NDLI: Object detection with novel shape representation using bounding edge fragments

Content Provider	IEEE Xplore Digital Library
Author	Anvaripour, M. Ebrahimnezhad, H.
Copyright Year	2010
Description	Author affiliation: Electrical Engineering Faculty, Computer Vision Research Lab, Sahand University of Technology, Tabriz, Iran (Anvaripour, M.; Ebrahimnezhad, H.)
Abstract	This paper proposes a new method to detect objects in images. Boundaries contain shape of the objects. To detect objects in cluttered images, we use boundary fragments. Boundary fragments are obtained by our new training procedure. Poisson equation is used to divide edges and extract generic model of the object. Gaussian Mixture Model (GMM) is used to model shape of the object. This creates relation between local boundary fragments. To localize our desired object in image, a voting process is performed in Hough space. A scale adaptive search algorithm is used in this voting space to find the most probable regions of object presence. By learned GMM, shape of the object is obtained from test image. We remove false positives, usual problems in object detection tasks, by matching generic model to the extracted shapes. Local orientations histograms of the objects model are used for matching. We evaluate our method and compare with state-of-the-art methods. Experimental results show the power of our proposed method in detection and its robustness in face to scale and translation variations.
Starting Page	846
Ending Page	851
File Size	1416419
Page Count	6
File Format	PDF
ISBN	9781424481835
DOI	10.1109/ISTEL.2010.5734140
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-04
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Object Detection Shape Image edge detection Boundary Fragments GMM Poisson Equation Object Shape Training Histograms Hough Voting Space Poisson equations Generic Model Feature extraction Mathematical model
Content Type	Text
Resource Type	Article

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