Image Processing -Matlab Tutorial 2

Today I'm going to present you some spatial filtering methods. This methods are known as Mask processing methods as well. These filters are used for different purposes.like

• Image Enhancements
• Edge Detection

Image Processing -Matlab Tutorial 1

Digital Image Processing 

Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing.

Difference between Image Processing and Computer Vision

SVM

Support Vector Machines

A support vector machine (SVM) is a statistical supervised learning technique from the field of machine learning applicable to both classification and regression. The original SVM algorithm was invented by Vladimir N. Vapnik and the current standard incarnation (soft margin) was proposed by Corinna Cortes and Vapnik in 1993 and published 1995. [1] SVMs use the spirit of the Structural Risk Minimization principle.

A support vector machine constructs a hyperplane or set of hyperplanes in a high or infinite dimensional space, which can be used for classification, regression, or other tasks. Good separation is achieved by the hyperplane that has the largest distance to the nearest training data point of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier. SVMs are used to classify linear and nonlinear separation models.

Whereas the original problem may be stated in a finite dimensional space, it often happens that the sets to discriminate are not linearly separable in that space. For this reason, it was proposed that the original finite-dimensional space be mapped into a much higher-dimensional space, presumably making the separation easier in that space. To keep the computational load reasonable, the mappings used by SVM schemes are designed to ensure that dot products may be computed easily in terms of the variables in the original space, by defining them in terms of a kernel function K(x,y) selected to suit the problem.

Radial Basis Function

Today I'm going to give a small introduction for a simple Machine Learning Algorithm called Radial Basis Function. Actually I was learnt this method in my machine learning class. You can get a good understand by referring my assignment. 
 
Neural Networks offer a powerful framework for representing nonlinear mappings from several inputs to one or more outputs. An important application of neural networks is regression. Instead of mapping the inputs into a discrete class label, the neural network maps the input variables into continuous values.

A major class of neural networks is the radial basis function (RBF) neural network. We will look at the architecture of RBF neural networks, followed by its applications in both regression and classification.

Clustering - K Means

Clustering analysis has been a topic of emerging research issue in data mining due its variety of applications. It is broadly use in wide variety of applications, including statistics, image processing, computational biology, mobile communication, medicine and economics. Clustering is a process which partitions a given data set into homogeneous groups based on given features such that similar objects are kept in a group whereas dissimilar objects are in different groups.

It is the most important unsupervised learning problem. It deals with finding structure in a collection of unlabeled data.