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research-article

Determining Probability of Importance of Features in a Sketch

[+] Author and Article Information
Ricardo Cruz-Lozano

Department of Mechanical Engineering Texas Tech University Lubbock, Texas, USA
ricardo.cruz-lozano@ttu.edu

Fisseha M. Alemayehu

School of Engineering, Computer Science and Mathematics West Texas A&M University Canyon, Texas, USA
falemayehu@wtamu.edu

Stephen Ekwaro-Osire

Department of Mechanical Engineering Texas Tech University Lubbock, Texas, USA
stephen.ekwaro-osire@ttu.edu

Haileyesus B. Endeshaw

Department of Mechanical Engineering Texas Tech University Lubbock, Texas, USA
haile.endeshaw@ttu.edu

1Corresponding author.

ASME doi:10.1115/1.4035867 History: Received January 28, 2016; Revised January 28, 2017

Abstract

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 includes conceptual design, eliciting user preferences, shape retrieval, and sketch-based modeling. There is a need for quantification of uncertainty in sketches in mapping of sketches to 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.

Copyright (c) 2017 by ASME
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