Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81591
Title: Part-based deformable object detection with a single sketch
Authors: Bhattacharjeea, Sreyasee Das
Mittal, Anurag
Keywords: Hand-drawn sketches
Dynamic programming
Contour-based object detection
Part-based models
Issue Date: 2015
Source: Bhattacharjeea, S. D., & Mittal, A. (2015). Part-based deformable object detection with a single sketch. Computer Vision and Image Understanding, 139, 73-87.
Series/Report no.: Computer Vision and Image Understanding
Abstract: 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.
URI: https://hdl.handle.net/10356/81591
http://hdl.handle.net/10220/39550
ISSN: 1077-3142
DOI: 10.1016/j.cviu.2015.06.005
Rights: © 2015 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by Computer Vision and Image Understanding, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.cviu.2015.06.005].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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