Results 221 to 230 of about 223,989 (265)

Illuminant classification based on random forest

2015 14th IAPR International Conference on Machine Vision Applications (MVA), 2015
We present a novel machine learning/pattern recognition based colour constancy method. We cast colour constancy as an illumination source recognition problem, and have developed an effective and efficient random forest based classification technique for inferring the class of illumination source of an image.
Bozhi Liu, Guoping Qiu
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Use of forest structure to improve classification

2014 IEEE Geoscience and Remote Sensing Symposium, 2014
This paper deals with forest classification in tropical and subtropical areas using multi-sources data fusion. Topological, environmental, structural and visual information are used to classify the samples. This study improves a previous classification by introducing airborne LiDAR information through the computation of the Digital Vegetation Elevation
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Variation and classification of forest

1981
Serai and swamp communities such as those mentioned above were named and briefly described by Chipp (1927) and Taylor (1960). Such small-scale local variation is relatively easy to recognise and explain. Even the earliest forest investigations, however, revealed the existence of differences from one part of the forest zone to another in the structure ...
J. B. Hall, M. D. Swaine
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Ontological Random Forests for Image Classification

International Journal of Information Retrieval Research, 2015
Previous image classification approaches mostly neglect semantics, which has two major limitations. First, categories are simply treated independently while in fact they have semantic overlaps. For example, “sedan” is a specific kind of “car”. Therefore, it's unreasonable to train a classifier to distinguish between “sedan” and “car”.
Ning Xu 0007, Jiangping Wang, Guojun Qi, Thomas S. Huang, Weiyao Lin   +4 more
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Hyperspectral feature selection for forest classification

IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004, 2004
Hyperspectral imagery contains many correlated bands, which not only increase computing complexity but also degrade classification accuracy if not enough training data are available [David A. Landgrebe, 2003]. To mitigate this problem, linear image transformations, such as principle components analysis (PCA), minimum noise fraction (MNF), and canonical
Tian Han 0003, David G. Goodenough, Andrew Dyk, Hao Chen 0004   +3 more
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Processing hyperion and ali for forest classification

IEEE Transactions on Geoscience and Remote Sensing, 2003
Hyperion (a hyperspectral sensor) and the Advanced Land Imager (ALI) (a multispectral sensor) are carried on the National Aeronautics and Space Administration's Earth Observing 1 (EO-1) satellite. The Evaluation and Validation of EO-1 for Sustainable Development (EVEOSD) is our project supporting the EO-1 mission.
David G. Goodenough, Andrew Dyk, K. Olaf Niemann, Jay S. Pearlman, Hao Chen 0004, Tian Han 0003, Matthew Murdoch, Chris West   +7 more
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A database for automatic classification of forest species

Machine Vision and Applications, 2012
Forest species can be taxonomically divided into groups, genera, and families. This is very important for an automatic forest species classification system, in order to avoid possible confusion between species belonging to two different groups, genera, or families.
Jefferson G. Martins, Luiz E. Soares de Oliveira, Silvana Nisgoski, Robert Sabourin   +3 more
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Random Forests for land cover classification

Pattern Recognition Letters, 2006
Random Forests are considered for classification of multisource remote sensing and geographic data. Various ensemble classification methods have been proposed in recent years. These methods have been proven to improve classification accuracy considerably. The most widely used ensemble methods are boosting and bagging.
Pall Oskar Gislason, Jón Atli Benediktsson, Johannes R. Sveinsson   +2 more
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Classification Using Probabilistic Random Forest

2015 IEEE Symposium Series on Computational Intelligence, 2015
The Probabilistic random forest is a classification model which chooses a subset of features for each random forest depending on the F-score of the features. In other words, the probability of a feature being chosen in the feature subset increases as the F-score of the feature in the dataset.
Rajhans Gondane, V. Susheela Devi
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Classification of cardiotocography records by random forest

2013 36th International Conference on Telecommunications and Signal Processing (TSP), 2013
The cardiotocography (CTG) is a diagnostic method which is widely used in prenatal care. The CTG is indicated since 27 weeks of pregnancy and it measures heart activity, uterine contraction and fetal movement. Results of the CTG allow recognizing of three basic different fetal states (physiological, suspect and pathological) and an obstetrician can ...
Tomas Peterek, Jana Krohova, Pavel Dohnálek, Petr Gajdos   +3 more
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