No self-introduction
Loading
Profile
Follow
codes (12)
Support vector machine
no vote
In machine learning , support vector machines 
Alaa855555 2016-08-23
Alaa855555 1 0
1
Face detection
no vote
========================================================================
MICROSOFT FOUNDATION CLASS LIBRARY : TrackEye
========================================================================
AppWizard has created this TrackEye application for you. This application
not only demonstrates the basics of using the Microsoft Foundation classes
but is also a starting point for writing your application.
This file contains a summary of what you will find in each of the files th
Alaa855555 0 0
1
Face Detection system
no vote
================================================================================
MICROSOFT FOUNDATION CLASS LIBRARY : VidCap Project Overview
===============================================================================
The application wizard has created this VidCap application for
you. This application not only demonstrates the basics of using the Microsoft
Foundation Classes but is also a starting point for writing your application.
This file contains a summary of what you will find in each of the files that
Alaa855555 1 0
1
Face recognition + Principal component analysis (P
no vote
Recently, the PCA has been extensively employed for face recognition algorithms. It is one of the most popular
representation methods for a face image The PCA method is used for dimension reduction for linear discriminate
analysis (LDA), generating a new paradigm, called fisherface. The fisherface approach is more insensitive to
variations of lighting, illumination and facial expressions
Alaa855555 0 0
1
Face recognition + Kernel Principal Component Anal
no vote
A kernel principal component analysis (PCA) was recently proposed as a nonlinear extension of a PCA.
The basic idea is to first map the input space into a feature space via nonlinear mapping and then compute
the principal components in that feature space. This letter adopts the kernel PCA as a mechanism for extracting
facial features. Through adopting a polynomial kernel, the Alaa855555 2016-08-23
Alaa855555 0 0
1
Kernel Principal Component Analysis
no vote
A kernel principal component analysis (PCA) was recently proposed as a nonlinear extension of a PCA. The basic
idea is to first map the input space into a feature space via nonlinear mapping and then compute the principal
components in that feature space. This letter adopts the kernel PCA as a mechanism for extracting facial
features. Through adopting a polynomial kernel, the principal components can be computed within the space
Alaa855555 0 0
1
Face recognition + PCA + SVM
no vote
A novel approach for solving face recognition problem. Our method combines 2D Principal Component Analysis
(2DPCA), one of the prominent methods for extracting feature vectors, and Support Vector Machine (SVM), the
most powerful discriminative method for classification. Experiments based on proposed method have been
conducted on OR database; the results show that the proposed method could improve the classification rates.
Alaa855555 4 0
1
Support victor machine implimentation
no vote
n machine learning , support vector machines ( Alaa855555 2016-08-23
Alaa855555 0 0
1
Blind prediction of natural video quality
no vote
We propose a blind (no reference or NR) video quality evaluation model that is nondistortion specific. The approach relies on a spatio-temporal model of video scenes in the discrete cosine transform domain, and on a model that characterizes the type of motion occurring in the scenes, to predict video quality. We use the models to define video statistics and perceptual features that are the basis of a video quality assessment (VQA) algorithm that does not require the presence of a pristine video to compare against in order to predict a perceptual quality score. The contributions of this paper are threefold. 1) We propose a spatio-temporal natural
Alaa855555 1 0
1
No-reference Image Quality Assessment based on Spa
no vote
We develop an efficient general-purpose no-reference (NR) image quality assessment (IQA) model that utilizes local spatial and spectral entropy features on distorted images. Using a 2-stage framework of distortion classification followed by quality assessment, we utilize a support vector machine (SVM) to train an image distortion and quality prediction engine. The resulting algorithm, dubbed Spatial-Spectral Entropy-based Quality (SSEQ) index, is capable of assessing the quality of a distorted image across multiple distortion categories. We explain the entropy features used and their relevance to perception and thoroughly evaluate the algorithm on the LIVE IQA database. We find that SSEQ matches well with human subjective opinions of image quality, and is statistically superior to the full-reference (FR) IQA algorithm SSIM and several top-performing
Alaa855555 0 0
1
View More
Follows0
Fans0