This research paper proposes an improved feature reduction and classification technique to identify mild and severe dementia from brain MRI data. The manual interpretation of changes in brain volume based on visual examination by radiologist or a physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification sy...

In this paper, we introduce a new method for steganalysis of grey-scale images. First, we analyzed the effect of various steganographic processes on the statistical properties of the image. So we extracted the optimal features from the images, which have high ability in make differentiated between two groups of normal and stego images. In this method, high order statistics in discrete wavelet t...

There’s some very important meaning in the study of realtime face recognition and tracking system for the video monitoring and artifical vision. The current method is still very susceptible to the illumination condition, non-real time and very common to fail to track the target face especially when partly covered or moving fast. In this paper, we propose to use Boosted Cascade combined with ski...

Feature extraction and classification are two key steps for activity recognition in a smart home environment. In this work, we used three methods for feature extraction: Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA). The new features selected by each method are then used as the inputs for a Weighted Support Vector Machines (WSVM...

Support vector machine (SVM) and HMAX model are two powerful recent techniques. SVMs are classifiers which have demonstrated high generalization capabilities in many different tasks, including the object recognition problem. HMAX is a feature extraction method and this method is motivated by a quantitative model of visual cortex. In this paper we combine these two techniques for the palmprint v...

Feature extraction is an important component of a pattern recognition system. It performs two tasks: transforming input parameter vector into a feature vector and/or reducing its dimensionality. A well-de3ned feature extraction algorithm makes the classi3cation process more e4ective and e5cient. Two popular methods for feature extraction are linear discriminant analysis (LDA) and principal comp...

Feature extraction and classification are two key steps for activity recognition in a smart home environment. In this work, we used three methods for feature extraction: Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA). The new features selected by each method are then used as the inputs for a Weighted Support Vector Machines (WSVM...

Neural network has been popular in time series prediction in financial areas, because of their advantages in handling nonlinear systems. This paper hybridizes genetic algorithm and artificial neural network method (GABP), and hybridizes principal component analysis and support vector machine (PCA-SVM) to predict the next opening price in stock markets. Principal component analysis method is app...

Accurate land cover classification information is a critical variable for many applications. This study presents a method to classify land cover using the fusion data of airborne discrete return LiDAR (Light Detection and Ranging) and CASI (Compact Airborne Spectrographic Imager) hyperspectral data. Four LiDAR-derived images (DTM, DSM, nDSM, and intensity) and CASI data (48 bands) with 1 m spat...

Histograms of Oriented Gradients (HOG) is one of the wellknown features for object recognition. HOG features are calculated by taking orientation histograms of edge intensity in a local region. N.Dalal et al. proposed an object detection algorithm in which HOG features were extracted from all locations of a dense grid on a image region and the combined features are classified by using linear Su...