Neural network technology for pattern recognition, stock prediction and market forecasting


Pattern recognition

DCT-ANN Face Identification

Wavelet-ANN Face Recognition

Text-Independent Speaker Recognition based on ANN

Assembler-based Neural Network Simulator

Facial Expression Recognition System

Iris Recognition Based on Neural Networks

Neural Networks Based Signature Recognition

Eye Detection Based Facial Expression Recognition

Gait Recognition System

Leaf Recognition System

Optical Character Recognition

Neural Network Fingerprint Recognition

Keystroke Recognition

EEG Recognition

Neural Network Speech Recognition

Image processing

Image Compression With Neural Networks

Stock Market Forecasting

Neural Network Forecasting

External resources

Advanced Source Code .Com

Genetic Algorithms .It

Face Recognition .It

Iris Recognition .It

High-Order Statistics For Plant Classification

Download now Matlab source code
Requirements: Matlab, Matlab Image Processing Toolbox, Matlab Signal Processing Toolbox, Matlab Neural Network Toolbox.

Plants exist everywhere we live, as well as places without us. Many of them carry significant information for the development of human society. The urgent situation is that many plants are at the risk of extinction. So it is very necessary to set up a database for plant protection. We believe that the first step is to teach a computer how to classify plants. Compared with other methods, such as cell and molecule biology methods, classification based on leaf image is the first choice for leaf plant classification. Sampling leaves and photoing them are low-cost and convenient. One can easily transfer the leaf image to a computer and a computer can extract features automatically in image processing techniques. Some systems employ descriptions used by botanists. But it is not easy to extract and transfer those features to a computer automatically.

We have developed an efficient algorithm for leaf classification that combines high-order statistics of image features together with shape information and neural network as nonlinear classifier. The code has been tested with FLAVIA database achieving an excellent recognition rate of 92.09% (32 classes, 40 training images and the remaining images used for testing for each class, hence there are 1280 training images and 627 test images in total randomly selected and no overlap exists between the training and test images).

FLAVIA source code and dataset are available at this URL Our approach outperforms this algorithm and moreover it does not require any human interfered part. In FLAVIA algorithm in fact you need to mark the two terminals of the main vein of the leaf via mouse click. The distance between the two terminals is defined as the physiological length.

Stephen Gang Wu, Forrest Sheng Bao, Eric You Xu, Yu-Xuan Wang, Yi-Fan Chang and Chiao-Liang Shiang, A Leaf Recognition Algorithm for Plant classification Using Probabilistic Neural Network, IEEE 7th International Symposium on Signal Processing and Information Technology, Dec. 2007, Cairo, Egypt.

Index Terms: Matlab, source, code, neural network, feature extraction, leaf recognition, plant classification.

Release 1.0 Date 2009.04.09
Major features:

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