Instance-Based Learning Algorithms
Machine Learning
Statistical feature matrix for texture analysis
CVGIP: Graphical Models and Image Processing
The nature of statistical learning theory
The nature of statistical learning theory
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Cover trees for nearest neighbor
ICML '06 Proceedings of the 23rd international conference on Machine learning
A semi-automatic segmentation procedure for feature extraction in remotely sensed imagery
Computers & Geosciences
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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Digital image processing provides powerful tools for fast and precise analysis of large image data sets in marine and geoscientific applications. Because of the increasing volume of georeferenced image and video data acquired by underwater platforms such as remotely operated vehicles, means of automatic analysis of the acquired image data are required. A new and fast-developing application is the combination of video imagery and mosaicking techniques for seafloor habitat mapping. In this article we introduce an approach to fully automatic detection and quantification of Pogonophora coverage in seafloor video mosaics from mud volcanoes. The automatic recognition is based on textural image features extracted from the raw image data and classification using machine learning techniques. Classification rates of up to 98.86% were achieved on the training data. The approach was extensively validated on a data set of more than 4000 seafloor video mosaics from the Hakon Mosby Mud Volcano.