An Introduction to Verification of Visualization Techniques by Tiago Etiene, Robert M. Kirby, Cláudio T. Silva

By Tiago Etiene, Robert M. Kirby, Cláudio T. Silva

As we elevate our reliance on computer-generated info, usually utilizing it as a part of our decision-making procedure, we needs to devise instruments to evaluate the correctness of that info. ponder, for instance, software program embedded on cars, used for simulating plane functionality, or utilized in clinical imaging. In these instances, software program correctness is of paramount value as there is little room for blunders. software program verification is likely one of the instruments to be had to achieve such pursuits. Verification is a well-known and extensively studied subfield of computing device technological know-how and computational technological know-how and the target is to assist us elevate self assurance within the software program implementation through verifying that the software program does what it truly is purported to do. The aim of this booklet is to introduce the reader to software program verification within the context of visualization. within the comparable approach we grew to become extra depending on advertisement software program, we've got additionally elevated our reliance on visualization software program. the reason being uncomplicated: visualization is the lens during which clients can comprehend complicated info, and as such it needs to be confirmed. The explosion in our skill to acquire info calls for instruments not just to shop and examine facts, but additionally to imagine it. This e-book is created from six chapters. After an advent to the objectives of the e-book, we current a short description of either worlds of visualization (Chapter 2) and verification (Chapter 3). We then continue to demonstrate the most steps of the verification pipeline for visualisation algorithms. We concentrate on vintage quantity visualization innovations, specifically, Isosurface Extraction (Chapter four) and Direct quantity Rendering (Chapter 5). We clarify how one can confirm implementations of these thoughts and document the newest leads to the sector of verification of visualization thoughts. The final bankruptcy concludes the e-book and highlights new examine subject matters for the long run.

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Extra info for An Introduction to Verification of Visualization Techniques (Synthesis Lectures on Visual Computing)

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If they are, we gain further confidence that our scheme has been properly implemented. 1. ). Once we have decided the numerical schemes we will employ, we then implement them in computer code. In this case, we have provided the Matlab code. 2. e red circles denote the errors in the approximation using Riemann integration, and the blue circles denote the error using the trapezoidal rule. is example serves to demonstrate two points. First, it shows us how we can use characteristics of the numerical methods we have chosen to give us some criterion against which to verify our code.

For instance, the VolVis project [35] is a rich source of volume data for testing volume visualization techniques. Another is the “Marschner-Lobb dataset,” developed by Marschner and Lobb [32] and used to compare several reconstruction filters. 9) has also become a standard in volume visualization. In the next chapter, we explain an alternative method for verifying isosurface extraction algorithms that can be used along with other techniques. 19 CHAPTER 3 Validation and Verification in Simulation Science In this chapter, we seek to highlight some places one can recognize validation and verification (V&V) in the simulation science process, and to help glean from these examples some of the general principles of V&V.

4) must be solved numerically. 2). 2. 2: Four steps of the volume ray casting algorithm: (1) ray casting, (2) ray sampling, (3) shading, and (4) compositing. Source: Wikimedia Commons [56]. pixel position directed toward the volume is discretized into sample points; the sample points are then evaluated according to transfer functions, and, lastly, combined into one value via numerical integration. id /, where i is the i -th sample in the ray, and d D D=n, where D is the length of the ray and n is the total number of sample points.

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