Large pattern recognition system using multi neural networks

Now a day, artificial neural network has been applied popularly in many fields of human life. However, creating an efficient network for a large classifier like handwriting recognition systems is still a big challenge to scientists.

 

Kernel Principal Component Analysis (KPCA)

Sample application demonstrating how to use Kernel Principal Component Analysis (KPCA) to perform non-linear transformations and dimensionality reduction.

 

Linear Discriminant Analysis (LDA)

Sample application demonstrating how to use Linear Discriminant Analysis (also known as LDA, or ''Fisher's (Multiple) Linear Discriminant Analysis'') to perform linear transformations and classification.

 

Principal Component Analysis (PCA)

Sample application demonstrating how to use Principal Component Analysis (PCA) to perform linear transformations and dimensionality reduction.

 

Clustering (Gaussian Mixture Models)

Point cloud clustering with Gaussian Mixture Models.

Multivariate Gaussian mixture distribution fitting using Expectation-Maximization.

 

 

Clustering (K-Means and MeanShift)

Color clustering with K-Means and Meanshift.

MeanShift and K-Means for color reduction (color clustering) in digital images.

 

Hidden Markov Models

Demonstrates how to use Hidden Markov Models (HMMs) and Markov Sequence Classifiers to recognize sequences of discrete observations.

 

 

Playing Card Recognition

This sample applications demonstrates the recognition of Playing Cards via Image Processing Techniques. The system has the capability to recognise a standard deck of playing cards, with both ranks and suits uniquely recognised. Recognising playing cards involves character segmentation, affine transformation, edge detection and template matching. Rotation and scaling are considered for robust playing card recognition

 

 

Image stitching (FREAK)

Image stitching with the FREAK feature detector.

Demonstrates how to perform automatic image stitching by interest point matching. The actual stitching uses many parts of the framework, such as the FREAK feature detector, RANSAC, k-nearest neighbor matching, homography estimation and the linear gradient image blending.

 

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