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This paper presents a study of line-wise text identification in comic books. Due to the unavailability of a single OCR system which can handle comic text of multiple scripts, the comic text identification based on script becomes an essential step for choosing the appropriate OCR. In this investigation, a new attempt has been made to explore a comic text identification technique of speech balloon to...
The classification of heartbeats using electrocardiogram (ECG) aiming arrhythmia detection is a well researched subject and still there are room for improvements concerning the recommended databases. In this sense, aiming to classify heartbeats for arrhythmia detection, we extend a previous ours proposal that uses vectorcardiogram, a bi-dimensional representation of two ECG leads, by incorporating...
In this paper, we propose a novel facial expression recognition method based on features of the motion, Facial Expression Recognition based on Motion Estimation (FERME). The proposed approach encodes the directional information of the facial expression. The facial motion is encoded by using the motion estimation between different images from the same (or similar) face. The facial expression image...
Classification of G protein-coupled receptors (GPCRs) according to their functions is an ongoing area of research which is helpful for the pharmaceutical industry in the development of drug targets for major diseases. Currently, more than 40% drugs in the market target GPCRs. The experimental methods of determining their function are very expensive and time consuming. Due to a rapid and constant increase...
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...
In order to solve the bottleneck of tedious and time-consuming manual labeling in singing voice detection, in this paper we integrate the active learning mechanism into the conventional SVM-based supervised learning algorithm. By selecting most informative unlabeled samples and asking for human annotation, active learning substantially reduces the number of training samples to be labeled and meanwhile...
To diagnose and classify the dysarthric speech, speech language pathologist (SLP) conducts a listening test. On the basis of the scores given by listeners the dysarthria is diagnosed and assessed. The above mentioned method is costly, time consuming and not very accurate. Unlike the traditional method, this research proposes an automatic diagnosis and assessment of dysarthria. The aim of this paper...
In this work, a new feature, residual sinusoidal peak amplitude (RSPA), is proposed for emotion classification. The RSPA feature is evaluated from the LP residual of the speech signal using sinusoidal model. Residual signal is a major source of the excitation and it is expected that emotional information can be well manifested in the residual signal. The effectiveness of the proposed feature is explored...
Classification between foggy and non-foggy images is a primitive step for automation in traffic activity and industries. The existing techniques provide low accuracy and needs validation over both synthetic and natural database. Foggy images are identified and classified based on their optical characteristics for vision enhancement and to make them more efficient for further processing. In proposed...
The research of facial beauty is an interdisciplinary topic involved in psychology, aesthetics, computer version and machine learning. In this paper, we propose several methods to assess facial beauty under unconstrained conditions. Our main works are as follows: First, we apply the local binary pattern (LBP) descriptor in different bins for face representation. We tried different types of LBP methods...
Prediction of seizures is a difficult problem as the EEG patterns are not wide-sense stationary and change from seizure to seizure, electrode to electrode, and from patient to patient. This paper presents a novel patient-specific algorithm for prediction of seizures in epileptic patients from either one or two single-channel or bipolar channel intra-cranial or scalp electroencephalogram (EEG) recordings...
The performance of a simple yet efficient local receptive field feature extractor is evaluated on state of the art handwritten databases showing that after the proper optimization of its parameters, very good accuracy performances can be obtained using a shallow classifier (e.g. the support vector machine), close to the ones achieved using more sophisticated techniques such as deep-learning classifiers...
A new technique to construct feature vector for gender classification is proposed in this paper. Here, new feature reduction technique is used to remove the irrelevant features of images. Feature reduction also helps in reducing the over fitting problem of the dataset. KPCA is a kernel based PCA which maps data from original space to non-linear feature space. Kernel trick helps in reducing the expensive...
Gait refers to the walking style of every individual person. Recent days gait is emerged as a supporting biometric using machine vision technique. This Work aims to develop a system capable of Human gait recognition by using model free approach. The gait database consists of silhouettes ie. outer frame of the human body. These silhouettes are affected by noises and discontinuities. So preprocessing...
In this paper, a new technique for constructing feature vector from DCT coefficients for gender classification has been presented. Firstly, images are divided into 8 × 8 sub images. DCT coefficients are calculated for each block in image. New technique is used for constructing the feature vector from DCT coefficients. Finally, SVM with Rbf kernel is used for classifying the images into male and female...
Acknowledgment of countenances with head stance and brightening change is an appealing and troublesome issue, and impacts fundamental applications in various zones, for instance, human-computer association and data driven development. Expelling the perfect segments from pictures is ceaselessly required in face affirmation figuring to achieve high exactness. In this paper, capable facial representation...
This paper introduces an empirical investigation which directly addresses and explores gender prediction capacity from digitised handwriting data from several different perspectives - such as feature type (static/dynamic) and content (fixed/variable) types - in order to provide extensive experimental evidence and analysis to guide the development of a better understanding of the opportunities for...
Fetal brain magnetic resonance imaging (MRI) is a rapidly emerging diagnostic imaging tool. However, automated fetal brain localization is one of the biggest obstacles in expediting and fully automating large-scale fetal MRI processing. We propose a method for automatic localization of fetal brain in 3T MRI when the images are acquired as a stack of 2D slices that are misaligned due to fetal motion...
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...
In this paper a new approach is proposed. The new approach is inspired by the compressive sensing theory used for data compression. Feature vectors from the facial data were extracted using compressive sensing technique. The compressive measurements were obtained at different feature vector lengths. Comparison of recognition rates with Local Binary Pattern (LBP) feature extractor were made using different...
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