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.
In this paper, we study the problem of rotation invariant texture classifications. There are several methods in texture recognition problem, we compare three best known methods such us: Gabor wavelet filter, Local Binary Pattern operators (LBP) and co-occurrence matrix (GLCM). A multi-class Support Vector Machines (SVM) is used as a classifier. The three methods are evaluated based on two different...
This paper presents a ”Decision Templates” (DTs) approach to develop customized Electrocardiogram (ECG) beat classifier in an effort to further improve the performance of ECG classification. Taking advantage of the Un-decimated Wavelet Transform (UWT), which also serves as a tool for noise reduction, we extracted 10 ECG morphological, as well as one timing interval features. For classification we...
The topics of fingerprint classification, indexing, and retrieval have been studied extensively in the past decades. One problem faced by researchers is that in all publicly available fingerprint databases, only a few fingerprint samples from each individual are available for training and testing, making it inappropriate to use sophisticated statistical methods for recognition. Hence most of the previous...
Microcalcifications are one of the key symptoms facilitating early detection of breast cancer. In this paper, The textural features are extracted from the segmented mammogram image to classify the microcalcifications into benign, malignant or normal. The reduced features are selected from the extracted set of features using reduction algorithms. Initially the reduced features are normalized between...
Ensemble methods have proved to be an effective tool to increase the performance of pattern recognition applications. An ensemble method behaves like an expert committee in predicting the class to which a sample belongs. In this paper, we present a novel ensemble method with high classification accuracy and resistance to noisy data. In our proposed method, we exploit a type of bagging in which the...
Digital mammography is a preferred method for early detection of breast cancer. However, in most cases, it is very difficult to distinguish benign and malignant masses without a biopsy, hence, misdiagnosis is always possible. In this paper, the Extreme Learning Machine (ELM) algorithm is used to classify the suspicious masses in digitized mammograms available in the Mini-MIAS database. As selection...
The signature verification is the behavioral parameter of biometrics and is used to authenticate a person. A typical signature verification system generally consists of four components: data acquisition, preprocessing, feature extraction and verification. In this paper, Biometric Security System Based on Signature Verification Using Neural Networks (BSSV) is presented. The global and grid features...
Online Social Networks are so popular nowadays that they are a major component of an individual's social interaction. They are also emotionally-rich environments where close friends share their emotions, feelings and thoughts. In this paper, a new framework is proposed for characterizing emotional interactions in social networks, and then using these characteristics to distinguish friends from acquaintances...
Previous sequential pattern mining algorithms have focused on improving performance in terms of runtime and memory consumption without considering the specifics of different data sources or application scenarios. In this paper, we focus on mining closed sequential patterns from website click streams by extending the state of the art Bi-Directional Extension (BIDE) algorithm in order to identify domain-specific...
A new feature selection approach is proposed for facial expression recognition system. The features are extracted using Gabor filters from Grey-scale images for characterizing facial texture. Then, an adaptive filter selection (AFS) algorithm is applied to choose the best subset of Gabor filters with different scales and orientations. In AFS algorithm, the filters are selected based on spectral difference...
In face recognition, coarsening-granularity image blocks mainly reflect some contour features and global information of human face identity. On the other hand, refining-granularity face parts can carry more local features about identifying information, for example: mouth, eyes and brows, etc. This paper proposes the framework of multi-granularity feature combination. Under the framework, a multi-granularity...
Language identification (LID) is always regarded to be a fascinating field to be studied. Studies on language identification has been carried out from early 1970's and up to now lot of research have been undergone in this area. In this paper a few of the papers are highlighted and reviewed based on the past history and the current state of research on various techniques that have been applied for...
In automotive industry the safety of cars behavior is monitoring using computers. The information acquired on the bus communication is often redundant and not relevant. Therefore in the case of faults detection and isolation based on machine learning model, we need to reduce the number of variables according with their relevance and allowing taking decision in real time. In this paper, we propose...
This paper describes an application of a particle swarm optimisation based AdaBoost algorithm to classify human facial expressions. The particle swarm is used to choose optimal Haar features for constructing weak classifiers within AdaBoost. This algorithm is trained using the Japanese Female Facial Expression dataset and tested on the Cohn-Kanade AU-Coded Face Expression Database. The results show...
This paper presents an algorithm to extract the region of interest (ROI) from the palm print image of the Hong Kong PolyU large-scale palm print database (version 2). Competitive coding method is used for feature extraction. Coding based methods are among the most promising palm print recognition methods because of their small feature size, fast matching speed, and high verification accuracy. Competitive...
Zoning is a widespread technique for hand-written character recognition. When a zoning method is considered, the pattern image is subdivided into zones each one providing regional information related to a specific part of the pattern. This paper presents an overview of zoning methods. Through the paper, both static and dynamic zonings are addressed and the most recent approaches for zoning design...
Intelligent Computer Aided Diagnosis (CAD) Systems can be used for detecting Microcalcification (MC) clusters in digital mammograms at the early stage. CAD systems help radiologists in identifying tumor patterns in an efficient and faster manner than other detection methods. In this paper, we propose a new approach for detecting tumors in mammograms using Radial Basis Function Networks (RBFNN). Prior...
In this work, we proposed a novel authentication system based on facial features. The proposed method is based on PCA and LDA for feature extraction, these extracted features are combined using wavelet fusion. In this work we use neural networks to classify extracted features of faces. The proposed method consists of six steps: i) Extraction of images from the database, ii) Preprocessing, iii) Feature...
Neighborhood preserving embedding (NPE) is a typical graph-based dimensionality reduction algorithm, which has been successfully applied in many practical problems such as face representation and recognition. NPE depends mainly on its underlying graph matrix which characters the local neighborhood reconstruction relationship between data points. However, the graph constructed in NPE merely utilizes...
Three kinds of rotation invariant image classification recognition algorithms based on texture characteristic are proposed. All the methods proposed are based on rotation-to-shift. First, the texture image is transformed by log-polar transform or Radon transform to convert the rotation to shift, then filter the transformed image using dual-tree complex wavelet transform(DT-CWT) or discrete stationary...
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.