Point cloud saliency detection via local sparse coding
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| Formato: | Artículo científico |
| Lenguaje: | en |
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Universidad Nacional de Colombia
2019
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| _version_ | 1876423393756053504 |
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| author | Esmeide Leal |
| author_facet | Esmeide Leal |
| contents | Point cloud saliency detection via local sparse coding Esmeide Leal German Sanchez-Torres John William Branch-Bedoya Ingeniería saliency Point clouds sparse coding minimum description length The human visual system (HVS) can process large quantities of visual information instantly. Visual saliency perception is the process of locating and identifying regions with a high degree of saliency from a visual standpoint. Mesh saliency detection has been studied extensively in recent years, but few studies have focused on 3D point cloud saliency detection. The estimation of visual saliency is important for computer graphics tasks such as simplification, segmentation, shape matching and resizing. In this paper, we present a method for the direct detection of saliency on unorganized point clouds. First, our method computes a set of overlapping neighborhoods and estimates a descriptor vector for each point inside it. Then, the descriptor vectors are used as a natural dictionary in order to apply a sparse coding process. Finally, we estimate a saliency map of the point neighborhoods based on the Minimum Description Length (MDL) principle. Experiment results show that the proposed method achieves similar results to those from the literature review and in some cases even improves on them. It captures the geometry of the point clouds without using any topological information and achieves an acceptable performance. The effectiveness and robustness of our approach are shown by comparing it to previous studies in the literature review. 2019 artículo científico 0012-7353 https://www.redalyc.org/articulo.oa?id=49662418028 https://www.redalyc.org/journal/496/49662418028/ https://www.redalyc.org/journal/496/49662418028/html/ https://www.redalyc.org/journal/496/49662418028/49662418028.epub https://www.redalyc.org/journal/496/49662418028/movil 10.15446/dyna.v86n209.75958 en http://www.redalyc.org/revista.oa?id=496 Dyna application/pdf Universidad Nacional de Colombia Dyna (Colombia) Num.209 Vol.86 |
| format | Artículo científico |
| id | redalyc_49662418028 |
| institution | Redalyc |
| language | en |
| publishDate | 2019 |
| publisher | Universidad Nacional de Colombia |
| spellingShingle | Point cloud saliency detection via local sparse coding Esmeide Leal Ingeniería saliency Point clouds sparse coding minimum description length Point cloud saliency detection via local sparse coding Esmeide Leal German Sanchez-Torres John William Branch-Bedoya Ingeniería saliency Point clouds sparse coding minimum description length The human visual system (HVS) can process large quantities of visual information instantly. Visual saliency perception is the process of locating and identifying regions with a high degree of saliency from a visual standpoint. Mesh saliency detection has been studied extensively in recent years, but few studies have focused on 3D point cloud saliency detection. The estimation of visual saliency is important for computer graphics tasks such as simplification, segmentation, shape matching and resizing. In this paper, we present a method for the direct detection of saliency on unorganized point clouds. First, our method computes a set of overlapping neighborhoods and estimates a descriptor vector for each point inside it. Then, the descriptor vectors are used as a natural dictionary in order to apply a sparse coding process. Finally, we estimate a saliency map of the point neighborhoods based on the Minimum Description Length (MDL) principle. Experiment results show that the proposed method achieves similar results to those from the literature review and in some cases even improves on them. It captures the geometry of the point clouds without using any topological information and achieves an acceptable performance. The effectiveness and robustness of our approach are shown by comparing it to previous studies in the literature review. 2019 artículo científico 0012-7353 https://www.redalyc.org/articulo.oa?id=49662418028 https://www.redalyc.org/journal/496/49662418028/ https://www.redalyc.org/journal/496/49662418028/html/ https://www.redalyc.org/journal/496/49662418028/49662418028.epub https://www.redalyc.org/journal/496/49662418028/movil 10.15446/dyna.v86n209.75958 en http://www.redalyc.org/revista.oa?id=496 Dyna application/pdf Universidad Nacional de Colombia Dyna (Colombia) Num.209 Vol.86 |
| title | Point cloud saliency detection via local sparse coding |
| topic | Ingeniería saliency Point clouds sparse coding minimum description length |
| url | https://www.redalyc.org/articulo.oa?id=49662418028 https://www.redalyc.org/journal/496/49662418028/ https://www.redalyc.org/journal/496/49662418028/html/ https://www.redalyc.org/journal/496/49662418028/49662418028.epub https://www.redalyc.org/journal/496/49662418028/movil |