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We provide a generic framework to learn shape dictionaries of landmark-based curves that are defined in the continuous domain. We first present an unbiased alignment method that involves the construction of a mean shape as well as training sets whose elements are subspaces that contain all affine transformations of the training samples. The alignment relies on orthogonal projection operators that...
Countering network threats, particularly intrusions, is a challenging area of research in the field of information security. Intruders use sophisticated mechanisms to hide the attack payload and break the detection techniques. To overcome that, various unsupervised learning approaches from the field of machine learning and pattern recognition have been employed. The most popularly used method is Principal...
Active shape model is widely used for facial feature localization. Regarding the traditional ASM algorithm can't describe the object shape precisely, an improved ASM algorithm is proposed. At first, we establish shape model and use PCA (Principle Component Analysis) to transform high-dimensional data to lower dimensions. Another work is to establish local texture model giving sample points with different...
With millions of people suffering from dementia worldwide, the global prevalence of dementia has a significant impact on the patients' lives, their caregivers' physical and emotional states, and the global economy. Early diagnosis of dementia helps in finding suitable therapies that reduce or even prevent further deterioration of patients' cognitive abilities. MRI scans are shown to be the most effective...
We present detection of various fdters using neural networks usable for our Long wave infrared (LWIR) hyperspectral detection system (HDES). Some reduction techniques are shown, for our aim of the small neural network with small computing requirements. In addition, the filter measurement is usable for calibration and verification of the HDES properties.
Gender classification play a significant role in recognition performance. For the purpose of visual surveillance, gender is considered as an important factor. In this paper a hybrid approach is proposed by fusing Gait Energy Image (GEI) with spatio temporal parameters for the gender classification. The dataset used is CASIA B which comprises of 118 subjects (89 males and 29 females). The proposed...
The goals of this paper were twofold: to continue and refine previous research in the topic of tree cover type classification by harnessing modern machine learning models, and to extend the conclusions of that work to demonstrate that results gained from such models can be used to assist U.S. land management agencies in current challenges they face. Using the same dataset as the past study, an artificial...
In this paper, we propose a method to classify K-pop dance based on motion data obtained from Kinect V2 for research of motion classification and development of anti-plagiarism system. To do this, 200-point dances of K-pop are acquired. Dance motions from 40 amateur dancers are acquired to construct a total of 400 data. The proposed classification method consists of three steps. First, we obtain 13...
As network intrusion data's scale gets larger and larger, designing parallel schemes for intrusion detection have been becoming research focus in the field of information security. In order to solve the problem that the intrusion detection algorithm is high time-consuming, the classification of large amounts of data occupies lots of memory and the efficiency of single detection is low, a parallel...
High dimensionality of feature space is a problem in supervised machine learning. Redundant or superfluous features either slow down the training process or dilute the quality of classification. Many methods are available in literature for dimensionality reduction. Earlier studies explored a discernibility matrix (DM) based reduct calculation for dimensionality reduction. Discernibility matrix works...
This paper presents two different implementations for recognition of handwritten numerals using a high performance autoencoder and Principal Component Analysis (PCA) by making use of neural networks. Different from other approaches, the non-linear mapping capability of neural networks is used extensively here. The implementation involves the deployment of a neural network, and the use of an auto encoder...
This paper describes the different classifier methods with minimum means of clusters to achieve face recognition rate of humans from the feature extracted of training face image data for many sets of images as a data base. Principal Component Analysis (PCA) is a robust method used as feature extraction techniques for face recognition but the recognition decreases with the variation of person's actions...
Identification of colorants of artworks is of paramount importance in the context of museums and art galleries. We present a technique to discriminate the fiber dyes into natural or synthetic class using principal component analysis (PCA). Spectral imaging is used to measure the reflectance spectra of a variety of dyed wools in visible to near infrared (Vis/NIR): 400–1000 nm and short wave infrared...
Face recognition has been gaining popularity for long time in various fields of human computer interaction. Moreover face recognition technique is widely used for automatic biometric security control, document verification, criminal investigation etc. In this paper we propose a new approach of using PCA based face recognition method for human verification. PCA based method seems to be interested due...
A principal components analysis (PCA) algorithm is one of the most important algorithms that has been used for doing many tasks; for example, data dimension reduction, data compression such as image compression, pattern recognition such as face detection and recognition, and many other things. An improved principal components analysis (IPCA) algorithm is similar to the PCA algorithm except that it...
In this paper, a new object recognition framework is presented. The framework includes a variety of object recognition approaches based on Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and the K-nearest neighbor (K-NN). A color image vector representation model is also introduced. Based on the representation model, color Eigenspace is constructed using PCA and LDA for feature...
Feature extraction addresses the problem of finding the most compact and informative set of features. To maximize the effectiveness of each single feature extraction algorithm and to develop an efficient intrusion detection system, an ensemble of Linear Discriminant Analysis (LDA) and Principle Component Analysis (PCA) feature extraction algorithms is implemented. This ensemble PCA-LDA method has...
In order to identify a large number of very similar objects, a novel recognition approach is proposed by mean of combination of two dynamic grouping algorithms, the visual processing mechanism, PCA and multi-pathway SVM. The samples have been segmented to appropriate groups by grouping features, and then features with rotation invariance and translation invariance of each group are extracted. Finally,...
One of the most commonly problem in the field of network intrusion detection system is the tremendous number of redundant and irrelevant information used to build an intrusion detection system. In order to overcome this problem, we have used and compared two dimensionality reduction methods namely PCA and Fuzzy PCA which allows us to keeping just the most relevant information from the network traffic...
Despite the success of Principal Component Analysis (PCA) for dimensionality reduction, it is known that its most expressive components do not necessarily represent important discriminant features for pattern recognition. In this paper, the problem of ranking PCA components, computed from multi-class databases, is addressed by building multiple linear learners that are combined through the AdaBoost...
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