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It has been estimated that autoimmune diseases are among the top ten leading causes of death among women in all age groups up to 65 years. However, the detection of it by indirect immunofluorescence (IIF) image analysis depends heavily on the experience of the physicians. An accurate and automatic Computer Aided Diagnosis (CAD) system will help greatly for the classification of the Human Epithelial...
Fish-eye cameras are efficient means to provide an omni-view video recording over a large area using a single camera. Although effective algorithms for human detection in images captured by conventional cameras have been developed, human detection in fish-eye images remains an open challenge. Recognizing that humans typically appear on radial lines emitted from the center in fish-eye images, we propose...
In this work we analyze the competitiveness of fuzzy rule-based systems in comparison with black box models like support vector machines to deal with fingerprint classification problems. With this aim, we carry out an experimental study applying different feature extraction models (covering almost every kind of features that are usually considered in this problem) and three fingerprint databases of...
Massive user generated content (UGC) videos are produced each day on the Internet. These videos have become a very important integrant in existing social networking services (SNS). However, unlike professional films, the content of UGC videos is usually unstructured and lacks contextual annotation for management. The motivation behind Huawei Accurate and Fast Mobile Video Annotation Challenge (MoVAC)...
Performance robustness of feature extraction with respect to environmental uncertainties is often critical for automated target detection & classification. This paper focuses on performance robustness in the sense that the extracted features are desired to be largely insensitive to environmental uncertainties, while they should be capable of recognizing the effects of small perturbations in the...
Flying bird detection (FBD) is important to avoid bird-aircraft collisions for aviation safety. It is a challenging task due to the wide variations in the appearance of flying birds. This paper describes a simple and efficient method to tackle the problem of FBD, which is based on a simplified bird skeleton descriptor. Since the skeletal structure that most flying birds possess is rather similar and...
The main objective of this work is to alleviate this problem by imposing the matching results from a classifier based on a set of constructed weighted templates to the boosting framework. The integration of global contour templates and local HOGs is through the adjustment of the hyperplane from the support vector machine. The concept behind is to bias the hyperplane and make it consistent with the...
The most crucial feature of human computer interaction is computers and computer-based applications to infer the emotional states of humans or others human agents based on covert and/or overt signals of those emotional states. In emotion recognition, bio-signals reflect sequences of neural activity induced by emotional events and also, have many technical advantages. The aim of this study is to classify...
Detection of vegetation in images is a common procedure in remote sensing and is commonly applied to satellite and aerial images. Recently it has been applied to images recorded from within ground vehicles for autonomous navigation in outdoor environments. In this paper we present a method for roadside vegetation detection intended for traffic safety and infrastructure maintenance. While many published...
Accurate detection of non-linguistic vocal events in social signals can have a great impact on the applicability of speech enabled interactive systems. In this paper, we investigate the use of random forest for vocal event detection. Random forest technique has been successfully employed in many areas such as object detection, face recognition, and audio event detection. This paper proposes to use...
Emotions are complex processes involving multiple response channels, including physiological systems, facial expressions and voices. Bio-signals reflect sequences of neural activity, which result in changes in autonomic and neuroendocrine systems induced by emotional events. Therefore in human-computer interaction researches, one of the most current interesting topics in emotion recognition is to...
Emotion Recognition is an important area of affective computing and has potential applications. This paper proposes a combinational model to compute the percentage of different emotions jointly present in a given speech input. This model is a weighted combination of the classifier models like Neural Network, k-Nearest Neighbors, Gaussian Mixture Model, Naïve Bayesian Classifier and Support Vector...
We target the problem of identifying the author of an Arabic text article. Our main aim is to develop an intelligent system that is capable of classifying a new article into one of seven classes that belong to seven different authors. For this purpose, we propose a novel dataset consisting of 12 features and 456 instances belonging to the 7 authors. In addition, we combine the proposed feature set...
Facial emotions are the most expressive way to display emotions. Many algorithms have been proposed which employ a particular set of people (usually a database) to both train and test their model. This paper focuses on the challenging task of database independent emotion recognition, which is a generalized case of subject-independent emotion recognition. The emotion recognition system employed in...
Training a system using a small number of instances to obtain accurate recognition/classification is a crucial need in document classification domain. The one-class classification is chosen since only positive samples are available for the training. In this paper, a new one-class classification method based on symbolic representation method is proposed. Initially a set of features is extracted from...
Sources such as speakers and environments from different communication devices produce signal variations that result in interference generated by different communication devices. Despite these convolutions, signal variations produced by different mobile devices leave intrinsic fingerprints on recorded calls, thus allowing the tracking of the models and brands of engaged mobile devices. This study...
This article presents a content-based image classification system to monitor the ripeness process of bell pepper (sweet pepper) via investigating and classifying the different maturity/ripeness stages. The proposed approach consists of three phases; namely pre-processing, feature extraction, and classification phases. Since the color of bell pepper surface is the most important characteristic to observe...
Much software lacks test oracles, which limits automated testing. Metamorphic testing is one proposed method for automating the testing process for programs without test oracles. Unfortunately, finding appropriate metamorphic relations for use in metamorphic testing remains a labor intensive task, which is generally performed by a domain expert or a programmer. We are investigating novel approaches...
In this paper, classifying and indexing video genres using Support Vector Machines (SVMs) are based on only audio features. In fact, those segmentation parameters are extracted at block levels, which have a major benefit by capturing local temporal information. The main contribution of our study is to present a powerful combination between the two employed audio descriptors Mel Frequency Cepstral...
In this letter, we propose to tackle rotation and scale variance in texture classification at the machine learning level. This is achieved by using image descriptors that interpret these variations as shifts in the feature vector. We model these variations as a covariate shift in the data. This shift is then reduced by minimising the Kullback–Leibler divergence between the true and estimated distributions...
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