472
S. Schiele et al.
13. Singh, M.R., Verma, P., Tung, H.-H., Bordawekar, S., Ramkrishna, D.: Screening crystal
morphologies from crystal structure. Cryst. Growth Des. 13(4), 1390–1396 (2013)
14. Schneider, R.: Convex Bodies: The Brunn-Minkowski Theory. Cambridge University Press,
Cambridge (2008)
15. Al-Rousan, T., Masad, E., Tutumluer, E., Pan, T.: Evaluation of image analysis techniques for
quantifying aggregate shape characteristics. Constr. Build. Mater. 21(5), 978–990 (2007)
16. Hentschel, M.L., Page, N.W.: Selection of descriptors for particle shape characterization. Part.
Part. Syst. Charact. 20(1), 25–38 (2003)
17. Masad, E., Button, J.W.: Unified imaging approach for measuring aggregate angularity and
texture. Comput.-Aided Civil Infrastruct. Eng. 15(4), 273–280 (2000)
18. Pons, M.N., et al.: Particle morphology: from visualisation to measurement. Powder Technol.
103(1), 44–57 (1999)
19. Burger, W., Burge, M.J.: Principles of Digital Image Processing, Undergraduate Topics in
Computer Science (2009)
20. Schorsch, S., Ochsenbein, D.R., Vetter, T., Morari, M., Mazzotti, M.: High accuracy online
measurement of multidimensional particle size distributions during crystallization. Chem. Eng.
Sci. 105, 155–168 (2014)
21. Gahn, C., Mersmann, A.: Brittle fracture in crystallization processes Part A. Attrition and
abrasion of brittle solids. Chem. Eng. Sci. 54(9), 1273–1282 (1999)
22. Briesen, H.: Model-based analysis of the effect of particle and impact geometry on attrition of
brittle material. Powder Technol. 178(2), 87–98 (2007)
23. Briesen, H.: Two-dimensional population balance modeling for shape dependent crystal
attrition. Chem. Eng. Sci. 64(4), 661–672 (2009)
24. Gorski, K.M., et al.: HEALPix: a framework for high-resolution discretization and fast analysis
of data distributed on the sphere. Astrophys J 622(2), 759–771 (2005)
25. Naruse, Y.: HEALPix Library for Matlab. 1.0 ed (2011)
26. Neubeck, A., Van Gool, L.: Efficient non-maximum suppression. In: Presented at the 18th
International Conference on Pattern Recognition (ICPR’06) (2006)
27. WAHBA, G.: A least-squares estimate of satellite attitude, Problem 65–1. SIAM Rev. 7 (1965)
28. Markley, F.L.: Attitude determination using vector observations and the singular value
decomposition. J. Astonaut. Sci. 36(3), 245–258 (1988)
29. Fernàndez, G., Kunt, M., Zrÿd, J.P.: A new plant cell image segmentation algorithm. In: Image
Analysis and Processing (Lecture Notes in Computer Science), pp. 229–234 (1995)
30. Indhumathi, C., Cai, Y.Y., Guan, Y.Q., Opas, M.: An automatic segmentation algorithm for 3D
cell cluster splitting using volumetric confocal images. J. Microsc. 243(1), 60–76 (2011)
31. Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion
simulations. IEEE Trans. Pattern Anal. Mach. Intell. 13(6), 583–598 (1991)
32. Gonzales, R.C., Woods, R.E., Eddis, S.L.: Digital Image Processing Using Matlab, 2nd ed.
McGraw Hill Education, India (2010)
33. Mackenzie, J.K., Thomson, M.J.: Some statistics associated with the random disorientation of
cubes. Biometrika 44(1–2), 205–210 (1957)
34. Vegelius, J., Janson, S., Johansson, F.: Measures of similarity between distributions. Qual.
Quant. 20(4) (1986)
35. Renkonen, O.: Statistisch-ökologische Untersuchungen über die terrestrische Käferwelt der
finnischen Bruchmoore. Dissertation, Societas zoologica-botanica Fennica Vanamo (1938)
36. Omar, H.M., Rohani, S.: Crystal population balance formulation and solution methods: a
review. Cryst. Growth Des. 17(7), 4028–4041 (2017)
37. Ramkrishna, D.: Population Balances. Academic Press, San Diego (2000)
38. Kroese, D.P., Taimre, T., Botev, Z.I.: Handbook of Monte Carlo Methods. Wiley, Hoboken
(2011)
39. Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipies in C++, 2
ed. Cambridge University Press, Cambridge (2007)
40. Snyder, R.C., Doherty, M.F.: Faceted crystal shape evolution during dissolution or growth.
AIChE J. 53(5), 1337–1348 (2007)
S. Schiele et al.
13. Singh, M.R., Verma, P., Tung, H.-H., Bordawekar, S., Ramkrishna, D.: Screening crystal
morphologies from crystal structure. Cryst. Growth Des. 13(4), 1390–1396 (2013)
14. Schneider, R.: Convex Bodies: The Brunn-Minkowski Theory. Cambridge University Press,
Cambridge (2008)
15. Al-Rousan, T., Masad, E., Tutumluer, E., Pan, T.: Evaluation of image analysis techniques for
quantifying aggregate shape characteristics. Constr. Build. Mater. 21(5), 978–990 (2007)
16. Hentschel, M.L., Page, N.W.: Selection of descriptors for particle shape characterization. Part.
Part. Syst. Charact. 20(1), 25–38 (2003)
17. Masad, E., Button, J.W.: Unified imaging approach for measuring aggregate angularity and
texture. Comput.-Aided Civil Infrastruct. Eng. 15(4), 273–280 (2000)
18. Pons, M.N., et al.: Particle morphology: from visualisation to measurement. Powder Technol.
103(1), 44–57 (1999)
19. Burger, W., Burge, M.J.: Principles of Digital Image Processing, Undergraduate Topics in
Computer Science (2009)
20. Schorsch, S., Ochsenbein, D.R., Vetter, T., Morari, M., Mazzotti, M.: High accuracy online
measurement of multidimensional particle size distributions during crystallization. Chem. Eng.
Sci. 105, 155–168 (2014)
21. Gahn, C., Mersmann, A.: Brittle fracture in crystallization processes Part A. Attrition and
abrasion of brittle solids. Chem. Eng. Sci. 54(9), 1273–1282 (1999)
22. Briesen, H.: Model-based analysis of the effect of particle and impact geometry on attrition of
brittle material. Powder Technol. 178(2), 87–98 (2007)
23. Briesen, H.: Two-dimensional population balance modeling for shape dependent crystal
attrition. Chem. Eng. Sci. 64(4), 661–672 (2009)
24. Gorski, K.M., et al.: HEALPix: a framework for high-resolution discretization and fast analysis
of data distributed on the sphere. Astrophys J 622(2), 759–771 (2005)
25. Naruse, Y.: HEALPix Library for Matlab. 1.0 ed (2011)
26. Neubeck, A., Van Gool, L.: Efficient non-maximum suppression. In: Presented at the 18th
International Conference on Pattern Recognition (ICPR’06) (2006)
27. WAHBA, G.: A least-squares estimate of satellite attitude, Problem 65–1. SIAM Rev. 7 (1965)
28. Markley, F.L.: Attitude determination using vector observations and the singular value
decomposition. J. Astonaut. Sci. 36(3), 245–258 (1988)
29. Fernàndez, G., Kunt, M., Zrÿd, J.P.: A new plant cell image segmentation algorithm. In: Image
Analysis and Processing (Lecture Notes in Computer Science), pp. 229–234 (1995)
30. Indhumathi, C., Cai, Y.Y., Guan, Y.Q., Opas, M.: An automatic segmentation algorithm for 3D
cell cluster splitting using volumetric confocal images. J. Microsc. 243(1), 60–76 (2011)
31. Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion
simulations. IEEE Trans. Pattern Anal. Mach. Intell. 13(6), 583–598 (1991)
32. Gonzales, R.C., Woods, R.E., Eddis, S.L.: Digital Image Processing Using Matlab, 2nd ed.
McGraw Hill Education, India (2010)
33. Mackenzie, J.K., Thomson, M.J.: Some statistics associated with the random disorientation of
cubes. Biometrika 44(1–2), 205–210 (1957)
34. Vegelius, J., Janson, S., Johansson, F.: Measures of similarity between distributions. Qual.
Quant. 20(4) (1986)
35. Renkonen, O.: Statistisch-ökologische Untersuchungen über die terrestrische Käferwelt der
finnischen Bruchmoore. Dissertation, Societas zoologica-botanica Fennica Vanamo (1938)
36. Omar, H.M., Rohani, S.: Crystal population balance formulation and solution methods: a
review. Cryst. Growth Des. 17(7), 4028–4041 (2017)
37. Ramkrishna, D.: Population Balances. Academic Press, San Diego (2000)
38. Kroese, D.P., Taimre, T., Botev, Z.I.: Handbook of Monte Carlo Methods. Wiley, Hoboken
(2011)
39. Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipies in C++, 2
ed. Cambridge University Press, Cambridge (2007)
40. Snyder, R.C., Doherty, M.F.: Faceted crystal shape evolution during dissolution or growth.
AIChE J. 53(5), 1337–1348 (2007)