A method for modeling freehand curves—The fuzzy spline interpolation

Saga, Sato; Makino, Hiromi; Sasaki, Juni-Ichi

doi:10.1002/scj.4690261008

Cited by 15 publications

(9 citation statements)

References 2 publications

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“…13. The fuzzy spline curves in these figures are internal representation models of freehand curves [6]. Each curve represents a freehand plot of the locus of a conical fuzzy point having ambiguity that is proportional to the roughness of the drawing manner.…”

Section: Methodsmentioning

confidence: 99%

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Structural freehand pattern identification based on drawing manner identification

Tsujii

Tanaka

Saga

2001

Systems & Computers in Japan

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SUMMARYThis paper proposes a structural freehand pattern identification method for the handwritten input interface of a CAD system. In this method, a handwritten curve is described as a fuzzy symbol by recognizing it as a geometrical curve primitive, then structural matching is applied to the symbol. The system consists of two parts: the fuzzy spline curve identification (FSCI) method and the fuzzy attribute graph (FAG) method. The system uses a hierarchical approach which aims at the construction of a generalpurpose kind of freehand pattern identification. This paper includes the results of experiments on the basic performance of the method. © 2001 Scripta Technica, Syst Comp Jpn, 32(6): 4554, 2001

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Section: Methodsmentioning

confidence: 99%

“…Fig. 4), with the diameter of its base circle rp k indicating the ambiguity [6]. Although a circular fuzzy model is discretely represented by a conical fuzzy point in Fig.…”

Section: Fscimentioning

confidence: 99%

Structural freehand pattern identification based on drawing manner identification

Tsujii

Tanaka

Saga

2001

Systems & Computers in Japan

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“…Fourth, the FSCI divides the fuzzy spline curve into one or more segments by detecting stop motions in the drawing using the algorithm proposed in Ref. 16). …”

Section: Identification Process Of Fscimentioning

confidence: 99%

Snapping of Fuzzy Objects Using the Multi-Resolution Fuzzy Grid Snapping Technique

Dematapitiya

Kawazoe

Nishikawa

et al. 2009

Journal of Information Processing

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In the present paper, we propose an automatic snapping method that aligns fuzzy objects in a multi-resolution grid system in order to improve the efficiency of sketch-based CAD systems. The sketch-based CAD system that we have previously realized successfully identifies sketch drawings as primitive geometrical curve objects by treating the sketches as fuzzy objects, the fuzziness of which is associated with the roughness of the drawing manner. However, when the system aligns the identified objects with a grid system, difficulties in the grid resolution setting arise because the identified objects often consist of both fine and coarse portions and thus require different grid resolution settings for proper alignment. Meanwhile, the resolution problem with respect to cursor point snapping has been solved by multi-resolution fuzzy grid snapping (MFGS), which realizes automatic selection of the snapping resolution by treating the cursor as a fuzzy point, the fuzziness of which is associated with the roughness of the pointing manner of the user. The present paper proposes a method to apply MFGS to fuzzy objects in order to resolve the difficulties involved in the setting of the snapping resolution of the sketch-based CAD system. Experimental results show that users can align identified objects to an appropriate resolution through MFGS by controlling the roughness of the drawing manner.

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“…We previously proposed the fuzzy spline curve identifier (FSCI) [1,2], which identifies a freehand-input curve as one of seven classes of geometrical curve primitive needed in CAD: linear segment (L), circle (C), circular arc (CA), ellipse (E), elliptic arc (EA), closed freehand curve (FC), and open freehand curve (FO). We have already shown that by using FSCI as the figure recognition engine, freehand input CAD interface [3][4][5][6] and a structural freehand figure recognition system [7] can be transparently constructed.…”

Section: Introductionmentioning

confidence: 99%

Improvement of identification algorithms in the freehand curve identifier FSCI

Takikawa

Yasufuku

Saga

2005

Electron Comm Jpn Pt II

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SUMMARYWe already proposed the freehand curve identifier FSCI, based on fuzzy theory, as a freehand figure recognition engine. We next investigated the improvement of the recognition rate of FSCI by learning optimization of parameters. This paper proposes improvements of the FSCI algorithm itself, namely, improvement of the hypothetical fuzzy model formulation, and introduction of necessity into the fuzzy inference rules. It was shown by experiments that these improvements have effects which cannot be obtained by conventional parameter adjustment.

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A method for modeling freehand curves—The fuzzy spline interpolation

Cited by 15 publications

References 2 publications

Structural freehand pattern identification based on drawing manner identification

Structural freehand pattern identification based on drawing manner identification

Snapping of Fuzzy Objects Using the Multi-Resolution Fuzzy Grid Snapping Technique

Improvement of identification algorithms in the freehand curve identifier FSCI

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