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Component trees for image sequences and streams
- Source :
- Pattern Recognition Letters, Pattern Recognition Letters, Elsevier, 2020, 129, pp.255-262. ⟨10.1016/j.patrec.2019.11.038⟩, Pattern Recognition Letters, 2020, 129, pp.255-262. ⟨10.1016/j.patrec.2019.11.038⟩
- Publication Year :
- 2020
- Publisher :
- HAL CCSD, 2020.
-
Abstract
- International audience; Morphological hierarchies now form a well-established framework for (still) image modeling and processing. However , their extension to time-related data remains largely unexplored. In this paper, we address such a topic and show how to analyze image sequences with tree-based representations. To do so, we distinguish between three kinds of models, namely spatial, temporal and spatial-temporal hierarchies. For each of them, we review different strategies to build the hierarchy from an image sequence. We also propose some algorithms to update such trees when new images are appended to the series and we compared the time complexity with tree building from scratch. We illustrate our findings with the max and min-tree structures built on grayscale data provided by Satellite Image Time Series that are gathering a growing interest in Earth Observation. Besides, we provide a comparative study for different hierarchies with classification experiments.
- Subjects :
- Hierarchy
Earth observation
Computer science
business.industry
Pattern recognition
02 engineering and technology
01 natural sciences
Grayscale
Image (mathematics)
Tree (data structure)
Artificial Intelligence
Component (UML)
[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]
0103 physical sciences
Signal Processing
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Satellite Image Time Series
Computer Vision and Pattern Recognition
Artificial intelligence
010306 general physics
business
Time complexity
Software
Subjects
Details
- Language :
- English
- ISSN :
- 01678655
- Database :
- OpenAIRE
- Journal :
- Pattern Recognition Letters, Pattern Recognition Letters, Elsevier, 2020, 129, pp.255-262. ⟨10.1016/j.patrec.2019.11.038⟩, Pattern Recognition Letters, 2020, 129, pp.255-262. ⟨10.1016/j.patrec.2019.11.038⟩
- Accession number :
- edsair.doi.dedup.....329573ae13afedaf7785c75fa4c1e878