Fruit recognition using Statistical and Features extraction by PCA

Fruit Recognition using Color and Texture Features

The computer vision strategies used to recognize a fruit rely on four basic features which characterize the object: intensity, color, shape and texture. This paper proposes an efficient fusion of color and texture features for fruit recognition. The recognition is done by the minimum distance classifier based upon the statistical and co-occurrence features derived from the Wavelet transformed s...

Face Recognition using Eigenfaces , PCA and Supprot Vector Machines

This paper is based on a combination of the principal component analysis (PCA), eigenface and support vector machines. Using N-fold method and with respect to the value of N, any person’s face images are divided into two sections. As a result, vectors of training features and test features are obtain ed. Classification precision and accuracy was examined with three different types of kernel and...

Devanagari Isolated Character Recognition by using Statistical features

In this paper, we present a methodology for off-line Isolated handwritten Devanagari character recognition. The proposed methodology relies on a three feature extraction techniques. The first technique is based on recursive subdivisions of the character image so that the resulting sub-images at each iteration have balanced (approximately equal) numbers of foreground pixels, as far as this is po...

Face Recognition Using Different Statistical Features

Facial Recognition using Eigenfaces by PCA

Face recognition systems have been grabbing high attention from commercial market point of view as well as pattern recognition field. It also stands high in researchers community. Face recognition have been fast growing, challenging and interesting area in real-time applications. A large number of face recognition algorithms have been developed from decades. The present paper refers to differen...