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As different staining patterns of HEp-2 cells indicate different diseases, the classification of Indirect Immune Fluorescence (IIF) images on Human Epithelial-2 (HEp-2) cell is important for clinical applications. Different from traditional pattern recognition techniques, we use CNN to extract more high-level features for cell images classification. Compared to the existing CNN based HEp-2 classification...
Fish recognition and identification in an underwater environment are important research topics. In this study, several real-world underwater videos were collected to construct a fish category database for further fish recognition and identification. Recently, compressive sensing, using reconstruction algorithms to reconstruct a sparse signal, has been successfully applied to face recognition. Reconstruction...
Autism Spectrum Disorders (ASD) are often associated with specific atypical postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) interfere with learning and social interaction. Wireless inertial sensing technology offers a valid infrastructure for real-time SMM detection, whose automation would provide support for tuned intervention and possibly early alert on the onset of meltdown...
Java Enterprise Edition is composed of multilingual source code that supports the development of efficient business applications. This architecture is implemented as a layered model that is supported by a series of object oriented patterns called JEA/J2EE Patterns. These Patterns provide recurring solutions for the development of effective enterprise applications. J2EE Patterns can also be used for...
Iris recognition has proved to be one of the most reliable and stable biometric for human identification. This paper outlines an iris recognition approach based on deep learning. In addition, contour based feature vector has been used to discriminate samples belonging to different classes i.e. difference of sclera-iris and iris-pupil contours, and is named as “Unique Signature”. Moreover, contours...
Pattern recognition algorithms have been applied in the surface electromyography (sEMG) based hand motion recognition for their promising accuracy. Research on proposing new features, improving classifiers and their combinations has been extensively conducted in the past decade. Meanwhile, the feature projection methodology, has been routinely exploited between the phases of feature extraction and...
This paper presents a new feature extraction algorithm for the challenging problem of the classification of myoelectric signals for prostheses control. The algorithm employs the orientation between a set of descriptors of muscular activities and a nonlinearly mapped version of them. It incorporates information about the Electromyogram (EMG) signal power spectrum characteristics derived from each analysis...
Several conventional methods have been implemented in pattern recognition, but few of them have biological plausibility. This paper mimics the hierarchical visual system and uses the precise-spike-driven (PSD) synaptic plasticity rule to learn. The well-known HMAX model imitates the visual cortex and uses Gabor filter and max pooling to extract features. Compared with the traditional HMAX model, our...
Exploiting simple actions to recognize complex actions instead of using complex actions as training samples can save labor expenses and time consumption. Each complex action is composed of a sequence of simple actions and different manners of combinations of simple actions can form different complex actions. Thus, in this paper, we focus on temporal order information (TOI), which can be used to improve...
Nowadays multidimensional data as a part of Big Data are collected in every organization and feature selection is one of the main approaches in terms of processing them with machine learning methods. In this study, firstly, a Feature Selection based on Lorentzian Metric (FSLM) is developed. The proposed method unlike from matrix multiplication in Euclidean space uses the Lorentzian analogue to calculate...
In this paper, we introduce a new color texture operator for natural texture classification, the Dominant and Minor Sum and Difference Histograms (DM-SDH) descriptor. The proposed approach allows to incorporate both color and texture information in order to enhance the texture discrimination performance. For this purpose, a vectorial representation of the image is used for the descriptor extraction...
Selecting an adequate machine learning model, e.g. for feature selection or classification, is a very important task in developing machine learning applications. In order to perform an adequate selection, statistic tests are introduced by several approaches but some of them are hard to reproduce in different case studies due to the lack of a systematic application procedure. This work presents a methodological...
In this paper, four continuous moment-based feature extraction techniques for Synthetic Aperture Radar (SAR) images are examined. Geometric Moments (GMs), Legendre Moments (LMs), Zernike Moments (ZMs) and Pseudo Zernike Moments (PZMs) are introduced as a feature extraction for three types of ground vehicles from SAR images. GMs are simplest moment that suffers from high degree of information redundancy...
Vehicle detection can provide volumes of useful data for city planning and transport management. It has always been a challenging task because of various complicated backgrounds and the relatively small sizes of targets, especially in high resolution satellite images. A novel model called joint-layer deep convolutional neural networks (JLDCNNs), which joins features in the higher layers and the lower...
In this paper a novel approach for automatic bird species classification is described. The proposed strategy is based on features taken from the textural content of spectrogram images of bird vocalizations. We show how several texture descriptors can be used for representing the spectrograms. The following approaches are tested here with spectrograms for the first time: Local Ternary Phase Quantization,...
Wearable Devices (WD) are systems designed to do a specific task, these system are embedded in daily life personal objects. Usual transducers in wearable devices involve accelerometers gyroscopes, cameras etc. In WD is required to design efficiently in terms of power. Therefore, a new trend incorporates acoustic transducer that does not need a power supply to sense. They are very cheap and easy to...
The use of micro expressions as a means to understand ones state of mind has received major interest owing to the rapid increase in security threats. The subtle changes that occur on ones face reveals one's hidden intentions. Recognition of these subtle intentions by humans can be challenging as this needs well trained people and is always a time consuming task. Automatic recognition of micro expressions...
Pattern recognition control applied on surface electromyography (EMG) from the extrinsic hand muscles has shown great promise for the control of powered prosthetics for transradial amputees. The use of limb prostheses is essential for maintaining personal independence and a more effective inclusion in society. However, due to their poor control, imposed by the reduced accuracy of hand movement classification,...
Sleep Apnea is a potentially serious sleep disorder in which you have one or more pauses in breathing or shallow breaths while you sleep. It is classified into 3 main types: Obstructive sleep apnea, Central sleep apnea, and Complex sleep apnea syndrome. Obstructive sleep apnea (OSA) represents 80% of the apnea cases which makes it the most common type. Polysomnography is the current traditional method...
The pattern recognition system for biometric identification, which was presented in this paper, used mathematical and statistical approaches such as Principal Component Analysis as a feature extraction method also Cross Validation and k-nearest neighbor with Euclidean metric distance for the classification method. The proposed recognition system used face and androgenic hair as biometric traits with...
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