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Mutual subspace method (MSM), which is one of image-based approaches, showed strong discrimination capability in gait recognition. In general, 2D image matrices are transformed into 1D image vectors to be used as input into MSM, and then principal component analysis (PCA) is applied to 1D vectors to generate a subspace. However, due to the high dimensionalities of 1D vectors, the evaluation accuracy...
Preliminary-summation-based PCA (PS-PCA), was recently proposed to handle the non-Gaussian features of industrial processes. However, when PS-PCA is applied to the monitoring of data with outliers, the “summation infection” phenomenon occurs, which makes PS-PCA ineffective. To eliminate the influence of outliers, this paper proposes a novel robust PS-PCA (RPS-PCA) which distinguishes outliers from...
Face recognition in real scenarios is mainly affected by illumination variation and occlusion, and therefore in order to develop a robust face recognition system these issues should be handled simultaneously. To this aim, the steps involved in the presented framework are (i) computationally simple and efficient preprocessing chain that eliminates major effects of illumination variation and noise while...
Dimensionality reduction of feature vector size plays a vital role in enhancing the text processing capabilities; it aims in reducing the size of the feature vector used in the mining tasks (classification, clustering… etc.). This paper proposes an efficient approach to be used in reducing the size of the feature vector for web text document classification process. This approach is based on using...
Recently, deep features extracted from Convolutional Neural Networks (CNNs) have been widely adopted in various applications, such as face recognition. Compared with the handcrafted descriptors, deep features have more powerful representation ability which can lead to better performance. Effective feature representations play an important role in ear recognition. While deep features have not been...
Our aim is to evaluate fundamental parameters from the analysis of the electromagnetic spectra of stars. We may use 103–105 spectra; each spectrum being a vector with 102–104 coordinates. We thus face the so-called “curse of dimensionality”. We look for a method to reduce the size of this data-space, keeping only the most relevant information. As a reference method, we use principal component analysis...
Prostate Cancer (PCa) is highly prevalent and is the second most common cause of cancer-related deaths in men. Multiparametric MRI (mpMRI) is robust in detecting PCa. We developed a weakly supervised computer-aided detection (CAD) system that uses biopsy points to learn to identify PCa on mpMRI. Our CAD system, which is based on a deep convolutional neural network architecture, yielded an area under...
The high dimension and large computational complexity are shortcomings of feature extraction in multi-level histogram sequence local binary pattern (M-HSLBP). In order to overcome those problems, a face recognition algorithm based on the combination of multi-level histogram sequence center-symmetric local binary pattern (M-HCSLBP) and Fisherface is proposed in this paper. First, CS-LBP algorithm is...
Face recognition is growingly becoming a very remarkable field in machine learning and artificial intelligence. In this paper, we introduce a modified scheme for face recognition based on the hybrid color model along with the Gabor Feature Extraction and Principal Component Analysis (PCA). Our algorithm is tested on two face databases namely, 'The MUCT Database' and 'The FEI Database' for recognition...
Attendance automation has become one of the most important needs in educational institutions and work places across the world, since it saves time and accurate too. Face recognition system needs least human cooperation and is viable too. The system automatically detects the student's entry in the class and marks attendance for the particular student periodically. The data collected can be used by...
In this paper, we present an intelligent system where agents can co-ordinate creative tasks through machine learning and cooperation. For machine learning, we used commonly used pattern recognition algorithm - Principal Component Analysis (PCA). Based on recognition, we plan a task that is performed by multiple intelligent agents. In our case, task is to draw a pattern or perform a creative art by...
This paper is devoted to three approaches of the face recognition realized in solving a complex program of visual identification. We described the corresponding algorithms. Graphs and fragments of their program realization are provided, the applicability of the results on the basis of the research is analyzed.
This paper proposes to presents a hand gesture recognition as an assistive tool for patients care. The system uses Haar-like feature, Adaboost algorithm and Cascade Classification for hand detection. Principal Component Analysis work together with Histogram of Oriented Gradients and hand gesture decision to achieve preferable results of hand gesture recognition. Hand detection accuracy achieves 91...
On the basis of the evaluation of local properties of the data many nonlinear techniques have been suggested the field of computer vision. The application of the dimensionality reduction covers many fields like medical, geographical, simulation and many more. I have studied MDS, LLE and LTSA. Overall, the users are allowed to access the search-tools in linear system. A review and systematic comparison...
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...
Biometric is emerging area in the computer science for the secure various systems. Day to day life peoples are preferred to use, robust and highly acceptable security system which can surpass the human errors. Many scientists are engaged to develop a strong biometric system, but there are a lot of challenges in the real time application. It is observed and found that researchers are only working on...
Face detection has attracted immense attention because it has many applications in computer vision communication and automatic control system. Face detection is a method to detect a face from an image which have several attributes in that image. Research into face detection, expression recognition, face tracking, pose estimation is required. By giving a single image, challenge is to detect the face...
Clustering is the organization of a set of data in homogeneous classes. It aims to classify the representation of the initial data. The automatic classification recovers all the methods allowing the automatic construction of such groups. This paper describes how to classify data using a new design of neural classifiers with radial basis function (RBF) based on a new algorithm for characterizing the...
Long-term visual SLAM, in familiar, semi-dynamic, and partially changing environments is an important area of research in robotics. The main problem we faced is the question of how to describe a scene discriminatively and compactly-both of which are necessary in order to cope with changes in appearance and a large amount of visual information. In this study, we address the above issues by mining visual...
The use of unimodal biometric system is very low because of physiological defects, modes of user and their environment. Some of those drawbacks are alleviated by providing same identity for multiple evidences. Here a multimodal biometric system is proposed based on LBP, PCA and probabilistic neural network (PNN). In proposed method LBP extracted the Face features from face images and those features...
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