Algorithm BOSS (Bag-of-Salient local Spectrums) for non-rigid and partial 3D object retrieval
The aim of the proposed algorithm is to expedite three-dimensional objects indexing and retrieval. Investigating the Bag-of-Features (BoF) paradigm, we focus on a set of extracted local descriptors from 3D objects. Using scalar function calculated on the surface mesh, the algorithm extracts the sali...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Haj Mohamed, Hela [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015transfer abstract |
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| Schlagwörter: |
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| Umfang: |
9 |
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| Übergeordnetes Werk: |
Enthalten in: The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast - Liu, Yang ELSEVIER, 2018, an international journal, Amsterdam |
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| Übergeordnetes Werk: |
volume:168 ; year:2015 ; day:30 ; month:11 ; pages:790-798 ; extent:9 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.neucom.2015.05.045 |
|---|
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| 245 | 1 | 0 | |a Algorithm BOSS (Bag-of-Salient local Spectrums) for non-rigid and partial 3D object retrieval |
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| 520 | |a The aim of the proposed algorithm is to expedite three-dimensional objects indexing and retrieval. Investigating the Bag-of-Features (BoF) paradigm, we focus on a set of extracted local descriptors from 3D objects. Using scalar function calculated on the surface mesh, the algorithm extracts the salient points, and then associates each of these points with a local Fourier descriptor. The descriptor is computed on the neighboring salient points by projecting the geometry onto the first eigenvectors of Laplace–Beltrami operator. Additionally, through an offline learning step, a visual dictionary is built by clustering a large set of feature descriptors. Then, each 3D shape is described by a histogram of these visual words occurrences weighted using the number of their local descriptors. Experimental results show the highly discriminative capability of the proposed approach against rigid and non-rigid transformations, noise and geometry changes. The performance of our algorithm is also demonstrated on global and partial shape retrieval. | ||
| 520 | |a The aim of the proposed algorithm is to expedite three-dimensional objects indexing and retrieval. Investigating the Bag-of-Features (BoF) paradigm, we focus on a set of extracted local descriptors from 3D objects. Using scalar function calculated on the surface mesh, the algorithm extracts the salient points, and then associates each of these points with a local Fourier descriptor. The descriptor is computed on the neighboring salient points by projecting the geometry onto the first eigenvectors of Laplace–Beltrami operator. Additionally, through an offline learning step, a visual dictionary is built by clustering a large set of feature descriptors. Then, each 3D shape is described by a histogram of these visual words occurrences weighted using the number of their local descriptors. Experimental results show the highly discriminative capability of the proposed approach against rigid and non-rigid transformations, noise and geometry changes. The performance of our algorithm is also demonstrated on global and partial shape retrieval. | ||
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| 650 | 7 | |a Bag of Features |2 Elsevier | |
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2015 |
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10.1016/j.neucom.2015.05.045 doi GBVA2015014000024.pica (DE-627)ELV013185470 (ELSEVIER)S0925-2312(15)00721-3 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Haj Mohamed, Hela verfasserin aut Algorithm BOSS (Bag-of-Salient local Spectrums) for non-rigid and partial 3D object retrieval 2015transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The aim of the proposed algorithm is to expedite three-dimensional objects indexing and retrieval. Investigating the Bag-of-Features (BoF) paradigm, we focus on a set of extracted local descriptors from 3D objects. Using scalar function calculated on the surface mesh, the algorithm extracts the salient points, and then associates each of these points with a local Fourier descriptor. The descriptor is computed on the neighboring salient points by projecting the geometry onto the first eigenvectors of Laplace–Beltrami operator. Additionally, through an offline learning step, a visual dictionary is built by clustering a large set of feature descriptors. Then, each 3D shape is described by a histogram of these visual words occurrences weighted using the number of their local descriptors. Experimental results show the highly discriminative capability of the proposed approach against rigid and non-rigid transformations, noise and geometry changes. The performance of our algorithm is also demonstrated on global and partial shape retrieval. The aim of the proposed algorithm is to expedite three-dimensional objects indexing and retrieval. Investigating the Bag-of-Features (BoF) paradigm, we focus on a set of extracted local descriptors from 3D objects. Using scalar function calculated on the surface mesh, the algorithm extracts the salient points, and then associates each of these points with a local Fourier descriptor. The descriptor is computed on the neighboring salient points by projecting the geometry onto the first eigenvectors of Laplace–Beltrami operator. Additionally, through an offline learning step, a visual dictionary is built by clustering a large set of feature descriptors. Then, each 3D shape is described by a histogram of these visual words occurrences weighted using the number of their local descriptors. Experimental results show the highly discriminative capability of the proposed approach against rigid and non-rigid transformations, noise and geometry changes. The performance of our algorithm is also demonstrated on global and partial shape retrieval. Laplace–Beltrami operator Elsevier Bag of Features Elsevier Heat kernel diffusion Elsevier 3D shape retrieval Elsevier Auto-diffusion distance Elsevier Belaid, Samir oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:168 year:2015 day:30 month:11 pages:790-798 extent:9 https://doi.org/10.1016/j.neucom.2015.05.045 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 168 2015 30 1130 790-798 9 045F 610 |
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10.1016/j.neucom.2015.05.045 doi GBVA2015014000024.pica (DE-627)ELV013185470 (ELSEVIER)S0925-2312(15)00721-3 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Haj Mohamed, Hela verfasserin aut Algorithm BOSS (Bag-of-Salient local Spectrums) for non-rigid and partial 3D object retrieval 2015transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The aim of the proposed algorithm is to expedite three-dimensional objects indexing and retrieval. Investigating the Bag-of-Features (BoF) paradigm, we focus on a set of extracted local descriptors from 3D objects. Using scalar function calculated on the surface mesh, the algorithm extracts the salient points, and then associates each of these points with a local Fourier descriptor. The descriptor is computed on the neighboring salient points by projecting the geometry onto the first eigenvectors of Laplace–Beltrami operator. Additionally, through an offline learning step, a visual dictionary is built by clustering a large set of feature descriptors. Then, each 3D shape is described by a histogram of these visual words occurrences weighted using the number of their local descriptors. Experimental results show the highly discriminative capability of the proposed approach against rigid and non-rigid transformations, noise and geometry changes. The performance of our algorithm is also demonstrated on global and partial shape retrieval. The aim of the proposed algorithm is to expedite three-dimensional objects indexing and retrieval. Investigating the Bag-of-Features (BoF) paradigm, we focus on a set of extracted local descriptors from 3D objects. Using scalar function calculated on the surface mesh, the algorithm extracts the salient points, and then associates each of these points with a local Fourier descriptor. The descriptor is computed on the neighboring salient points by projecting the geometry onto the first eigenvectors of Laplace–Beltrami operator. Additionally, through an offline learning step, a visual dictionary is built by clustering a large set of feature descriptors. Then, each 3D shape is described by a histogram of these visual words occurrences weighted using the number of their local descriptors. Experimental results show the highly discriminative capability of the proposed approach against rigid and non-rigid transformations, noise and geometry changes. The performance of our algorithm is also demonstrated on global and partial shape retrieval. Laplace–Beltrami operator Elsevier Bag of Features Elsevier Heat kernel diffusion Elsevier 3D shape retrieval Elsevier Auto-diffusion distance Elsevier Belaid, Samir oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:168 year:2015 day:30 month:11 pages:790-798 extent:9 https://doi.org/10.1016/j.neucom.2015.05.045 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 168 2015 30 1130 790-798 9 045F 610 |
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10.1016/j.neucom.2015.05.045 doi GBVA2015014000024.pica (DE-627)ELV013185470 (ELSEVIER)S0925-2312(15)00721-3 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Haj Mohamed, Hela verfasserin aut Algorithm BOSS (Bag-of-Salient local Spectrums) for non-rigid and partial 3D object retrieval 2015transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The aim of the proposed algorithm is to expedite three-dimensional objects indexing and retrieval. Investigating the Bag-of-Features (BoF) paradigm, we focus on a set of extracted local descriptors from 3D objects. Using scalar function calculated on the surface mesh, the algorithm extracts the salient points, and then associates each of these points with a local Fourier descriptor. The descriptor is computed on the neighboring salient points by projecting the geometry onto the first eigenvectors of Laplace–Beltrami operator. Additionally, through an offline learning step, a visual dictionary is built by clustering a large set of feature descriptors. Then, each 3D shape is described by a histogram of these visual words occurrences weighted using the number of their local descriptors. Experimental results show the highly discriminative capability of the proposed approach against rigid and non-rigid transformations, noise and geometry changes. The performance of our algorithm is also demonstrated on global and partial shape retrieval. The aim of the proposed algorithm is to expedite three-dimensional objects indexing and retrieval. Investigating the Bag-of-Features (BoF) paradigm, we focus on a set of extracted local descriptors from 3D objects. Using scalar function calculated on the surface mesh, the algorithm extracts the salient points, and then associates each of these points with a local Fourier descriptor. The descriptor is computed on the neighboring salient points by projecting the geometry onto the first eigenvectors of Laplace–Beltrami operator. Additionally, through an offline learning step, a visual dictionary is built by clustering a large set of feature descriptors. Then, each 3D shape is described by a histogram of these visual words occurrences weighted using the number of their local descriptors. Experimental results show the highly discriminative capability of the proposed approach against rigid and non-rigid transformations, noise and geometry changes. The performance of our algorithm is also demonstrated on global and partial shape retrieval. Laplace–Beltrami operator Elsevier Bag of Features Elsevier Heat kernel diffusion Elsevier 3D shape retrieval Elsevier Auto-diffusion distance Elsevier Belaid, Samir oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:168 year:2015 day:30 month:11 pages:790-798 extent:9 https://doi.org/10.1016/j.neucom.2015.05.045 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 168 2015 30 1130 790-798 9 045F 610 |
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10.1016/j.neucom.2015.05.045 doi GBVA2015014000024.pica (DE-627)ELV013185470 (ELSEVIER)S0925-2312(15)00721-3 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Haj Mohamed, Hela verfasserin aut Algorithm BOSS (Bag-of-Salient local Spectrums) for non-rigid and partial 3D object retrieval 2015transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The aim of the proposed algorithm is to expedite three-dimensional objects indexing and retrieval. Investigating the Bag-of-Features (BoF) paradigm, we focus on a set of extracted local descriptors from 3D objects. Using scalar function calculated on the surface mesh, the algorithm extracts the salient points, and then associates each of these points with a local Fourier descriptor. The descriptor is computed on the neighboring salient points by projecting the geometry onto the first eigenvectors of Laplace–Beltrami operator. Additionally, through an offline learning step, a visual dictionary is built by clustering a large set of feature descriptors. Then, each 3D shape is described by a histogram of these visual words occurrences weighted using the number of their local descriptors. Experimental results show the highly discriminative capability of the proposed approach against rigid and non-rigid transformations, noise and geometry changes. The performance of our algorithm is also demonstrated on global and partial shape retrieval. The aim of the proposed algorithm is to expedite three-dimensional objects indexing and retrieval. Investigating the Bag-of-Features (BoF) paradigm, we focus on a set of extracted local descriptors from 3D objects. Using scalar function calculated on the surface mesh, the algorithm extracts the salient points, and then associates each of these points with a local Fourier descriptor. The descriptor is computed on the neighboring salient points by projecting the geometry onto the first eigenvectors of Laplace–Beltrami operator. Additionally, through an offline learning step, a visual dictionary is built by clustering a large set of feature descriptors. Then, each 3D shape is described by a histogram of these visual words occurrences weighted using the number of their local descriptors. Experimental results show the highly discriminative capability of the proposed approach against rigid and non-rigid transformations, noise and geometry changes. The performance of our algorithm is also demonstrated on global and partial shape retrieval. Laplace–Beltrami operator Elsevier Bag of Features Elsevier Heat kernel diffusion Elsevier 3D shape retrieval Elsevier Auto-diffusion distance Elsevier Belaid, Samir oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:168 year:2015 day:30 month:11 pages:790-798 extent:9 https://doi.org/10.1016/j.neucom.2015.05.045 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 168 2015 30 1130 790-798 9 045F 610 |
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10.1016/j.neucom.2015.05.045 doi GBVA2015014000024.pica (DE-627)ELV013185470 (ELSEVIER)S0925-2312(15)00721-3 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Haj Mohamed, Hela verfasserin aut Algorithm BOSS (Bag-of-Salient local Spectrums) for non-rigid and partial 3D object retrieval 2015transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The aim of the proposed algorithm is to expedite three-dimensional objects indexing and retrieval. Investigating the Bag-of-Features (BoF) paradigm, we focus on a set of extracted local descriptors from 3D objects. Using scalar function calculated on the surface mesh, the algorithm extracts the salient points, and then associates each of these points with a local Fourier descriptor. The descriptor is computed on the neighboring salient points by projecting the geometry onto the first eigenvectors of Laplace–Beltrami operator. Additionally, through an offline learning step, a visual dictionary is built by clustering a large set of feature descriptors. Then, each 3D shape is described by a histogram of these visual words occurrences weighted using the number of their local descriptors. Experimental results show the highly discriminative capability of the proposed approach against rigid and non-rigid transformations, noise and geometry changes. The performance of our algorithm is also demonstrated on global and partial shape retrieval. The aim of the proposed algorithm is to expedite three-dimensional objects indexing and retrieval. Investigating the Bag-of-Features (BoF) paradigm, we focus on a set of extracted local descriptors from 3D objects. Using scalar function calculated on the surface mesh, the algorithm extracts the salient points, and then associates each of these points with a local Fourier descriptor. The descriptor is computed on the neighboring salient points by projecting the geometry onto the first eigenvectors of Laplace–Beltrami operator. Additionally, through an offline learning step, a visual dictionary is built by clustering a large set of feature descriptors. Then, each 3D shape is described by a histogram of these visual words occurrences weighted using the number of their local descriptors. Experimental results show the highly discriminative capability of the proposed approach against rigid and non-rigid transformations, noise and geometry changes. The performance of our algorithm is also demonstrated on global and partial shape retrieval. Laplace–Beltrami operator Elsevier Bag of Features Elsevier Heat kernel diffusion Elsevier 3D shape retrieval Elsevier Auto-diffusion distance Elsevier Belaid, Samir oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:168 year:2015 day:30 month:11 pages:790-798 extent:9 https://doi.org/10.1016/j.neucom.2015.05.045 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 168 2015 30 1130 790-798 9 045F 610 |
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The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
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Algorithm BOSS (Bag-of-Salient local Spectrums) for non-rigid and partial 3D object retrieval |
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The aim of the proposed algorithm is to expedite three-dimensional objects indexing and retrieval. Investigating the Bag-of-Features (BoF) paradigm, we focus on a set of extracted local descriptors from 3D objects. Using scalar function calculated on the surface mesh, the algorithm extracts the salient points, and then associates each of these points with a local Fourier descriptor. The descriptor is computed on the neighboring salient points by projecting the geometry onto the first eigenvectors of Laplace–Beltrami operator. Additionally, through an offline learning step, a visual dictionary is built by clustering a large set of feature descriptors. Then, each 3D shape is described by a histogram of these visual words occurrences weighted using the number of their local descriptors. Experimental results show the highly discriminative capability of the proposed approach against rigid and non-rigid transformations, noise and geometry changes. The performance of our algorithm is also demonstrated on global and partial shape retrieval. |
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The aim of the proposed algorithm is to expedite three-dimensional objects indexing and retrieval. Investigating the Bag-of-Features (BoF) paradigm, we focus on a set of extracted local descriptors from 3D objects. Using scalar function calculated on the surface mesh, the algorithm extracts the salient points, and then associates each of these points with a local Fourier descriptor. The descriptor is computed on the neighboring salient points by projecting the geometry onto the first eigenvectors of Laplace–Beltrami operator. Additionally, through an offline learning step, a visual dictionary is built by clustering a large set of feature descriptors. Then, each 3D shape is described by a histogram of these visual words occurrences weighted using the number of their local descriptors. Experimental results show the highly discriminative capability of the proposed approach against rigid and non-rigid transformations, noise and geometry changes. The performance of our algorithm is also demonstrated on global and partial shape retrieval. |
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The aim of the proposed algorithm is to expedite three-dimensional objects indexing and retrieval. Investigating the Bag-of-Features (BoF) paradigm, we focus on a set of extracted local descriptors from 3D objects. Using scalar function calculated on the surface mesh, the algorithm extracts the salient points, and then associates each of these points with a local Fourier descriptor. The descriptor is computed on the neighboring salient points by projecting the geometry onto the first eigenvectors of Laplace–Beltrami operator. Additionally, through an offline learning step, a visual dictionary is built by clustering a large set of feature descriptors. Then, each 3D shape is described by a histogram of these visual words occurrences weighted using the number of their local descriptors. Experimental results show the highly discriminative capability of the proposed approach against rigid and non-rigid transformations, noise and geometry changes. The performance of our algorithm is also demonstrated on global and partial shape retrieval. |
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