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This paper addresses the problem of automatic target recognition (ATR) using inverse synthetic aperture radar (ISAR) images. In this context, we propose a novel approach for feature extraction to describe precisely an aircraft target from ISAR images. In our approach, a visual attention model is adopted to separate the salient regions from the background. After that, the scale invariant feature transform...
Nowadays, there are significant amounts of malware codes that are created every day. However, the majority of these samples (malware) are variations of other malware that have been already identified. Therefore, most of the analyzed malware have similar structure among them. In this investigation, we will present a technic to extract features throughout different abstraction levels in order to classify...
In this paper, a new multi-dimensional facial recognition system is proposed. A new technique for data reduction for multidimensional biometric facial analysis to improve face recognition performance in real environments is implemented. For this the tensorial methods are adopted, the sample of the face must be reshaped by natural tensor representations into vectors of very large dimensions. This remodeling...
The lung cancer is the reason of a lot of deaths on population around the world. An early diagnosis brings a most curable and simpler treatment options. Due to complexity diagnosis of small pulmonary nodules, Computer-Aided Diagnosis (CAD) tools provides an assistance to radiologist aiming the improvement in the diagnosis. Extracting relevant image features is of great importance for these tools....
in recent years, digital painting collections are available to the public and this is growing in museums digital galleries. With the availability of large collections of digital, it is essential to develop multimedia systems for archiving and retrieving them. Recognition the style of each artist is one of the key issues, however, most artists do not identify their styles. Traditionally, people empirically...
In this paper, we propose techniques for detecting anomalies in user accesses by learning profiles of normal access patterns of users based on both the syntactic and semantic features of past users queries stored in database logs. New accesses are checked upon these profiles and deviations are considered anomalous accesses which may be indications of potential insider attacks. We consider two scenarios...
Image classification is a fundamental problem in computer vision and pattern recognition. Feature extraction is often regarded as the key for classifying images. Traditional ways rely on handcrafted features heavily, such as SIFT and BoW. In this paper, we concentrate on recognizing some specific categories of images (e.g. adult content and political images) in Email. And most importantly we propose...
Feature extraction and classification are two essential components in face recognition. Feature extraction is a process to reduce the original input high-dimensional data and reserve the crucial information. Considering the problem that the human face image is high-dimensional, dimensionality reduction (DR) methods can be employed to obtain low-dimensional data for recognition. Eigenspace-based method...
Electrical activity in the heart is given by electrocardiogram (ECG) signal. Manual analysis of ECG beat is very time consuming task as it may contain hundreds of thousands of beats for 24 hours of ECG signal. This study gives a robust classification model for ECG using Rough Set Theory (RST). RST generates rules which are simple and more apprehensible for the user causing the extraction of more accurate...
Many technical papers and scientific studies were written about nowadays mobile devices popularity. Also biometrics and especially the face recognition belong already to standard authentication methods. However, the face is not the only one human feature, which contains unique data about person. Shape and folds of a human ear could also be used for reliable identification of people. Information obtained...
In this study, gender prediction is investigated for the face images. To extract the features of the images, Local Binary Pattern (LBP) is used with its different parameters. To classify the images male or female, K-Nearest Neighbors (KNN) and Discriminant Analysis (DA) methods are used. Their performances according to the LBP parameters are compared. Also classification methods' parameters are changed...
The major objective of mobility management is to achieve efficient contents transfer for mobile nodes. Content Centric Networking (CCN), as a content-oriented architecture, is designed for efficient content delivery which can easily provide mobility management based on content caching. Caching schemes of CCN deeply influences the content acquisition efficiency and whole network performance. However,...
Facial expression recognition is the most important criteria for effective Human Computer Interaction (HCI) as well as a medium to understand and communicate with children who cannot emote verbally. In this paper, we propose a feature extraction technique by embedding 2D-LDA and 2D-PCA. The features extracted were then tested on standard classifiers i.e., Support Vector Machine (SVM) and K-Nearest...
This paper presents a new statistical model for describing real textured images. Our model is based on the observation that the Scale-Invariant Feature Transform (SIFT) descriptors extracted from a given image can be properly modeled by the Gamma distribution. The maximum-likehood algorithm was used to estimate the two parameters of the Gamma distribution. The efficiency of the proposed approach was...
The use of pattern recognition and classification has increased in various real world applications such as face recognition and other crucial applications. The key aim, of these applications, is to automate the various complicated task. This paper presents the face recognition application and their investigation. The investigation of the face recognition leads to find some of the most optimum approaches...
Facial expression carries information about the emotional and physical state of a human being. In the past few years facial expression recognition has become a promising research area. With the developing robotic era the systems needs intelligence to understand human's behavior. Expression recognition is applied and useful from emoticons to disease detection in medical field. Apart from automated...
Here, we propose a method for recognition of handwritten English digit utilizing discrete cosine space-frequency transform known as the Discrete Cosine S-Transform (DCST). Experiments have been conducted on the publicly availabe standard MNIST handwritten digit database. The DCST features along with an Artificial Neural Network (ANN) classifier is utilized for solving the classification issues of...
Palm print is one of the important hand related biometrics characteristics with high user acceptance. A new classification approach using heart line feature of palm print is proposed in this paper. The hand image captured from digital camera is preprocessed to find palm print Region of interest (ROI). Gabor filters are applied on palm print ROI and line detection operation is proposed to extract heart...
Information about the emotional state of a person can be inferred from facial expressions. Emotion recognition has become an active research area in recent years in various fields such as Human Robot Interaction (HRI), medicine, intelligent vehicle, etc., The challenges in emotion recognition from images with pose variations, motivates researchers to explore further. In this paper, we have proposed...
In this paper, we present our work on speech-smile/shaking vowels classification. An efficient classification system would be a first step towards the estimation (from speech signals only) of amusement levels beyond smile, as indeed shaking vowels represent a transition from smile to laughter superimposed to speech. A database containing examples of both classes has been collected from acted and spontaneous...
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