3D shape description of the bicipital groove of the proximal humerus

2006 ◽  
Author(s):  
Aaron D. Ward ◽  
Mark E. Schweitzer ◽  
Ghassan Hamarneh
Keyword(s):  
Proximal Humerus ◽  
Shape Description ◽  
Bicipital Groove ◽  
3D Shape

Pseudo-3D Shape Description for Facetted Materials

1998 ◽  
Vol 15 (2) ◽  
pp. 100-107 ◽  
Author(s):  
Marie-Noe¨lle Pons ◽  
Herve´ Vivier ◽  
Thierry Rolland
Keyword(s):  
Shape Description ◽  
3D Shape

Quantitative 3D Shape Description of Dust Particles from Treated Seeds by Means of X-ray Micro-CT

2015 ◽  
Vol 49 (12) ◽  
pp. 7310-7318 ◽  
Author(s):  
Wouter Devarrewaere ◽  
Dieter Foqué ◽  
Udo Heimbach ◽  
Dennis Cantre ◽  
Bart Nicolai ◽  
...  
Keyword(s):  
Dust Particles ◽  
Shape Description ◽  
Micro Ct ◽  
X Ray ◽  
3D Shape

scholarly journals Cortical thickness analysis of the proximal humerus

2017 ◽  
Vol 11 (2) ◽  
pp. 87-93
Author(s):  
Addie Majed ◽  
Tanujan Thangarajah ◽  
Dominic Southgate ◽  
Peter Reilly ◽  
Anthony Bull ◽  
...  
Keyword(s):  
Cortical Thickness ◽  
Structural Changes ◽  
Proximal Humerus ◽  
Articular Surface ◽  
Progressive Increase ◽  
Bicipital Groove ◽  
Lesser Tuberosity ◽  
Greater Tuberosity ◽  
Cortical Thinning ◽  
Engineering Software

Background Structural changes within the proximal humerus influence the mechanical properties of the entire bone and predispose to low-energy fractures with complex patterns. The aim of the present study was to measure the cortical thickness in different regions of the proximal humerus. Methods Thirty-seven proximal humeri were analyzed using novel engineering software to determine cortical thickness in 10 distinct anatomical zones. Results The cortical thickness values ranged from 0.33 mm to 3.5 mm. Fifteen specimens demonstrated a consistent pattern of progressive cortical thinning that increased between the bicipital groove (thickest), the lesser tuberosity and the greater tuberosity (thinnest). Fifteen humeri were characterized by a progressive increase in cortical thickness between the greater tuberosity (thinnest), the bicipital groove and lesser tuberosity (thickest). The diaphysis exhibited the thickest cortical zone in 27 specimens, whereas the articular surface possessed the thinnest cortex in 18 cases. Conclusions In conclusion, this is the first study to comprehensively assess cortical thickness of the humeral head. Our findings suggest that proximal humeral fractures occur along lines of cortical thinning and are displaced by the hard glenoid bone. The identification of specific areas of thick cortices may improve pre-operative planning and optimize fracture fixation.

3D Shape description algorithms applied to the problem of model retrieval

2011 ◽  
Vol 1 (1) ◽  
Author(s):  
Dariusz Frejlichowski
Keyword(s):  
Three Dimensional ◽  
3D Models ◽  
Multimedia Data ◽  
Computer Hardware ◽  
Shape Description ◽  
3D Shape ◽  
Retrieval Result ◽  
Dimensional Shape ◽  
Image Shape ◽  
Three Dimensional Shape

AbstractInterest in three-dimensional shape retrieval is currently increasing, driven by two important reasons — the rapid increase of the amount of multimedia data and a noticeable advance in computer hardware and software during recent years. Presently, it is possible to retrieve complicated 3D models in a reasonable span of time thanks to the use of sophisticated 3D shape description algorithms, a feat which was unthinkable a few years ago. The main issue is the efficiency of the approaches, which must work both quickly and reliably. Hence, in this paper four 3D shape description algorithms — Extended Gaussian Image, Shape Distributions, Shape Histograms and Light Field Descriptor — were experimentally compared in order to determine which was most effective. As it turned out, the latter obtained the best retrieval result.

scholarly journals A Modified Exoskeleton for 3D Shape Description and Recognition.

2003 ◽  
Vol 123 (2) ◽  
pp. 292-300
Author(s):  
Rajalida Lipikorn ◽  
Akinobu Shimizu ◽  
Yoshihiro Hagihara ◽  
Hidefumi Kobatake
Keyword(s):  
Shape Description ◽  
3D Shape

scholarly journals Modeling the Surround of MT Cells and Their Selectivity for Surface Orientation in Depth Specified by Motion

1998 ◽  
Vol 10 (2) ◽  
pp. 295-312 ◽  
Author(s):  
Lin Liu ◽  
Marc M. Van Hulle
Keyword(s):  
Velocity Field ◽  
Projective Transformation ◽  
Receptive Fields ◽  
Directional Derivatives ◽  
Theoretical Studies ◽  
Shape Description ◽  
Motion Information ◽  
Middle Temporal ◽  
3D Shape ◽  
Derivatives Of

The projective transformation onto the retina loses the explicit 3D shape description of a moving object. Theoretical studies show that the reconstruction of 3D shape from 2D motion information (shape from motion, SFM) is feasible provided that the first- and second-order directional derivatives of the 2D velocity field are available. Experimental recordings have revealed that the receptive fields of the majority of the cells in macaque area middle temporal (MT) display an antagonistic (suppressive) surround and that a sizable portion of these surrounds are asymmetrical. This has led to the conjecture that these cells provide a local measure for the directional derivatives of the 2D velocity field. In this article, we adopt a nonparametric and biologically plausible approach to modeling the role played by the MT surrounds in the recovery of the orientation in depth (the slant and tilt) of a moving (translating) plane. A three-layered neural network is trained to represent the slant and tilt from the projected motion vectors. The hidden units of the network have speed-tuning characteristics and represent the MT model neurons with their surrounds. We conjecture that the MT surround results from lateral inhibitory connections with other MT cells and that populations of these cells, with different surround types, code linearly for slant and tilt of translating planes.

A sparse coding approach for local-to-global 3D shape description

2014 ◽  
Vol 30 (11) ◽  
pp. 1233-1245 ◽  
Author(s):  
Davide Boscaini ◽  
Umberto Castellani
Keyword(s):  
Sparse Coding ◽  
Shape Description ◽  
3D Shape
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