Download Combinatorial Image Analaysis: 15th International Workshop, by Mousumi Dutt, Arindam Biswas, Partha Bhowmick, Bhargab B. PDF

By Mousumi Dutt, Arindam Biswas, Partha Bhowmick, Bhargab B. Bhattacharya (auth.), Reneta P. Barneva, Valentin E. Brimkov, Jake K. Aggarwal (eds.)

This quantity constitutes the refereed court cases of the fifteenth overseas Workshop on Combinatorial snapshot research, IWCIA 2012, held in Austin, TX, united states in November 2012. The 23 revised complete papers awarded have been rigorously reviewed and chosen from various submissions. the subjects lined comprise electronic geometry, combinatorics in electronic areas, electronic curves and surfaces; electronic topologyl grammars, transformation, functions; grammars and types in snapshot research; photograph modifications, morphologic operations, photo segmentation; and discrete tomography, applications.

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We use both digital Gaussian curvatures and digital mean curvatures to characterize 3D shapes. Then propose a multiscale method and a feature vector method for 3D similarity measurement. We found that Gaussian curvatures mainly describe the global features and average characteristics such as the five regions of a human face. However, mean curvatures can be used to find local features and extreme points such as nose in 3D facial data. Keywords: Digital space, Digital Gaussian curvature, Digital mean curvature, Multi-scale, Feature vector, Classification.

Simulation results in R^4 Simulation results in R^6 iterations iterations 12 10 8 6 4 2 15 10 5 50 100 150 200 250 300 data points 50 100 150 200 250 300 data points Fig. 6. Time complexity for varying size of the data set. From the above analysis, the expected number of iteration grows as (n + 1) log N . The experimental results show the average number of iterations that were needed to find the enclosing elemental subset. The average was taken over 100 experiments for each N = 10, 20, . .. The smooth curves show plots of the best fit of the form a + b log2 (N ) to the experimental results, where N represents the number of data points.

Journal of Visual Communication and Image Representation 21, 295–310 (2010) 5. com 6. : Tomographic image reconstruction from optical projections in light-diffusing media. Appl. Opt. 36, 180–213 (1997) 7. : Digital Tomographic Filtering of Radiographs. IEEE Trans. Medical Imaging 2(2), 76–88 (1983) 8. : A short reader’s guide to 3D tomographic reconstruction. Comput. Med. Imaging Graph. 25(2), 113–116 (2001) 30 N. Karmakar, A. Biswas, and P. Bhowmick 9. com 10. in 11. : Construction of 3D Orthogonal Cover of a Digital Object.

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