Part-based deformable object detection with a single sketch
Object Detection using shape is interesting since it is well known that humans can recognise an object simply from its shape. Thus, shape-based methods have great promise to handle a large amount of shape variation using a com- pact representation. In this paper, we present a new algorithm for ob...
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Main Authors: | , |
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Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2016
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/81591 http://hdl.handle.net/10220/39550 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | Object Detection using shape is interesting since it is well known that humans
can recognise an object simply from its shape. Thus, shape-based methods
have great promise to handle a large amount of shape variation using a com-
pact representation. In this paper, we present a new algorithm for object
detection that uses a single reasonably good sketch as a reference to build a
model for the object. The method hierarchically segments a given sketch into
parts using an automatic algorithm and estimates a di erent a ne transfor-
mation for each part while matching. A Hough-style voting scheme collects
evidence for the object from the leaves to the root in the part decomposi-
tion tree for robust detection. Missing edge segments, clutter and generic
object deformations are handled by
exibly following the contour paths in
the edge image that resemble the model contours. E cient data-structures
and a two-stage matching approach assist in yielding an e cient and robust
system. Results on ETHZ and several other popular image datasets yield
promising results compared to the state-of-the-art. A new dataset of real-life
hand-drawn sketches for all the object categories in the ETHZ dataset is also
used for evaluation. |
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