scholarly journals Comparative Study of Depth-Image Matching with Steepest Descendent and Simulated Annealing Algorithms

Author(s):  
Hiroshi Noborio ◽  
Shogo Yoshida ◽  
Kaoru Watanabe ◽  
Daiki Yano ◽  
Masanao Koeda
2016 ◽  
Vol 11 (4) ◽  
pp. 373
Author(s):  
Hamza Kamal Idrissi ◽  
Zaid Kartit ◽  
Ali Kartit ◽  
Mohamed El Marraki

2016 ◽  
Vol 11 (1) ◽  
pp. 42 ◽  
Author(s):  
Hamza Kamal Idrissi ◽  
Zaid Kartit ◽  
Ali Kartit ◽  
Mohamed El Marraki

Author(s):  
Roberto Benedetti ◽  
Maria Michela Dickson ◽  
Giuseppe Espa ◽  
Francesco Pantalone ◽  
Federica Piersimoni

AbstractBalanced sampling is a random method for sample selection, the use of which is preferable when auxiliary information is available for all units of a population. However, implementing balanced sampling can be a challenging task, and this is due in part to the computational efforts required and the necessity to respect balancing constraints and inclusion probabilities. In the present paper, a new algorithm for selecting balanced samples is proposed. This method is inspired by simulated annealing algorithms, as a balanced sample selection can be interpreted as an optimization problem. A set of simulation experiments and an example using real data shows the efficiency and the accuracy of the proposed algorithm.


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