Inhalt

•  
   Abstract
•  
   Acknowledgment
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   Acronyms
•  
  Introduction
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   Motivation
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   Computer Vision for Underwater Environmental Monitoring
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   Image acquisition challenges
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   Annotation challenges
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   Example
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   Contributions
•  
   Publications
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   Structure
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   Notation
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  Computer Vision
•  
   Digital image
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  Color spaces
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   RGB color space
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   CIE L*a*b* and CIE L*u*v color space
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   HSV color space
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   HSI color space
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   l alpha beta color space
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   YCbCr color space
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   Feature extraction
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   Local statistics
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   Histogram features
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   Canny edge features
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   Gabor features
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   Data-driven features
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   Feature normalization
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   Feature selection
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   Augmentation
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   Machine learning
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   Unsupervised machine learning
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   Supervised machine learning
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   Deep learning
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   Training and tuning
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   Post-processing
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   Evaluation
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  Manual annotation of underwater images
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   Manual annotation
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   Quality assessment
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   Gold standard
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   Correctness
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   Reproducibility
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   BIIGLE
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  Underwater imaging
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   Digital imaging
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   Underwater in-situ imaging
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   Fixed underwater observatories
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   Underwater wet-lab imaging
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  Preprocessing for underwater images
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   Overview of underwater image preprocessing methods
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  Applied preprocessing methods
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   Auto white balancing
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   Fixed white balancing
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   Histogram equalization
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   Contrast limited adaptive histogram equalization
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   Automatic color equalization
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   Local space average color scaling
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   White Patch Retinex and Gray World combination
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   Unsupervised color correction
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   Automated underwater image preprocessing
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   Feature space based illumination and color enhancement
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   l alpha beta underwater color correction
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  Assessment
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   Cluster index ranking
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  Computer vision for in-situ monitoring
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   Sessile species: Lophelia pertusa
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   Material
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   Task 1: color monitoring
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   Task 2: polyp activity monitoring
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   Results
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   Discussion
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   Conclusion
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  Mobile species: shrimp
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   Material
•  
   Methods
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   Results
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   Discussion
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   Conclusion
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  Computer vision for wet-lab experiments
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   Sessile species: Mesophyllum engelhartii
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   Material
•  
   Methods
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   Results
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   Discussion
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   Conclusion
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  Data science for environmental monitoring
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  In-situ: Lophelia pertusa
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   Material
•  
   Methods
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   Results
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   Discussion
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   Conclusion
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  Wet-lab: Mesophyllum engelhartii
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   Material
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   Methods
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   Results
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   Discussion
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   Conclusion
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  Discussion
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   Computer vision systems
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   Data science
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   Conclusion
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   Future perspectives
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   Bibliography