Rubber

2002 ◽  
Vol 35 (5) ◽  
pp. 1714-1721 ◽  
Author(s):  
Maristella Gussoni ◽  
Fulvia Greco ◽  
Marina Mapelli ◽  
Alessandra Vezzoli ◽  
Elisabetta Ranucci ◽  
...  
Keyword(s):  
Silicone Rubber ◽  
Morphological Study ◽  
Proton Nmr ◽  
Nmr Imaging
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