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Template-based sketch recognition using Hidden Markov Models Flor, Roey
Abstract
Sketch recognition is the process by which the objects in a hand-drawn diagram can be recognized and identified. We provide a method to recognize objects in sketches by casting the problem in terms of searching for known 2D template shapes in the sketch. The template is defined as an ordered polyline and the recognition requires searching for a similarly-shaped sequential path through the line segments that comprise the sketch. The search for the best-matching path can be modeled using a Hidden Markov Model (HMM). We use an efficient dynamic programming method to evaluate the HMM with further optimizations based on the use of hand-drawn sketches. The technique we developed can cope with several issues that are common to sketches such as small gaps and branching. We allow for objects with either open or closed boundaries by allowing backtracking over the templates. We demonstrate the algorithm for a variety of templates and scanned drawings. We show that a likelihood score produced by the results can provide a meaningful measure of similarity to a template. An example-based method is presented for setting a meaningful recognition threshold, which can allow further refinement of results when that template is used again. Limitations of the algorithm and directions for future work are discussed.
Item Metadata
Title |
Template-based sketch recognition using Hidden Markov Models
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
2010
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Description |
Sketch recognition is the process by which the objects in a hand-drawn diagram
can be recognized and identified. We provide a method to recognize
objects in sketches by casting the problem in terms of searching for known
2D template shapes in the sketch. The template is defined as an ordered
polyline and the recognition requires searching for a similarly-shaped sequential
path through the line segments that comprise the sketch. The search
for the best-matching path can be modeled using a Hidden Markov Model
(HMM). We use an efficient dynamic programming method to evaluate the
HMM with further optimizations based on the use of hand-drawn sketches.
The technique we developed can cope with several issues that are common
to sketches such as small gaps and branching. We allow for objects with either
open or closed boundaries by allowing backtracking over the templates.
We demonstrate the algorithm for a variety of templates and scanned drawings.
We show that a likelihood score produced by the results can provide a
meaningful measure of similarity to a template. An example-based method
is presented for setting a meaningful recognition threshold, which can allow
further refinement of results when that template is used again. Limitations
of the algorithm and directions for future work are discussed.
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Genre | |
Type | |
Language |
eng
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Date Available |
2010-11-30
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0051971
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2011-05
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Campus | |
Scholarly Level |
Graduate
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International