Automatic Detection of Cervical Vertebral Landmarks for Fluoroscopic Joint Motion Analysis

Author(s):  
Ida Marie Groth Jakobsen ◽  
Maciej Plocharski
2013 ◽  
Vol 50 (10) ◽  
pp. 840-844
Author(s):  
Yukiya INOUE ◽  
Mayumi KIHARA ◽  
Junko YOSHIMURA ◽  
Naoki YOSHIDA ◽  
Kenji MATSUMOTO ◽  
...  

2017 ◽  
Author(s):  
Soheil Ghafurian ◽  
Ilker Hacihaliloglu ◽  
Dimitris N. Metaxas ◽  
Virak Tan ◽  
Kang Li

2005 ◽  
Vol 20 (2) ◽  
pp. 166-171 ◽  
Author(s):  
L. Leonard ◽  
D. Sirkett ◽  
G. Mullineux ◽  
G.E.B Giddins ◽  
A.W. Miles

1985 ◽  
Vol 18 (7) ◽  
pp. 531
Author(s):  
Steven Peterson ◽  
Arthur Erdman

1990 ◽  
Vol 23 (3) ◽  
pp. 259-269 ◽  
Author(s):  
A. de Lange ◽  
R. Huiskes ◽  
J.M.G. Kauer

1998 ◽  
Vol 23 (6) ◽  
pp. 792-795 ◽  
Author(s):  
N. SOMIA ◽  
G. S. RASH ◽  
M. WACHOWIAK ◽  
A. GUPTA

We studied the initiation and sequence of digital joint motion during unrestricted flexion and extension using a 3-D motion analysis of all fingers moving simultaneously. Our results showed that motion started in a single joint in 83% of flexion and 80% of extension cycles. The DIP joint initiated flexion and extension in the index, middle, and ring fingers, but in the little finger, flexion started in the PIP joint, and extension in the MP joint. The two most frequent sequences of joint movement during flexion of the three radial fingers were DIP-PIP-MP and PIP-DIP-MP. The two most frequent sequences during extension of the three radial fingers were DIP-MP-PIP followed by DIP-MP/PIP. In the little finger, however, the most frequent sequences during flexion were PIP-DIP-MP followed by DIP-PIP-MP and during extension, DIP-MP/PIP followed by PIP/DIP-MP


Author(s):  
Manasi Pathade ◽  
Madhuri Khambete

Continuous monitoring and automatic detection of crowd activities is extremely helpful for management at public places to avoid any possible disaster. Analysis of crowded scene is a critical task as it typically involves poor resolution of objects, occlusions and complex dynamics. In this paper, we propose a novel, systematic and generalized method based on global motion analysis of people to detect Congestion situation in crowded scenes at entry/exit corridors. Our approach is tested on video footages acquired from surveillance cameras installed at exit corridors of public places. The results show the expediency of our approach.


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