2013
DOI: 10.1155/2013/732457
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Drawable Region of the Generalized Log Aesthetic Curves

Abstract: The main characteristic of visually pleasing curves used for product design is a monotonic curvature profile. Recently, a planar curve called Generalized Log Aesthetic Curve (GLAC) has been extended from the Log Aesthetic Curve (LAC), and it has an additional shape parameter,ν. This curve preserves the monotonicity of curvature and is said to produce visually pleasing curves. This paper delves on the drawable region of the GLAC segment which indicates the probable solutions of shape parameters from given inter… Show more

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Cited by 12 publications
(12 citation statements)
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“…3. To note, the constraints are easily satisfied during the parameter searching and the existence of solutions is proven to be far wider than the LA curves [7]. Although the final end curvatures change due to scaling, the G 2 continuity is achieved successfully.…”
Section: Output: As Shape or C-shape Of Glac Segment Beginmentioning
confidence: 97%
See 2 more Smart Citations
“…3. To note, the constraints are easily satisfied during the parameter searching and the existence of solutions is proven to be far wider than the LA curves [7]. Although the final end curvatures change due to scaling, the G 2 continuity is achieved successfully.…”
Section: Output: As Shape or C-shape Of Glac Segment Beginmentioning
confidence: 97%
“…The interactive design of GLAC satisfying G 1 Hermite data has been proposed by Gobithaasan et al [7] where its drawable region has been proven to be much wider than the leading LA curves.…”
Section: G 2 Continuous Glacmentioning
confidence: 99%
See 1 more Smart Citation
“…In order to indicate its robustness in Computer Aided Design applications, Gobithaasan et al (2013) showed that the Generalized Log Aesthetic Curve has wider drawable region compared to Log Aesthetic Curve. However, the Logarithmic Curvature Graph gradient of GLAC is in the form of a nonlinear function as follows: Λ 1 1 whereas LAC has LCG gradient as .…”
Section: Research Highlightmentioning
confidence: 99%
“…In 2012, Ziatdinov et al [17] presented an analytic solution of LAC in terms of Incomplete Gamma Function which reduced the computation time up to 13 times. Recent progresses include shape analysis on GLAC [18], its drawable region which is wider than LAC [19] and the implementation of G2 LAC as plug-in for Rhino CAD system [20]. This paper proposes a numerical method which is called classical Runge-Kutta method to compute LAC.…”
Section: Introductionmentioning
confidence: 99%