Stereo image based object localization framework for visually impaired people using edge orientation histogram and co-occurrence matrices
© 2015 ICST. A new framework that uses internet-based images for detecting objects and estimating real world location of the objects via stereo images is proposed. This framework provides a self-learning ability for detecting desired objects in the scene without pre-prepared classifiers by harvestin...
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th-cmuir.6653943832-445102018-04-25T07:51:26Z Stereo image based object localization framework for visually impaired people using edge orientation histogram and co-occurrence matrices Supakit Fuangkaew Karn Patanukhom Agricultural and Biological Sciences © 2015 ICST. A new framework that uses internet-based images for detecting objects and estimating real world location of the objects via stereo images is proposed. This framework provides a self-learning ability for detecting desired objects in the scene without pre-prepared classifiers by harvesting sample images of the objects from the internet. Histogram and co-occurrence matrices of edge orientation are used as features. The objects are recognized based on likelihood scores and distance in the feature space between every window in the scene and k-nearest prototypes. A local feature matching is used to match the feature points in stereo pair. Disparities from stereo images are used to estimate real world distance and direction of the objects. The experiments on 120 pairs of stereo images from three object classes show the satisfying results in comparison to baseline methods. 2018-01-24T04:43:56Z 2018-01-24T04:43:56Z 2015-01-01 Conference Proceeding 2-s2.0-84943278953 10.4108/icst.iniscom.2015.258352 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84943278953&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/44510 |
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Agricultural and Biological Sciences Supakit Fuangkaew Karn Patanukhom Stereo image based object localization framework for visually impaired people using edge orientation histogram and co-occurrence matrices |
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© 2015 ICST. A new framework that uses internet-based images for detecting objects and estimating real world location of the objects via stereo images is proposed. This framework provides a self-learning ability for detecting desired objects in the scene without pre-prepared classifiers by harvesting sample images of the objects from the internet. Histogram and co-occurrence matrices of edge orientation are used as features. The objects are recognized based on likelihood scores and distance in the feature space between every window in the scene and k-nearest prototypes. A local feature matching is used to match the feature points in stereo pair. Disparities from stereo images are used to estimate real world distance and direction of the objects. The experiments on 120 pairs of stereo images from three object classes show the satisfying results in comparison to baseline methods. |
format |
Conference Proceeding |
author |
Supakit Fuangkaew Karn Patanukhom |
author_facet |
Supakit Fuangkaew Karn Patanukhom |
author_sort |
Supakit Fuangkaew |
title |
Stereo image based object localization framework for visually impaired people using edge orientation histogram and co-occurrence matrices |
title_short |
Stereo image based object localization framework for visually impaired people using edge orientation histogram and co-occurrence matrices |
title_full |
Stereo image based object localization framework for visually impaired people using edge orientation histogram and co-occurrence matrices |
title_fullStr |
Stereo image based object localization framework for visually impaired people using edge orientation histogram and co-occurrence matrices |
title_full_unstemmed |
Stereo image based object localization framework for visually impaired people using edge orientation histogram and co-occurrence matrices |
title_sort |
stereo image based object localization framework for visually impaired people using edge orientation histogram and co-occurrence matrices |
publishDate |
2018 |
url |
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84943278953&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/44510 |
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