Implicit polynomials, orthogonal distance regression, and the closest point on a curve

Redding, N.J.

doi:10.1109/34.825757

Cited by 17 publications

(8 citation statements)

References 23 publications

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“…6(a), it can be Euclidean in the translational plane). minðA tc ff 1 g; A tc ff 2 g; A tc ff 3 gÞ is therefore equal to minðdstðf i ; E ði À 1Þði þ 1Þ ÞÞ which is shown in expression (6).…”

Section: Quantifying the Optimization Target With Margin Approximationmentioning

confidence: 95%

“…There are lots of strategies to fulfilling the two items. In this paper, we propose to combine optimal translational margins in expression (6) with the least maximum inter-finger distance in expression (7) for approximation:…”

Section: Quantifying the Optimization Target With Margin Approximationmentioning

confidence: 99%

“…With the point clouds collected from depth sensors, researchers usually employ segment fitting: such as Principle Component Analysis (PCA) plus General Hough Transform (GHT) [4] or Iterative Closest Point (ICP) [5], or curve fitting: such as Implicit Polynomials (IP) curves [6], to model target objects and build up the screw systems for grasping synthesis [7,8]. One problem of these fitted geometric models is that they are unsafe for screw extraction since a small noise may result into significant error, especially in the case of small friction force and small number of fingers.…”

Section: Introductionmentioning

confidence: 99%

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Finger-position optimization by using caging qualities

Wan

Fukui

2016

Signal Processing

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Section: Quantifying the Optimization Target With Margin Approximationmentioning

confidence: 95%

Section: Quantifying the Optimization Target With Margin Approximationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Finger-position optimization by using caging qualities

Wan

Fukui

2016

Signal Processing

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“…(Bézier curves are described by third degree polynomials, hence computing the minimum distance from an arbitrary point to the curve involves minimizing a sixth degree polynomial, equivalent to solving a fifth degree polynomial.) A numerical solution is both computationally expensive and heavily dependent on the goodness of the initial guesses for roots [12], hence we resort to an approximation. We discretize the Bézier curve using a piecewise linear curve and compute the error for that curve.…”

Section: Handling Curvesmentioning

confidence: 99%

Sketch based interfaces

Sezgin

Stahovich²,

Davis

2007

ACM SIGGRAPH 2007 Courses

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Freehand sketching is a natural and crucial part of everyday human interaction, yet is almost totally unsupported by current user interfaces. We are working to combine the flexibility and ease of use of paper and pencil with the processing power of a computer, to produce a user interface for design that feels as natural as paper, yet is considerably smarter. One of the most basic steps in accomplishing this is converting the original digitized pen strokes in a sketch into the intended geometric objects. In this paper we describe an implemented system that combines multiple sources of knowledge to provide robust early processing for freehand sketching.

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“…Intrinsically, the pre-processing procedures, e.g. curve or polygon fitting [3], comprise an error source. Further, the problem grows more complicated as materials of target surfaces, target internal properties, or possible external force sets are taken into account [4] [5].…”

Section: Introductionmentioning

confidence: 99%

From analysis to practice: Three-finger caging of planar convex objects

Wan

Fukui

Shimosaka

et al. 2011

2011 IEEE/SICE International Symposium on System Integration (SII)

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This paper presents the development of planar caging manipulation. It involves a preliminary conclusion where the targets are limited to convex objects and the finger number is limited to three. Despite the popularity of form or force closure analysis, we prefer caging as it owns merits like requiring little dynamics, reducing kinematics and affording robust breaking margins to tolerate control errors. The analysis part of this paper theoretically discusses optimization procedures that best exploits the merits from caging. The practice part presents implementation details of our proposal in real work space by employing KINECT, a low-cost depth image capture produced by PrimeSense. Especially, some artifice and strategies are discussed and compared in this part to fulfill application requirements. Experimental results show the efficacy of our analysis and its promising future.

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Implicit polynomials, orthogonal distance regression, and the closest point on a curve

Cited by 17 publications

References 23 publications

Finger-position optimization by using caging qualities

Finger-position optimization by using caging qualities

Sketch based interfaces

From analysis to practice: Three-finger caging of planar convex objects

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