The Visualization Pipeline
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The visualization pipeline consists of 4 main stages: 1) data acquisition, where data is produced or acquired, 2) data enhancement, where data is prepared or preprocessed, 3) visualization mapping, where data is mapped to geometric primitives, and 4) rendering, where geometric data is transformed into images. Common operations at each stage include measurement, filtering, mapping to points and colors, and projection from 3D to 2D. Visualization tools like VTK and VisTrails implement the pipeline to transform data into informative images.
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The Visualization Pipeline
- 1. The Visualization Pipeline Theo Paul Santana - 2018
- 2. Introduction The role of visualization is to create image that convey type of insights into a given process. The visualization process consists of the sequence of steps, or operations, that manipulate the data produced by the process under study and ultimately deliver the desired images. The visualization process can be seen as a pipeline, consisting of several stages, each modeled by a specific data transformation operation. The input data flows through this pipeline, being transformed in various ways, until it generate the output images. The sequence of data, take place in the visualization process is often called the visualization pipeline.
- 3. Visualization Pipeline Overview Data is transformed into images Rendering (3D or 2D) Data is mapped to geometric primitives Visualization Mapping Data is prepared or preprocessed Data is produced or acquired Data Acquisition Data Enhancement
- 4. 1. Data Acquisition (Data is produced or acquired) Data are prepared for visualization (e.g., by applying a smoothing filter, interpolating missing values, or correcting erroneous measurements) usually computer-centered, little or no user interaction. Data acquisition is the process of sampling signals that measure real world physical conditions and converting the resulting samples into digital numeric values that can be manipulated by a computer. ● Measurement, e.g., CT, MRI ● Written down, scanned in as text ● User input, into databases or spreadsheets ● Simulation, eg., Computational fluid dynamics simulation (CFD) ● Modeling, e.g, Computer Aided Design (CAD), dynamical system ● Videos or Images are recorded
- 5. 2. Data Enhancement (Data is prepared or preprocessed ) Datas are enhanced and prepared, selection of data portions to be visualized -- usually user-centered. Data enhancement is all about making sure any data that is coming into the business is being looked at with a critical eye and is being filtered down to maximize its value. ● Filtering, e.g. smoothing (noise filtering) ● Errors are discovered and corrected ● Missing values may be handled ● Resampling or modify grid representation ● Derive new data, eg., gradients ● Data interpolation
- 6. 3. Visualization Mapping (Data is mapped to geometric primitives) Focus data are mapped to geometric primitives (e.g., points, lines) and their attributes (e.g., color, position, size); most critical step for achieving Expressiveness and Effectiveness. Data is represented by geometric primitives: points, lines, triangles, polygons, cubes, shape, color, transparency. ● Compute isosurface ● Compute glyphs or icos ● Compute graph layout ● Compute voxel attributes: colors, transparency...
- 7. 4. Rendering (3D or 2D) (Data is transformed into images) Geometric data are transformed to image data. Rendering or image synthesis is the automatic process of generating a photorealistic or non-photorealistic image from a 2D or 3D model by means of computer programs ● Projection (3D > 2D) ● Visibility Calculation ● Shading ● Compositing (Accumulate Transparency and colors values) ● Animation
- 8. Dataflow Programming with VTK VTK (Visualization Toolkit) its current status as a one of the most popular visualization packages for researchers. The Visualization Toolkit (VTK) is an open- source, freely available software system for 3D computer graphics, image processing and visualization. VTK consists of a C++ class library and several interpreted interface layers including Tcl/Tk, Java, and Python.
- 9. Dataflow Programming with VisTrails VisTrails is another software for visualization packages for researchers. VisTrails is a scientific workflow management system developed at the Scientific Computing and Imaging Institute at the University of Utah that provides support for data exploration and visualization. It is written in Python and employs Qt via PyQt bindings.
- 10. CONCLUSION Application can separate and structures the pipeline in different ways, depending on the design and implementation consideration that go beyond. In this presentation I showed the 4 main ingredients, so visualization pipeline is described from both conceptual and implementation of view. There is no clear cut separation of the visualization stage. 1. Data Acquisition or Importing 2. Data Enhancement and Filtering 3. Mapping 4. Rendering
- 11. Reference and Further Reading Interactive Data Visualization: Foundations, Techniques and Applications, Second Edition by Matthew O. Ward, Georges Grinstein, Daniel Kleim, AK Peters/CRC Press, 2015 Data Visualization Principles and Practice, Second Edition by Alexandru Telea, AK Peters/CRC Press, 2015 Information Visualization: Perception for Design, Third Edition by Colin Ware, Morgan Kaufmann Publishers, 2013
- 12. THANKS Theo Paul Santana / 张飞 Mobile: +86 13611996578 E-Mail: theops2@gmail.com Internet: http://www.theosantana.com
Editor's Notes
- Data Acquisition: Datas people producing data Data Enhancement: Modification of the data for the next stage of the pipeline, removes values or add new values Visualization mapping: When the datas are mapped to shapes, Rendering: Computer graphics, you guys should be expert on it, since a lot of students has design background.
- Importing Data First, we have to import the data. This implies finding a representation of the original information we want to investigate in terms of a data set, be it continuous or discrete. Practically, importing data means choosing a specific dataset implementation and converting the original information to the representation implied by the chosen dataset. Ideally, this is a one-to-one mapping or data copying. It is important to realize that the choices made during data importing determine the quality of the resulting images, and thus the effectiveness of the visualization. For example, changing the underlying grid structure from quads to triangles changes the interpolation method which for some visualization algorithm changes the resulting image. For this reason, the data importing step should try to preserve as much of the available input information as possible, and make as few assumptions as possible about what is important and what is not.
- Imagine getting a set of numbers tossed at you without a clue as to what they mean and what they are going to do for you. Pre processing, change or modify in some way to prepare for visualization, noise is elimte from data, sometimes they can find error e discovery and eliminate it, missing values, you need to decide the missing values, you need to modify the data representation, you need to drive new data, min and max statistics, data interpolation add values between other values. We have to decide which are the data’s important aspects, or features, we are interested in. In most cases the imported data is not one-to-one with the aspects we want to get insight into. We must distill our raw data sets into more appropriate representations, also called enriched datasets, which encode our features of interest. This process is called data filtering or data enriching. On the one hand, data is filtered to extract relevant information. On the other hand, data is enriched with higher-level information that supports a given task.
- After we need the data enhacement we need to transfer the data in shapes and colors, we need use the terminology as geometrics primitives, as points, lines, triangles, polygons we get the raw data and put on shapes. We have some complicate of algorms, they are ways but a bit complicate with the compute. The filtering operation produces an enriched dataset that should directly represent the features of interest for a specific exploration task. Once we have this representation, we must map it to the visual domain. We do this by associating elements of the visual domain with the data elements present in the enriched dataset. This step of the visualization process is called mapping. The visual domain is a multidimensional space whose axes, or dimensions are those elements we perceive as quasiindependent visual attributes, such as shape, position, size, color, texture, illumination, and motion. Typically, a visual feature is a colored, shaded, textured, or animated 2D or 3D shape. Data mapping is probably the operation in the visualization pipeline that is most characteristic for the visualization process as it influences the resulting image more than any other step. There are many different mapping techniques the visualization can be based on, which we will illustrate in the following chapters by introducing various visualization algorithms.
- Which ones are visible or which ones are not, shading is a kind of classic computer graphic topic and alot of people do that, compositing is another rend computer technic and animation as well, those are cool on computer graphic topics, if you have chance to take computer graphic class I recommend because is fun. The rendering operation is the final step of the visualization process. Rendering takes the 3D scene created by the mapping operation, together with several user-specified viewing parameters such as the viewpoint and lighting, and renders it to produce the desired images. In typical visualization applications, viewing parameters are considered part of the rendering operation. This allows users to interactively navigate and examine the rendered result of a given visualization. Indeed if the viewpoint changes but the 3D scene produced by the mapping stays the same, all we have to do is render the scene anew with the new viewing parameters, which is a relatively cheap operation.
- The visualization pipeline offers an intuitive architectural model to design complex data processing and data visualization application combining lowe level functionalituy so called dataflow graph.
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