A learning algorithm for model‐based object detection

A learning algorithm for model‐based object detection Purpose – Detecting objects in images and videos is a difficult task that has challenged the field of computer vision. Most of the algorithms for object detection are sensitive to background clutter and occlusion, and cannot localize the edge of the object. An object's shape is typically the most discriminative cue for its recognition by humans. The purpose of this paper is to introduce a model‐based object detection method which uses only shape‐fragment features. Design/methodology/approach – The object shape model is learned from a small set of training images and all object models are composed of shape fragments. The model of the object is in multi‐scales. Findings – The major contributions of this paper are the application of learned shape fragments‐based model for object detection in complex environment and a novel two‐stage object detection framework. Originality/value – The results presented in this paper are competitive with other state‐of‐the‐art object detection methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Sensor Review Emerald Publishing

A learning algorithm for model‐based object detection

Sensor Review, Volume 33 (1): 15 – Jan 18, 2013

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Publisher
Emerald Publishing
Copyright
Copyright © 2013 Emerald Group Publishing Limited. All rights reserved.
ISSN
0260-2288
DOI
10.1108/02602281311294324
Publisher site
See Article on Publisher Site

Abstract

Purpose – Detecting objects in images and videos is a difficult task that has challenged the field of computer vision. Most of the algorithms for object detection are sensitive to background clutter and occlusion, and cannot localize the edge of the object. An object's shape is typically the most discriminative cue for its recognition by humans. The purpose of this paper is to introduce a model‐based object detection method which uses only shape‐fragment features. Design/methodology/approach – The object shape model is learned from a small set of training images and all object models are composed of shape fragments. The model of the object is in multi‐scales. Findings – The major contributions of this paper are the application of learned shape fragments‐based model for object detection in complex environment and a novel two‐stage object detection framework. Originality/value – The results presented in this paper are competitive with other state‐of‐the‐art object detection methods.

Journal

Sensor ReviewEmerald Publishing

Published: Jan 18, 2013

Keywords: Image processing; Programming and algorithm theory; Computer applications; Object detection; Shape matching; Image segmentation; Shape fragment; Computer vision

References

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