Driver-Independent Assessment of Arousal States from Video Sequences Based on the Classification of Eyeblink Patterns.
Roongroj NopsuwanchaiYoshihiro NoguchiMieko OhsugaYoshiyuki KamakuraYumiko InouePublished in: ITSC (2008)
Keyphrases
- video sequences
- classification accuracy
- pattern recognition
- text classification
- lung disease
- pattern analysis
- support vector machine svm
- feature extraction
- neural network
- classification scheme
- classification algorithm
- decision trees
- support vector machine
- image classification
- classification method
- feature selection
- model selection
- data sets
- pattern representation
- frequent patterns
- temporal sequences
- training patterns
- discovering patterns
- automatic classification
- machine learning
- pattern discovery
- visual tracking
- feature vectors
- training set
- feature space
- preprocessing
- three dimensional