Automatic Transfer Functions Based on Informational Divergence

Abstract

Fig. 1. Volume renderings of the tooth data set using transfer functions obtained with different target distributions. From left to right, the target distributions used are occurrence weighted by intensity, occurrence weighted by importance (1 for enamel and 0.5 for the rest), occurrence weighted by gradient, and occurrence weighted by importance using a mask of the nerve.Abstract-In this paper we present a framework to define transfer functions from a target distribution provided by the user. A target distribution can reflect the data importance, or highly relevant data value interval, or spatial segmentation. Our approach is based on a communication channel between a set of viewpoints and a set of bins of a volume data set, and it supports 1D as well as 2D transfer functions including the gradient information. The transfer functions are obtained by minimizing the informational divergence or Kullback-Leibler distance between the visibility distribution captured by the viewpoints and a target distribution selected by the user. The use of the derivative of the informational divergence allows for a fast optimization process. Different target distributions for 1D and 2D transfer functions are analyzed together with importance-driven and view-based techniques.

An Affordance-Based Framework for Human Computation and Human-Computer Collaboration

“…In presenting a preliminary framework for describing and comparing systems involving human and machine collaborators, we aspire to lay the foundation for a more rigorous analysis of the tools and approaches presented by our field, thereby paving the way for the construction of an increasingly robust understanding of analytical reasoning and how to best support insight generation. [41] CrowdSearch [80] ParallelTopics [23] Dissimilarity [50] VH+ML [28] Implicit tagging [62] reCAPTCHA [77] VizWiz [10] Phetch [74] ESP Game [73] KissKissBan [33] LabelMe [59] Ka-captcha [21] PeekABoom [76] MRI [12] iView [83] iVisClassifier [18] Saliency [37] RP Explorer [3] DimStiller [36] WireVis [46] Action trails [65] NetClinic [47] Trajectories [4] Risk assessment [51] Automatic transfer functions [57] MDX [66] Automated+viz [68] CzSaw [39] Fold.it [20] HRI scripts [17] Animated agents for VR [56] VA Model-learning [29] EyeSpy [6] MonoTrans2 [35] CastingWords [16] Click2Annotate [15] Wrangler [40] Soylent [8] Crowdsourced solutions [67] Crowdsourced design [81] Stress OutSourced …”

“…2a), computational methods for manipulating large datasets have been used to help users navigate and make sense of massive text corpora [23]. It has also been utilized to refine classification models and performing dimension reduction [18,29,51], interactively cluster data [4], and automatically extract transfer functions from user-selected data [57]. It has been used to suggest informative data views [83], and even to help users externalize and understand their own insight generation process [15,39,41,46,65].…”

“…The study of general human-computer collaboration offers a plethora of examples of successful human/machine teams [15,23,37,39,41,46,50,65,66,68,83]. Developments in supervised machine learning in the visualization community present several vetted techniques for human intervention into computationally complex tasks [3,4,12,18,28,29,36,47,51,57]. The emerging field of human computation inverts the traditional paradigm of machines providing computational support for problems that humans find challenging, and demonstrates success using aggregated human processing power facilitated by machines to perform difficult computational tasks such as image labeling [21,33,73,74], annotating audio clips [44,49], and even folding proteins [20].…”

Visibility-difference entropy for automatic transfer function generation

“…Moreover, the user has to provide an initial transfer function requiring previous knowledge, which is then optimized in terms of the visibility and the error to the initial transfer function. Incorporating informational divergence 19 is a way to fit the visibility distribution to a user defined target distribution. Again the user must choose a target distribution beforehand, requiring previous knowledge.…”

“…Figure 9 highlights the difference of our method to the most recent approaches. 18,19 The approach from Ruiz et al 19 requires the user to specify a target distribution first. Even though they do not describe how they do it for the tooth dataset their respective results look similar to the image in Figure 9(b).…”