Neural Networks
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modern Information Retrieval
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A multistage adaptive thresholding method
Pattern Recognition Letters
Evaluation of the effects of Gabor filter parameters on texture classification
Pattern Recognition
Image thresholding by variational minimax optimization
Pattern Recognition
Comparison and fusion of multiresolution features for texture classification
Pattern Recognition Letters
IEEE Transactions on Information Theory
Design-based texture feature fusion using Gabor filters and co-occurrence probabilities
IEEE Transactions on Image Processing
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Image based burning state recognition plays an important role in sintering process control of rotary kiln. Although many efforts on dealing with this problem have been made over the past years, the recognition performance cannot be satisfactory due to the disturbance from smoke and dust inside the kiln. This work aims to develop a reliable burning state recognition system using extreme learning machines with heterogeneous features. The recorded flame images are firstly represented by various low-level features, which characterize the distribution of the temperature field and the flame color, the local and global configurations. To learn the merits of our proposed flame image-based burning state recognition system, four learner models (ELM, MLP, PNN and SVM) are examined by a typical flame image database with 482 images. Simulation results demonstrate that the heterogeneous features based ELM classifiers outperform other classifiers in terms of both recognition accuracy and computational complexity.