Отрывок: On the automation of gestalt perception in remotely sensed data Michaelsen E. Computer Optics, 2018, Vol. 42(6) 1011 a) b) c) Fig. 2. Example image #5 – Thimphu, Bhutan, a) original, converted to intensities, b) super-pixel segments, c) super-pixel features (without colors), courtesy to Google Earth Search The combinatorial problem resulting from such gen- erative models was already seen by the pioneers of the field such as Rosenfeld or ...
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Поле DC | Значение | Язык |
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dc.contributor.author | Michaelsen, E. | - |
dc.date.accessioned | 2018-12-29 10:01:10 | - |
dc.date.available | 2018-12-29 10:01:10 | - |
dc.date.issued | 2018 | - |
dc.identifier | Dspace\SGAU\20181225\73261 | ru |
dc.identifier.citation | Michaelsen E. On the automation of gestalt perception in remotely sensed data. Computer Optics 2018; 42(6): 1008-1014. DOI: 10.18287/2412-6179-2018-42-6-1008-1014 | ru |
dc.identifier.uri | https://dx.doi.org/10.18287/2412-6179-2018-42-6-1008-1014 | - |
dc.identifier.uri | http://repo.ssau.ru/handle/Zhurnal-Komputernaya-optika/On-the-automation-of-gestalt-perception-in-remotely-sensed-data-73261 | - |
dc.description.abstract | Gestalt perception, the laws of seeing, and perceptual grouping is rarely addressed in the context of remotely sensed imagery. The paper at hand reviews the corresponding state as well in machine vision as in remote sensing, in particular concerning urban areas. Automatic methods can be separated into three types: 1) knowledge-based inference, which needs machine-readable knowledge, 2) automatic learning methods, which require labeled or un-labeled example images, and 3) perceptual grouping along the lines of the laws of seeing, which should be pre-coded and should work on any kind of imagery, but in particular on urban aerial or satellite data. Perceptual grouping of parts into aggregates is a combinatorial problem. Exhaustive enumeration of all combinations is intractable. The paper at hand presents a constant-false-alarm-rate search rationale. An open problem is the choice of the extraction method for the primitive objects to start with. Here super-pixel-segmentation is used. | ru |
dc.language.iso | en | ru |
dc.publisher | Новая техника | ru |
dc.relation.ispartofseries | 42;6 | - |
dc.subject | perceptual grouping | ru |
dc.subject | remote sensing | ru |
dc.subject | urban areas | ru |
dc.title | On the automation of gestalt perception in remotely sensed data | ru |
dc.type | Article | ru |
dc.textpart | On the automation of gestalt perception in remotely sensed data Michaelsen E. Computer Optics, 2018, Vol. 42(6) 1011 a) b) c) Fig. 2. Example image #5 – Thimphu, Bhutan, a) original, converted to intensities, b) super-pixel segments, c) super-pixel features (without colors), courtesy to Google Earth Search The combinatorial problem resulting from such gen- erative models was already seen by the pioneers of the field such as Rosenfeld or ... | - |
Располагается в коллекциях: | Журнал "Компьютерная оптика" |
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420609.pdf | Основная статья | 3.19 MB | Adobe PDF | Просмотреть/Открыть |
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