SpeedSeg: A technique for segmenting pen strokes using pen speed

Herold, James; Stahovich, Thomas F.

doi:10.1016/j.cag.2010.12.003

Cited by 22 publications

(19 citation statements)

References 26 publications

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“…RankFrag has only been tested for finding corner points and not tangent vertices, as done by other techniques [12,1]. It can be directly used in various structural methods for symbol recognition.…”

Section: Discussionmentioning

confidence: 99%

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RankFrag: A Machine Learning-Based Technique for Finding Corners in Hand-Drawn Digital Curves

Costagliola

Rosa

Fuccella

2015

International Conferences on Distributed Multimedia Systems

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We describe RankFrag: a technique which uses machine learning to detect corner points in hand-drawn digital curves. RankFrag classifies the stroke points by iteratively extracting them from a list of corner candidates. The points extracted in the last iterations are said to have a higher rank and are more likely to be corners. The technique has been tested on three different datasets described in the literature. We observed that, considering both accuracy and efficiency, RankFrag performs better than other state-of-art techniques.

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Section: Discussionmentioning

confidence: 99%

“…SpeedSeg [12] and TCVD [1] are examples of such methods. TCVD is also able to find both the corners and the points where there is a significant change in curvature (referred to as "tangent vertices" in [1]).…”

Section: Related Workmentioning

confidence: 99%

RankFrag: A Machine Learning-Based Technique for Finding Corners in Hand-Drawn Digital Curves

Costagliola

Rosa

Fuccella

2015

International Conferences on Distributed Multimedia Systems

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“…9 In contrast, our method finds piecewise approximations via dynamic programming combined with an adaptive cost function.…”

Section: Piecewise Approximationmentioning

confidence: 99%

DPFrag: Trainable Stroke Fragmentation Based on Dynamic Programming

Tumen

Sezgin

2013

IEEE Comput. Grap. Appl.

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Many computer graphics applications must fragment freehand curves into sets of prespecified geometric primitives. For example, sketch recognition typically converts hand-drawn strokes into line and arc segments and then combines these primitives into meaningful symbols for recognizing drawings. However, current fragmentation methods' shortcomings make them impractical. For example, they require manual tuning, require excessive computational resources, or produce suboptimal solutions that rely on local decisions. DPFrag is an efficient, globally optimal fragmentation method that learns segmentation parameters from data and produces fragmentations by combining primitive recognizers in a dynamic-programming framework. The fragmentation is fast and doesn't require laborious and tedious parameter tuning. In experiments, it beat state-of-the-art methods on standard databases with only a handful of labeled examples.

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“…Therefore, the first step towards the development of an interactive natural user interface is to minimize the uncertainty in the captured motion through segmentation. Threshold based algorithms are the most common approach in segmentation which is applied to some motion features such as curvature or speed [3][4].…”

Section: Introductionmentioning

confidence: 99%

“…Although the problem of sketch processing in this approach can be solved all in one stage, this method is computationally expensive and therefore not suitable for realtime applications. This problem can be overcome by employing dynamic programming [12,13] or using hybrid methods where fragmentation and segmentations are combined [4], [14]. Usually hybrid approaches are constist of two phases in which segmention is performed at first and then fragmentation is applied to the segments.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Segmentation for Air Gestures Identification

Ghobadi

Esfahani

2014

Volume 1B: 34th Computers and Information in Engineering Conference

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Having a user-friendly Human-CAD interaction with high speed and accuracy plays a key role in development of future intelligent modeling environments. A major part of this puzzle is sketch identification using either 2D gestures — which is commonly recorded from mouse, light pen and touchpad — or air gestures captured from some newly emerged devices such as Leap Motion and Soft-Kinect. To this end, we present a leaning based technique for segmentation of air gestures. The proposed technique can detect the separation points of any single-stroke air gesture using specific motion features such as speed, curvature and center of curvature. Two types of separation points are considered: 1) rough separation points or simply corner points and 2) soft separation points such as inflection points. The segmentation is performed in two steps: Support Vector Machine (SVM) is used to adaptively differentiate the corner points from regular points. A soft segmentation method is then implemented to further break the rough segments into a set of smaller arcs and lines based on sudden change in the center of curvature. The experimental validation shows robust performance of the proposed method and low computation expenses.

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SpeedSeg: A technique for segmenting pen strokes using pen speed

Cited by 22 publications

References 26 publications

RankFrag: A Machine Learning-Based Technique for Finding Corners in Hand-Drawn Digital Curves

RankFrag: A Machine Learning-Based Technique for Finding Corners in Hand-Drawn Digital Curves

DPFrag: Trainable Stroke Fragmentation Based on Dynamic Programming

Adaptive Segmentation for Air Gestures Identification

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