Optimal geometric algorithms for digitized pictures on an optical mesh

2003 ◽  
Author(s):  
M.M. Eshaghian ◽  
V.K.P. Kumar
Keyword(s):  
Geometric Algorithms ◽  
Digitized Pictures

An Overview of Digital Image Processing and Recognition (Invited Paper)

1982 ◽  
Vol 40 ◽  
pp. 700-702
Author(s):  
R. C. Gonzalez
Keyword(s):  
Image Processing ◽  
New York ◽  
Digital Image Processing ◽  
Digital Image ◽  
Practical Implementation ◽  
Submarine Cable ◽  
Vigorous Growth ◽  
Digitized Pictures ◽  
Processing Techniques ◽  
And Storage

Interest in digital image processing techniques dates back to the early 1920's, when digitized pictures of world news events were first transmitted by submarine cable between New York and London. Applications of digital image processing concepts, however, did not become widespread until the middle 1960's, when third-generation digital computers began to offer the speed and storage capabilities required for practical implementation of image processing algorithms. Since then, this area has experienced vigorous growth, having been a subject of interdisciplinary research in fields ranging from engineering and computer science to biology, chemistry, and medicine.

Parallel geometric algorithms on a mesh-connected computer

Algorithmica ◽  
1990 ◽  
Vol 5 (1-4) ◽  
pp. 155-177 ◽  
Author(s):  
C. S. Jeong ◽  
D. T. Lee
Keyword(s):  
Geometric Algorithms

scholarly journals Sublinear Geometric Algorithms

2005 ◽  
Vol 35 (3) ◽  
pp. 627-646 ◽  
Author(s):  
Bernard Chazelle ◽  
Ding Liu ◽  
Avner Magen
Keyword(s):  
Geometric Algorithms

GEOMETRIC ALGORITHMS FOR THE CONSTRAINED 1-D K-MEANS CLUSTERING PROBLEMS AND IMRT APPLICATIONS

2009 ◽  
Vol 20 (02) ◽  
pp. 361-377
Author(s):  
DANNY Z. CHEN ◽  
MARK A. HEALY ◽  
CHAO WANG ◽  
BIN XU
Keyword(s):  
Radiation Therapy ◽  
Treatment Planning ◽  
Intensity Modulated Radiation Therapy ◽  
Geometric Algorithms ◽  
Maximum Difference ◽  
Continuous Version ◽  
Heuristic Approaches ◽  
Clustering Problem ◽  
Intensity Modulated ◽  
Clustering Problems

In this paper, we present efficient geometric algorithms for the discrete constrained 1-D K-means clustering problem and extend our solutions to the continuous version of the problem. One key clustering constraint we consider is that the maximum difference in each cluster cannot be larger than a given threshold. These constrained 1-D K-means clustering problems appear in various applications, especially in intensity-modulated radiation therapy (IMRT). Our algorithms improve the efficiency and accuracy of the heuristic approaches used in clinical IMRT treatment planning.

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