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Research Papers

Determining Probability of Importance of Features in a Sketch

[+] Author and Article Information
Ricardo Cruz-Lozano

Mem. ASME
Department of Mechanical Engineering,
Texas Tech University,
Lubbock, TX 49409
e-mail: ricardo.cruz-lozano@ttu.edu

Fisseha M. Alemayehu

Mem. ASME
School of Engineering,
Computer Science and Mathematics,
West Texas A&M University,
Canyon, TX 79016
e-mail: falemayehu@wtamu.edu

Stephen Ekwaro-Osire

Mem. ASME
Department of Mechanical Engineering,
Texas Tech University,
Lubbock, TX 79409
e-mail: stephen.ekwaro-osire@ttu.edu

Haileyesus B. Endeshaw

Mem. ASME
Department of Mechanical Engineering,
Texas Tech University,
Lubbock, TX 79409
e-mail: haile.endeshaw@ttu.edu

1Corresponding author.

Manuscript received January 28, 2016; final manuscript received January 28, 2017; published online June 13, 2017. Assoc. Editor: Jeremy M. Gernand.

ASME J. Risk Uncertainty Part B 3(4), 041003 (Jun 13, 2017) (13 pages) Paper No: RISK-16-1051; doi: 10.1115/1.4035867 History: Received January 28, 2016; Revised January 28, 2017

Sketches can be categorized as personal, shared, persuasive, and handover sketches. Depending on each category, their level of ambiguity also varies. The applications of sketches include conceptual design, eliciting user preferences, shape retrieval, and sketch-based modeling (SBM). There is a need for quantification of uncertainty in sketches in mapping of sketches to three-dimensional (3D) models in sketch-based modeling, in eliciting user preferences, and in tuning the level of uncertainty in sketches at the conceptual design stage. This paper investigates the role of probability of importance in quantifying the level of uncertainty in sketches by raising the following three research questions: How are the features in a sketch ranked? What is the probability of importance of features in a sketch? What is the level of uncertainty in a sketch? This paper presents an improved framework for uncertainty quantification in sketches. The framework is capable of identifying and ranking the features in the sketch, determining their probability of importance, and finally quantifying the level of uncertainty in the sketch. Ranking the features of a sketch is performed by a hierarchical approach, whereas probability of importance is determined by assessing the probability of likeliness using a shape matching approach and a probability transformation. Quantification of uncertainty is accomplished by using the principle of normalization of entropy. A case study of a bicycle sketch is used to demonstrate that the framework eliminates the need of expert input in assessment of uncertainty in sketches and, hence, can be used by design practitioners with limited experience.

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Figures

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Fig. 1

Improved framework for the quantification of uncertainty in sketches

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Fig. 2

Sketch of a bicycle

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Fig. 3

Identified features in the sketch of a bicycle: (a) mounting system, (b) steering system, (c) power system, and (d) rear wheel system

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Fig. 4

Determining standard images of bicycle components

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Fig. 5

Determining Li for the features of the sketch of a bicycle

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Fig. 6

Uncertainty as a function of the probability of importance value of the most important feature (p1)

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Fig. 7

Uncertainty as a function of p1 and the second feature ()% of residual probability for (a) three features, (b) four features, and (c) five features

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Fig. 8

Example of levels of uncertainty of sketches of a bicycle: (a) high uncertainty (u = 60.08%) and (b) low uncertainty (u = 10.95%)

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