Front-End Vision and Multi-Scale Image Analysis: Multi-scale Computer Vision Theory and Applications, written in Mathematica
Many approaches have been proposed to solve the problem of finding the optic flow field of an image sequence. Three major classes of optic flow computation techniques can discriminated (see for a good overview Beauchemin and Barron IBeauchemin19951): gradient based (or differential) methods; phase b...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
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Springer Netherlands
2003
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| Ausgabe: | 1st ed. 2003 |
| Schriftenreihe: | Computational Imaging and Vision
27 |
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| Online-Zugang: | UBY01 URL des Eerstveröffentlichers |
| Zusammenfassung: | Many approaches have been proposed to solve the problem of finding the optic flow field of an image sequence. Three major classes of optic flow computation techniques can discriminated (see for a good overview Beauchemin and Barron IBeauchemin19951): gradient based (or differential) methods; phase based (or frequency domain) methods; correlation based (or area) methods; feature point (or sparse data) tracking methods; In this chapter we compute the optic flow as a dense optic flow field with a multi scale differential method. The method, originally proposed by Florack and Nielsen [Florack1998a] is known as the Multiscale Optic Flow Constrain Equation (MOFCE). This is a scale space version of the well known computer vision implementation of the optic flow constraint equation, as originally proposed by Horn and Schunck [Horn1981]. This scale space variation, as usual, consists of the introduction of the aperture of the observation in the process. The application to stereo has been described by Maas et al. [Maas 1995a, Maas 1996a]. Of course, difficulties arise when structure emerges or disappears, such as with occlusion, cloud formation etc. Then knowledge is needed about the processes and objects involved. In this chapter we focus on the scale space approach to the local measurement of optic flow, as we may expect the visual front end to do. 17. 2 Motion detection with pairs of receptive fields As a biologically motivated start, we begin with discussing some neurophysiological findings in the visual system with respect to motion detection |
| Beschreibung: | 1 Online-Ressource (XVIII, 466 p) |
| ISBN: | 9781402088407 |
| DOI: | 10.1007/978-1-4020-8840-7 |
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| 520 | |a Many approaches have been proposed to solve the problem of finding the optic flow field of an image sequence. Three major classes of optic flow computation techniques can discriminated (see for a good overview Beauchemin and Barron IBeauchemin19951): gradient based (or differential) methods; phase based (or frequency domain) methods; correlation based (or area) methods; feature point (or sparse data) tracking methods; In this chapter we compute the optic flow as a dense optic flow field with a multi scale differential method. The method, originally proposed by Florack and Nielsen [Florack1998a] is known as the Multiscale Optic Flow Constrain Equation (MOFCE). This is a scale space version of the well known computer vision implementation of the optic flow constraint equation, as originally proposed by Horn and Schunck [Horn1981]. This scale space variation, as usual, consists of the introduction of the aperture of the observation in the process. The application to stereo has been described by Maas et al. [Maas 1995a, Maas 1996a]. Of course, difficulties arise when structure emerges or disappears, such as with occlusion, cloud formation etc. Then knowledge is needed about the processes and objects involved. In this chapter we focus on the scale space approach to the local measurement of optic flow, as we may expect the visual front end to do. 17. 2 Motion detection with pairs of receptive fields As a biologically motivated start, we begin with discussing some neurophysiological findings in the visual system with respect to motion detection | ||
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| index_date | 2024-07-03T16:12:22Z |
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| institution | BVB |
| isbn | 9781402088407 |
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| spelling | Haar Romeny, Bart M. Verfasser aut Front-End Vision and Multi-Scale Image Analysis Multi-scale Computer Vision Theory and Applications, written in Mathematica by Bart M. Haar Romeny 1st ed. 2003 Dordrecht Springer Netherlands 2003 1 Online-Ressource (XVIII, 466 p) txt rdacontent c rdamedia cr rdacarrier Computational Imaging and Vision 27 Many approaches have been proposed to solve the problem of finding the optic flow field of an image sequence. Three major classes of optic flow computation techniques can discriminated (see for a good overview Beauchemin and Barron IBeauchemin19951): gradient based (or differential) methods; phase based (or frequency domain) methods; correlation based (or area) methods; feature point (or sparse data) tracking methods; In this chapter we compute the optic flow as a dense optic flow field with a multi scale differential method. The method, originally proposed by Florack and Nielsen [Florack1998a] is known as the Multiscale Optic Flow Constrain Equation (MOFCE). This is a scale space version of the well known computer vision implementation of the optic flow constraint equation, as originally proposed by Horn and Schunck [Horn1981]. This scale space variation, as usual, consists of the introduction of the aperture of the observation in the process. The application to stereo has been described by Maas et al. [Maas 1995a, Maas 1996a]. Of course, difficulties arise when structure emerges or disappears, such as with occlusion, cloud formation etc. Then knowledge is needed about the processes and objects involved. In this chapter we focus on the scale space approach to the local measurement of optic flow, as we may expect the visual front end to do. 17. 2 Motion detection with pairs of receptive fields As a biologically motivated start, we begin with discussing some neurophysiological findings in the visual system with respect to motion detection Computer Imaging, Vision, Pattern Recognition and Graphics Image Processing and Computer Vision Biological and Medical Physics, Biophysics Mathematical and Computational Biology Artificial Intelligence Optical data processing Biophysics Biological physics Biomathematics Artificial intelligence Mathematica Programm (DE-588)4268208-3 gnd rswk-swf Maschinelles Sehen (DE-588)4129594-8 gnd rswk-swf Mehrskalenanalyse (DE-588)4416235-2 gnd rswk-swf Maschinelles Sehen (DE-588)4129594-8 s Mehrskalenanalyse (DE-588)4416235-2 s Mathematica Programm (DE-588)4268208-3 s DE-604 Erscheint auch als Druck-Ausgabe 9781402015038 Erscheint auch als Druck-Ausgabe 9789401751490 Erscheint auch als Druck-Ausgabe 9781402015076 https://doi.org/10.1007/978-1-4020-8840-7 Verlag URL des Eerstveröffentlichers Volltext |
| spellingShingle | Haar Romeny, Bart M. Front-End Vision and Multi-Scale Image Analysis Multi-scale Computer Vision Theory and Applications, written in Mathematica Computer Imaging, Vision, Pattern Recognition and Graphics Image Processing and Computer Vision Biological and Medical Physics, Biophysics Mathematical and Computational Biology Artificial Intelligence Optical data processing Biophysics Biological physics Biomathematics Artificial intelligence Mathematica Programm (DE-588)4268208-3 gnd Maschinelles Sehen (DE-588)4129594-8 gnd Mehrskalenanalyse (DE-588)4416235-2 gnd |
| subject_GND | (DE-588)4268208-3 (DE-588)4129594-8 (DE-588)4416235-2 |
| title | Front-End Vision and Multi-Scale Image Analysis Multi-scale Computer Vision Theory and Applications, written in Mathematica |
| title_auth | Front-End Vision and Multi-Scale Image Analysis Multi-scale Computer Vision Theory and Applications, written in Mathematica |
| title_exact_search | Front-End Vision and Multi-Scale Image Analysis Multi-scale Computer Vision Theory and Applications, written in Mathematica |
| title_exact_search_txtP | Front-End Vision and Multi-Scale Image Analysis Multi-scale Computer Vision Theory and Applications, written in Mathematica |
| title_full | Front-End Vision and Multi-Scale Image Analysis Multi-scale Computer Vision Theory and Applications, written in Mathematica by Bart M. Haar Romeny |
| title_fullStr | Front-End Vision and Multi-Scale Image Analysis Multi-scale Computer Vision Theory and Applications, written in Mathematica by Bart M. Haar Romeny |
| title_full_unstemmed | Front-End Vision and Multi-Scale Image Analysis Multi-scale Computer Vision Theory and Applications, written in Mathematica by Bart M. Haar Romeny |
| title_short | Front-End Vision and Multi-Scale Image Analysis |
| title_sort | front end vision and multi scale image analysis multi scale computer vision theory and applications written in mathematica |
| title_sub | Multi-scale Computer Vision Theory and Applications, written in Mathematica |
| topic | Computer Imaging, Vision, Pattern Recognition and Graphics Image Processing and Computer Vision Biological and Medical Physics, Biophysics Mathematical and Computational Biology Artificial Intelligence Optical data processing Biophysics Biological physics Biomathematics Artificial intelligence Mathematica Programm (DE-588)4268208-3 gnd Maschinelles Sehen (DE-588)4129594-8 gnd Mehrskalenanalyse (DE-588)4416235-2 gnd |
| topic_facet | Computer Imaging, Vision, Pattern Recognition and Graphics Image Processing and Computer Vision Biological and Medical Physics, Biophysics Mathematical and Computational Biology Artificial Intelligence Optical data processing Biophysics Biological physics Biomathematics Artificial intelligence Mathematica Programm Maschinelles Sehen Mehrskalenanalyse |
| url | https://doi.org/10.1007/978-1-4020-8840-7 |
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