The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Image fusion is widely recognized as an important technique in pattern recognition and computer vision. The object of image fusion is fuse one or more source images with different focus points in to one image, so that the result of image fusion is an image which is more clarity and better information. This paper presents a novel and improved pixel-level multifocal image fusion technique has been implemented...
In allusion to similarity calculation difficulty caused by high maintenance of image data, this paper introduces sparse principal component algorithm to figure out embedded subspace after dimensionality reduction of image visual words on the basis of traditional spectral hashing image index method so that image high-dimension index results can be explained overall. This method is called sparse spectral...
Multi-instance learning (MIL) has been widely applied to diverse applications involving complicated data objects such as images and genes. However, most existing MIL algorithms can only handle small-or moderate-sized data. In order to deal with the large scale problems in MIL, we propose an efficient and scalable MIL algorithm named miFV. Our algorithm maps the original MIL bags into a new feature...
For text data, feature dimension reduction is a very significant and important work for simplifying document representation and enhancing computation of learning algorithm. There are usually two main dimension reduction strategies, feature extraction and feature selection. Feature extraction is to create new features to represent documents, whereas feature selection will return a subset of words as...
Currency recognition system is one of the fast growing research fields under image processing. This paper proposes a novel method for Indian currency recognition. Our proposed approach identifies denomination by extracting features like Center Numeral, Shape, RBI Seal, Latent Image and Micro Letter. Principal Component Analysis is used to reduce the dimensions and a similarity based classifier is...
Gender classification can play a significant role in security and surveillance system. It aids in identification of a person by recognizing its gender (male/female) from the face image only. Extracting discriminate features for male and female is a fundamental and challenging problem in the field of computer vision. In this manuscript, a combination of Approximation Face Image (AFI) with Principal...
Many statistical learning tasks deal with data which are presented in high-dimensional spaces, and the 'curse of dimensionality' phenomenon is often an obstacle to the use of many methods for solving these tasks. To avoid this phenomenon, various dimensionality reduction algorithms are used as the first key step in solving these tasks. The algorithms transform original high-dimensional data into lower...
The plausibility and robustness of an inferential control system entirely depend on the prediction accuracy of the estimator used as the feedback element. This paper is based on a previously proposed Gaussian process inferential controller that employs Gaussian process soft sensor as an estimator. The paper enhances the robustness and the reliability of the control system, particularly, during sensor...
In this paper, a new type of hybrid method that hybridizes PCA and EBGM as a two-stage procedure is presented to improve recognition performance in large-scale face recognition. Among various methods in face recognition, PCA is considered to identify human faces by holistic views, while EBGM is supposed to distinguish one face from another by details, but they are both excellent representative methods...
Recently, randomized partition trees have been theoretically shown to be very effective in performing high dimensional nearest neighbor search. In this paper, we introduce a variant of randomized partition trees for high dimensional nearest neighbor search problem and provide theoretical justification for its choice. Experiments on various real-life datasets show that performance of this new variant...
This paper addresses the problem of diagnosis of diseases on cotton leaf using Principle Component Analysis (PCA), Nearest Neighbourhood Classifier (KNN). Cotton leaf data analysis aims to study the diseases pattern which are defined as any deterioration of normal physiological functions of plants, producing characteristic symptoms in terms of undesirable color changes mainly occurs upon leaves; caused...
In this paper, the Gabor filter is studied and further expanded for temporal facial expression analysis. Originally, the Gabor feature describes both spatial and frequency characteristics of 2D images. The prominent of the theorem has been validated in research communities for a decade due to its similarity to the human perception system. The performance of the filter in the existing research gives...
The social-based forwarding scheme has recently been shown to be an effective solution to improve the performance of opportunistic routing. Most of the current works focus on the globally defined node centrality, resulting in a bias towards the most popular nodes. However, these nodes may not be appropriate relay candidates for some target nodes, because they may have low importance relative to these...
Human face recognition technology is one of the hottest research in the field of pattern recognition at present. In this paper, the principle component analysis (PCA) and bidirectional principle component analysis (BDPCA) methods are proposed to recognize a grayscale face image, for which the size of the spatial distribution is 64 × 64. At first, the main part of the face is extracted to form the...
The goal of the study presented in this paper is to investigate the performance of MIMO-WCDMA networks, where Principal Component Analysis (PCA) is employed at the reception. In this context, multipath propagation is exploited, as the individual received signals can be seen as different instances of the same physical phenomenon (i.e. transmission and reception of WCDMA sequences). As results indicate,...
Principal component analysis (PCA) is an effective statistical technique for face recognition because it can reduce the dimensions of a given unlabeled high-dimensional dataset while keeping its spatial characteristics as much as possible. However, since PCA only explains the covariance structure of all the data its most expressive components, it cannot represent the most important discriminant directions...
Face recognition using eigenfaces is a popular technique based on principal component analysis (PCA). However, its performance suffers from the presence of outliers due to occlusions and noise often encountered in unconstrained settings. We address this problem by utilizing L1-eigenfaces for robust face recognition. We introduce an effective approach for L1-eigenfaces based on combining fast computation...
Face recognition, one of the most explored themes in biometry, is used in a wide range of applications: access control, forensic detection, surveillance and monitoring system, robotic and human machine interaction. In this paper, a new classifier is proposed for face recognition. The performance of this new classifier is compared with the performance of the KNN classifier. The face image database...
In the present work, appearance-based face recognition method called the Laplacianface approach is used. The face images are mapped into a face subspace for analysis by using Locality Preserving Projections(LPP). The technique is different from Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) which effectively see only the Euclidean structure of face space. The main goal of...
The human face is one of the easiest characteristic, which can be used in biometric security system to identify a user. Face recognition technology, is very popular and it is used more widely because it does not require any kind of physical contact between the users and the device. Camera scans the user face and match it to a database for verification. Furthermore, it is easy to install and does not...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.