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We propose a general approach towards feature extraction for identifying sonar targets based on their composition and geometry. The key idea is to discover the geometric connections between braid-like features within acoustic color topography that includes magnitude and phase information. Specifically, we characterize each target as a graph of intersecting braided features, detected across the complex-valued...
Accurate Human Epithelial-2 (HEp-2) cell image classification plays an important role in the diagnosis of many autoimmune diseases. However, the traditional approach requires experienced experts to artificially identify cell patterns, which extremely increases the workload and suffer from the subjective opinion of physician. To address it, we propose a very deep residual network (ResNet) based framework...
3D point cloud classification is an important task in applications for many areas such as robotics, urban planning and augmented reality. 3D sensors measure a high amount of points in the 3D scene objects' surface at a high collect rate, so robust techniques are needed to process all input data and also deal with some imprecision. A common solution for these tasks is the use of robust features extraction...
This study describes a method for using a camera to automatically recognize the speed limits on speed-limit signs. This method consists of the following three processes: first (1) a method of detecting the speed-limit signs with a machine learning method utilizing the local binary pattern (LBP) feature quantities as information helpful for identification, then (2) an image processing method using...
Extraction of relevant features from high-dimensional multi-way functional MRI (fMRI) data is essential for the classification of a cognitive task. In general, fMRI records a combination of neural activation signals and several other noisy components. Alternatively, fMRI data is represented as a high dimensional array using a number of voxels, time instants, and snapshots. The organisation of fMRI...
This study presents a cluster analysis framework for acoustic signals of the novel catheter stethoscope. The objective of the current study is to collect the blood flow sounds from a body site of a Yorkshire pig using the novel acoustic catheter stethoscope and further recognize any changes in the sinus rhythm patterns. Initially the collected blood flow sounds are preprocessed with noise cancellation...
During the recent years, there have been many studies implemented on the automatic diagnosis of Alzheimer's Disease (AD) using different methods. The focus of most of these studies has relied upon the detection of AD from neu-roimaging data. However, recognizing symptoms early as much as possible(Pre-detection) is crucial as disease modifying drugs will be most effective if administered early in the...
Pattern recognition scheme is used for discriminating various classes of hand motion with feature extracted from the surface electromyography signals. However, while using a relatively large feature set for classification process, the computational complexity increases tremendously. To overcome this, the paper implements feature selection technique using wrapper evaluation and four different search...
Embedded computer vision applications have been incorporated in industrial automation, improving quality and safety of processes. Such systems involve pattern classifiers for specific functions that, many times, demand high memory footprint and processing time. This work suggests a strategy to choose GLCM (Gray Level Co-occurrence Matrix) features for an SVM classifier that can reduce computer resources...
This paper aims at the classification of hand gestures using electromyographic signals (EMG) obtained through a MyoTM armband, which has eight medical grade electrodes. Each electrode provides information regarding muscles contraction performed during the execution of the movement. From these electrodes signals are extracted seven features for each one of eight electrodes. After extraction of the...
This paper aims to develop a framework for vehicle type classification using convolutional neural network based on vehicle rear view images. Compared with the extraction of the appearance features from vehicle side view and frontal view images, there has been relatively little research on vehicle type classification by using vehicle rear view images' information. The vehicle rear view images are detected...
Pattern recognition based on myoelectric speed control is critical for neural-controlled powered lower limb prostheses. We preliminarily investigated the performance of surface electromyography signals used to identify movement modes with different speeds. The pattern recognition was tested on electromyography data collected from five muscles of sixteen able-bodied subjects. The BP neural network...
Computer-assisted analysis of endoscopic imagescan be helpful to the automatic diagnosis and classificationof neoplastic lesions. Barrett's esophagus (BE) is a commontype of reflux that is not straightforward to be detected byendoscopic surveillance, thus being way susceptible to erroneousdiagnosis, which can cause cancer when not treated properly. In this work, we introduce the Optimum-Path Forest...
The logging and further analysis of borehole images is a major step in the interpretation of geological events. Natural fractures and beddings are features whose identification is commonly performed using acoustic and electrical borehole imaging tools. Such identification is a tedious task and is made visually by geologists, who must be experts on classification. The correct identification of planar...
Intramuscular Electromyography (EMG) signal provides a significant source of information that plays an inevitable role in the diagnosis of neuromuscular disorders. The ensemble method represents a supervised machine learning algorithm that constructs a combination of classifiers to achieve accurate classification decision. In this respect, the aim of this study is to propose classification method...
The idea of lip reading as a visual technique which people may use to translate lip movement into phrases without relying on speech itself is fascinating. There are numerous application areas in which lip reading could provide full assistance. Although there may be a downside to using the lip reading system, whether it may range from problems such as time constraint to minor word recognition mistakes,...
The typical method of entering a password for user authentication is vulnerable to hacking; therefore, various security technologies using bio-signals, such as iris scan, electrocardiography, electromyography (EMG), and fingerprint recognition, are being developed. In this research, an authentication algorithm using an EMG signal is proposed to supplement the weakness of personal certification techniques...
Upper limb prostheses controlled with Pattern Recognition (PR) and myoelectric signals have great promise for amputees who lost an upper limb since it can control large number of movements intuitively. One of the existing challenges with such PR systems include the need to develop new feature extraction techniques to facilitate clinical implementation of PR systems to satisfy amputees' needs. In this...
Myoelectric control with surface EMG signal has achieved great success in clinics, but only limited to the control of 2-Degrees-of-freedom prosthesis. With the appearance of multiple-channel and high-density EMG system and the advances of pattern recognition technology, it becomes possible to control a multi-degree smart prosthesis using EMG signals. However, it requires high performance EMG systems...
Palm vein recognition has emerged as a novelty highly invariant biometric technique that is difficult to forge due to their internal nature. In this work the texture descriptors Local Binary Patterns (LBP) and Uniform Local Binary Patterns (LBPU) are analyzed as feature extraction methods for biometric verification based on palm veins. Their performance and efficiency has been studied through a multivariate...
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