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Hello! I'm writing an article but a very insecure about my english. Can anyone correct this text for me? I'll be extremely grateful!
Unfortunately it is a scientific and very specific text . Sorry..
If is possible, i would like to receive not just the correction, but some tips of how to use better the english to write, i.e., ordinary construction that i falied.
In tensor field visualization we can adopt different approaches to represent the visual information generates by the field. The discrete approach is commonly used when punctual data is sufficient to obtain the required information. A superquadric glyph is an ordinary fashion to represent local information given by the field mapping into geometric primitives, like cubes, ellipses and cylinders this information. Using the concept of glyphs, SHAW, et al, have developed their work for multidimensional generic data visualization. The main contribution of their work is to connect the advantages of the visual human being perception and superquadrics intrinsic interpolation feature. In a extension of their work (SHAW et al 3), they look to measure how many forms assumed by superquadric people can identify. Later, KINDLMANN et al (4) have used glyphs to visualize tensor fields specifically. WESTIN et al proposed a anisotropic metric to identify and compare a set of glyphs. Is presented the problem of cuboidal glyphs, that can induce a wrong analyses when the glyphs adopt planar or linear forms. In one hand, all problems that involves symmetry can be solved using ellipsoidal glyphs. In other hand, is notice ambiguity situations in the visual identification of the tensor. If the point of view direction is aligned to the main eigenvector, ellipsoidal linear tensors can be identified as spheres. Thus, in their work was presented a method to solve the problems of anisotropic and ambiguity in tensor field visualization using adaptive glyphs: depending the point of view, the tensor assume different forms, maintaining the original basic geometry.
We can find in literature other approaches to understanding tensor field data visually. Instead of generation of spread informations, we can adopt continuous methods. In order to smooth the tensor information among two points in a multidimensional space, this methods presents interesting results. In DELMARCELLE et al. the concept of tensor field lines - extended from DICKSON - is generalized, and the concept of hyperstreamlines is presented. In that work they represent all information of a tensor field taking into account not points, but the trajectory generated by the tensor using the eigenvectors. This approach is interesting to visualize symmetric tensor fields, where its eigenvectors are real and orthogonal. However, the field becomes hard to visualize for a large number of hypersetreamlines. DELMARCELEE et al. (8) has presented another problem in hypersetreamlines use: the degeneration. If the tensor has at least two equal eigenvectors, this problem is detected. In WEINSTEIN et al. is presented a method to avoid degeneration due to planar and spherical tensors in input data. In their work was used tensors from magnetic resonance image (Diffusion Tensors - Magnetic Resonance Image).