Data Visualization: Principles and Practice, Second Edition by Alexandru C. Telea

By Alexandru C. Telea

Designing an entire visualization process consists of many refined judgements. while designing a posh, real-world visualization procedure, such judgements contain many sorts of constraints, similar to functionality, platform (in)dependence, to be had programming languages and kinds, user-interface toolkits, input/output info layout constraints, integration with third-party code, and extra.

Focusing on these thoughts and techniques with the broadest applicability throughout fields, the second one variation of Data Visualization: ideas and Practice presents a streamlined creation to numerous visualization suggestions. The ebook illustrates a large choice of purposes of knowledge visualizations, illustrating the variety of difficulties that may be tackled through such tools, and emphasizes the powerful connections among visualization and similar disciplines akin to imaging and special effects. It covers a variety of sub-topics in facts visualization: information illustration; visualization of scalar, vector, tensor, and volumetric facts; photo processing and area modeling innovations; and data visualization.

See What’s New within the moment Edition:

  • Additional visualization algorithms and techniques
  • New examples of mixed recommendations for diffusion tensor imaging (DTI) visualization, illustrative fiber tune rendering, and fiber bundling techniques
  • Additional recommendations for point-cloud reconstruction
  • Additional complicated picture segmentation algorithms
  • Several vital software program platforms and libraries

Algorithmic and software program layout matters are illustrated all through by means of (pseudo)code fragments written within the C++ programming language. routines overlaying the themes mentioned within the booklet, in addition to datasets and resource code, also are supplied as extra on-line resources.

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2 2 In our example, f (x, y) = e−(x +y ) , so the vector v = (−2xf, −2yf, 1) has the same direction as the normal, though not the same length. We can thus compute the normal n by normalizing v: n= − 2xf ,− 2yf + 4y 2 f 2 +1 4x2 f 2 + 4y 2 f 2 +1 , 1 . 4. 6). 4x2 f 2 4x2 f 2 Computing vertex normals. 4). 1, our function may not be specified analytically, but as a sampled dataset. , polygonal) datasets using a simple technique called normal averaging. PN all polygons that have pi as a vertex. We define the vertex normal ni at pi as the average of all polygon normals n(Pj ) that have pi as a 36 2.

When a polygon is rendered, its vertex texture coordinates are interpolated at every pixel, similarly to the color interpolation performed by Gouraud shading. The pixel is next rendered using a combination of the polygon color and texture color tex(s, t). Putting it all together, we can see the entire texture-mapping procedure as a function T that maps a pixel from the surface of the rendered object to another pixel in the texture image tex. 6 can be implemented in OpenGL by the following steps.

8. The height plot drawn half-transparently on top of the domain grid using a white background. glBlendFunc(GL SRC ALPHA,GL ONE MINUS SRC ALPHA) realizes the convex combination dst = sf ∗ src + (1 − sf ) ∗ dst. 10) The value of dst is always clamped to one by OpenGL, so it is a good idea to use blending factors that will sum up to one when all primitives are drawn. 10). Finally, there is a fourth constant GL ZERO, which sets the respective source or destination blending factor to zero. , the source color will simply overwrite the destination (frame buffer) color.

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