In this paper, a novel algorithm based on Ridgelet Transform and support vector machine is proposed for human action recognition. The Ridgelet transform is a directional multi-resolution transform and it is more suitable for describing the human action by performing its directional information to form spatial features vectors. The dynamic transition between the spatial features is carried out u...

With fast evolving technology, it is necessary to design an efficient security system which can detect unauthorized access on any system. The need of the time is to implement an extremely secure, economic and perfect system for face recognition that can protect our system from unauthorized access. So, in this paper, a robust face recognition system approach is proposed for image decomposition u...

This paper deals with the application of Principal Component Analysis (PCA) and the Hotelling’s T2 Chart, using data collected from a drinking water treatment process. PCA is applied primarily for the dimensional reduction of the collected data. The Hotelling’s T2 control chart was used for the fault detection of the process. The data was taken from a United Utilities Multistage Water Treatment...

In this paper, a new face recognition system based on Haar wavelet transform (HWT) and Principal Component Analysis (PCA) using Levenberg-Marquardt backpropagation (LMBP) neural network is presented. The image face is preprocessed and detected. The Haar wavelet is used to form the coefficient matrix for the detected face. The image feature vector is obtained by computing PCA for the coefficient...

Face recognition is a technique to automatically identify or verify individuals. It receives a great attention in identification, authentication, security and many more applications. Diverse methods had been proposed for this purpose and also a lot of comparative studies were performed. However, researchers could not reach unified conclusion. In this paper, we are reporting an extensive quantit...

PCA has find out its most important application in the field of linear algebra. PCA is a method of extracting information from confusing data sets so used in various fields like neuroscience, computer graphics, etc [19]. PCA is a vector space transform often used to reduce multidimensional data sets to lower dimensions for analysis. Depending on the field of application, it is also named the di...

Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are well known techniques for face recognition. Both PCA and LDA by themselves have good recognition rates. We propose Canonical Correlation Analysis (CCA) for combining two feature extractors to improve the performance of the system, by obtaining the advantages of both. CCA finds the transformation for each extractor dat...

This paper describes an automatic summarization approach that constructs a summary by extracting the significant sentences. The approach takes advantage of the cooccurrence relationships between terms only in the document. The techniques used are principal component analysis (PCA) to extract the significant terms and singular value decompostion (SVD) to find out the significant sentences. The P...