CHAPTER 4 THE VISUALIZATION PIPELINE
CHAPTER 4 THE VISUALIZATION PIPELINE
CONTENTS The focus is on presenting the structure of a complete visualization application, both from a conceptual and a practical perspective 1 Conceptual perspective Implementation considerations Algorithms used in the visualization Structure of the visualization applications
CONTENTS The focus is on presenting the structure of a complete visualization application, both from a conceptual and a practical perspective. 4.1 Conceptual perspective 4.2 Implementation considerations 4.3 Algorithms used in the visualization 4.4 Structure of the visualization applications
4.1 CONCEPTUAL PERSPECTIVE imported enriched 2D/3D final raw data dataset dataset shape mage f(xy)-+R3 0.2.-5. import data acquisition data enriching, map abstract draw visual transformation data to visual representations resampling representations measuring deyice or Lend user i simulation insight into the original phenomenon Fig 4.1 The visualization pipeline COmputatio the cycle above in real time)
4.1 CONCEPTUAL PERSPECTIVE Fig 4.1 The visualization pipeline (Computational Steering: the cycle above in real time)
4.1 CONCEPTUAL PERSPECTIVE Four Visualization Stages data importing: data filtering and enrichment; data mapping; data rendering -unction mappin:Vs.Di->〗n Vis: function mapping Di the set of all possible types of raw input data n: the set of produced images Reverse function mapping nsight:丌->Di
4.1 CONCEPTUAL PERSPECTIVE • Four Visualization Stages: data importing; data filtering and enrichment; data mapping; data rendering • Function mapping: Vis: Di --> Л Vis: function mapping Di : the set of all possible types of raw input data Л : the set of produced images Reverse function mapping: Insight: Л --> Di
4.1 CONCEPTUAL PERSPECTIVE mport Filter Map Render D D D Insignt Fig 4. 2 The visualization process seen as a composition of functions
4.1 CONCEPTUAL PERSPECTIVE Fig 4.2 The visualization process seen as a composition of functions
4.1.1 DATA Finding a representation of the original information D,. the raw information D the set of all supported datasets of a given visualization process In practice, data importing can imply translating between different data storage Formats Or resampling the data from the continuous to the discrete domain The data importing step should try to preserve as much of the available Input information as possible Make as few assumption as possible about what is important and what is not
4.1.1 IMPORTING DATA • Finding a representation of the original information • DI : the raw information • D : the set of all supported datasets of a given visualization process • In practice, data importing can imply translating between different data storage formats • Or resampling the data from the continuous to the discrete domain • The data importing step should try to preserve as much of the available input information as possible • Make as few assumption as possible about what is important and what is not Import : D D I →
4.1.1 DATA Finding a representation of the original information resampling the data from the continuous to the discrete domain E.G. Petroleum seismic data seismic reflection wave = digital sampling data
4.1.1 IMPORTING DATA • Finding a representation of the original information -- resampling the data from the continuous to the discrete domain E.G. Petroleum seismic data -- seismic reflection wave => digital sampling data
4.1.2 DATA AND Decide We must somehow turn our raw dataset into more appropriate representations---enriched datasets Data filtering or data enrich ning two tasks Extract relevant information Enriched with hig -Ei e! information that supports a given task The input and output are datasets
4.1.2 DATA FILTERINGAND ENRICHMENT • Decide important aspects or features. • We must somehow turn our raw dataset into more appropriate representations---enriched datasets • Data filtering or data enriching, two tasks • Extract relevant information • Enriched with high-level information that supports a given task • The input and output are datasets Filter : D D →
4.1.2 DATA FILTERING AND ENRICHMENT Petroleum seismic data wave correction; denoising Medical data noise data removal: enhancement of certain material data. etc
4.1.2 DATA FILTERING AND ENRICHMENT • Petroleum seismic data -- wave correction; denoising • Medical data -- noise data removal; enhancement of certain material data, etc
4.1.3 DATA Once we have the needed data, we must map it to the visual domain ° Mapping function D dataset Dy: dataset of visual features Comparison about mapping and rendering Mapping: convert invisible"to visible" representations; Rendering: simulates the physical process of lighting a visible 3D scene
4.1.3 MAPPING DATA • Once we have the needed data, we must map it to the visual domain. • Mapping function D: dataset Dv: dataset of visual features • Comparison about mapping and rendering • Mapping: convert “invisible” to “visible” representations; • Rendering: simulates the physical process of lighting a “visible” 3D scene. Map : D D → V
