Multi-modal RGB-D Image Segmentation from Appearance and Geometric Depth Maps
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| Natura: | Artículo científico |
| Lingua: | en |
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Instituto Tecnológico Metropolitano
2020
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| _version_ | 1876477049691963392 |
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| author | Isail Salazar |
| author_facet | Isail Salazar |
| contents | Multi-modal RGB-D Image Segmentation from Appearance and Geometric Depth Maps Isail Salazar Said Pertuz Fabio Martínez Ingeniería RGB over multi D images segmentation Classical image segmentation algorithms exploit the detection of similarities and discontinuities of different visual cues to define and differentiate multiple regions of interest in images. However, due to the high variability and uncertainty of image data, producing accurate results is difficult. In other words, segmentation based just on color is often insufficient for a large percentage of real-life scenes. This work presents a novel multi-modal segmentation strategy that integrates depth and appearance cues from RGB-D images by building a hierarchical region-based representation, i.e., a multi-modal segmentation tree (MM-tree). For this purpose, RGB-D image pairs are represented in a complementary fashion by different segmentation maps. Based on color images, a color segmentation tree (C-tree) is created to obtain segmented and over-segmented maps. From depth images, two independent segmentation maps are derived by computing planar and 3D edge primitives. Then, an iterative region merging process can be used to locally group the previously obtained maps into the MM-tree. Finally, the top emerging MM-tree level coherently integrates the available information from depth and appearance maps. The experiments were conducted using the NYU-Depth V2 RGB-D dataset, which demonstrated the competitive results of our strategy compared to state-of-the-art segmentation methods. Specifically, using test images, our method reached average scores of 0.56 in Segmentation Covering and 2.13 in Variation of Information. 2020 artículo científico 0123-7799 https://www.redalyc.org/articulo.oa?id=344263272008 https://www.redalyc.org/journal/3442/344263272008/ https://www.redalyc.org/journal/3442/344263272008/html/ https://www.redalyc.org/journal/3442/344263272008/344263272008.epub https://www.redalyc.org/journal/3442/344263272008/movil https://doi.org/10.22430/22565337.1538 en http://www.redalyc.org/revista.oa?id=3442 TecnoLógicas application/pdf Instituto Tecnológico Metropolitano TecnoLógicas (Colombia) Num.48 Vol.23 |
| format | Artículo científico |
| id | redalyc_344263272008 |
| institution | Redalyc |
| language | en |
| publishDate | 2020 |
| publisher | Instituto Tecnológico Metropolitano |
| spellingShingle | Multi-modal RGB-D Image Segmentation from Appearance and Geometric Depth Maps Isail Salazar Ingeniería RGB over multi D images segmentation Multi-modal RGB-D Image Segmentation from Appearance and Geometric Depth Maps Isail Salazar Said Pertuz Fabio Martínez Ingeniería RGB over multi D images segmentation Classical image segmentation algorithms exploit the detection of similarities and discontinuities of different visual cues to define and differentiate multiple regions of interest in images. However, due to the high variability and uncertainty of image data, producing accurate results is difficult. In other words, segmentation based just on color is often insufficient for a large percentage of real-life scenes. This work presents a novel multi-modal segmentation strategy that integrates depth and appearance cues from RGB-D images by building a hierarchical region-based representation, i.e., a multi-modal segmentation tree (MM-tree). For this purpose, RGB-D image pairs are represented in a complementary fashion by different segmentation maps. Based on color images, a color segmentation tree (C-tree) is created to obtain segmented and over-segmented maps. From depth images, two independent segmentation maps are derived by computing planar and 3D edge primitives. Then, an iterative region merging process can be used to locally group the previously obtained maps into the MM-tree. Finally, the top emerging MM-tree level coherently integrates the available information from depth and appearance maps. The experiments were conducted using the NYU-Depth V2 RGB-D dataset, which demonstrated the competitive results of our strategy compared to state-of-the-art segmentation methods. Specifically, using test images, our method reached average scores of 0.56 in Segmentation Covering and 2.13 in Variation of Information. 2020 artículo científico 0123-7799 https://www.redalyc.org/articulo.oa?id=344263272008 https://www.redalyc.org/journal/3442/344263272008/ https://www.redalyc.org/journal/3442/344263272008/html/ https://www.redalyc.org/journal/3442/344263272008/344263272008.epub https://www.redalyc.org/journal/3442/344263272008/movil https://doi.org/10.22430/22565337.1538 en http://www.redalyc.org/revista.oa?id=3442 TecnoLógicas application/pdf Instituto Tecnológico Metropolitano TecnoLógicas (Colombia) Num.48 Vol.23 |
| title | Multi-modal RGB-D Image Segmentation from Appearance and Geometric Depth Maps |
| topic | Ingeniería RGB over multi D images segmentation |
| url | https://www.redalyc.org/articulo.oa?id=344263272008 https://www.redalyc.org/journal/3442/344263272008/ https://www.redalyc.org/journal/3442/344263272008/html/ https://www.redalyc.org/journal/3442/344263272008/344263272008.epub https://www.redalyc.org/journal/3442/344263272008/movil https://doi.org/10.22430/22565337.1538 |