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We present a revised pipe-line of the existing 3D object detection and pose estimation framework based on point pair feature matching. This framework proposed to represent 3D target object using self-similar point pairs, and then matching such model to 3D scene using efficient Hough-like voting scheme operating on the reduced pose parameter space. Even though this work produces great results and motivated...
Several techniques aim to classify human activity using data from sensors e.g., GPS, accelerometer, Wi-Fi and GSM. The sensor data allow inferring transportation modes as car, bus, walk, and bike. Despite some techniques show improvements in accuracy, researchers constantly deal with issues such as over-segmentation and low precision in trip reporting. Journeys are over-segmented due to the ambiguous...
This paper proposes an effective method to construct a new descriptor for solving face recognition with pose variance. In this method, we use feature of nose and HAT-MARS descriptor to build a combined descriptor for matching between gallery and probe images. For experiments, our method uses only one gallery image on CMU-PIE database. The empirical results show that our method is strong and efficient.
Human activity recognition is one of the most important core building blocks behind many applications on smartphone such as medical applications, fitness tracking, context-aware mobile, human survey system, etc. This paper describes a robust system for human activity recognition by smartphone. Different from other work, we investigated the use and combination feature selection and instance selection...
Brain signals arise as a mixture of various neural processes that occur in different spatial, frequency and temporal locations. In detection paradigms, algorithms are developed that target specific processes. In this work, we apply tensor factorisation to a set of intracranial electroencephalography data from a group of epileptic patients and factorise the data into three modes; space, time and frequency...
Sleep apnea is a sleep-related breathing disorder that involves a decrease or complete halt in airflow despite an ongoing effort to breathe. The most common form of sleep apnea is well known as Obstructive sleep apnea (OSA) which is currently diagnosed using polysomnography (PSG) at sleeping labs. This diagnostic technique is both expensive and inconvenient. It requires an expert human to observe...
Due to heavy clutters and occlusions of complex background, natural images contain complex features in data structure which often cause errors in image classification. In this paper, we propose semi-supervised bi-dictionary learning for image classification with smooth representation-based label propagation (SRLP) which extends reconstruction-based classification in a probabilistic manner. First,...
In this letter, a simple, yet very powerful local descriptor called local pattern descriptor (LPD) is proposed for synthetic aperture radar (SAR) images classification. The descriptor aims at exploiting the underlying properties of SAR image texture. Specifically, LPD consists of two parts: image quantization and statistical features extraction. The method of image quantization is based on recent...
Parkinson's disease (PD) is a disorder of the central nervous system and about 89% of the people with PD suffering from speech and voice disorders. In this paper, we adopted a dynamic feature selection based on fuzzy entropy measures for speech pattern classification of Parkinson's diseases. To investigate the effect of feature selection, Linear Discriminant Analysis (LDA) was applied to distinguish...
In this paper, a new method for ECG biometric recognition using a hierarchical scheme classifier is presented. The integral process of the method is introduced, including preprocessing, feature extraction and classification. To achieve a better performance of proposed method, cross-validation is applied to determine the parameters in the classifier. As a result, proposed method offers considerably...
Traffic identification technique is used for classification of different network protocols and applications even with detection of users' network activities. In this paper, we conduct our study on some typical users' network activities and present a traffic identification method to describe the feature about users' behaviors. We convert users' network activities information into different sequences...
This paper proposes a feature-based technique to detect pedestrians and recognize vehicles within thermal images that have been captured during nighttime. The proposed technique applies the support vector machine (SVM) classifier on CENsus Transformed histogRam Oriented Gradient (CENTROG) features in order to classify and detect humans and/or vehicles. Although thermal images suffer from low image...
Undesirable emails (spam) are increasingly becoming a big problem nowadays, not only for users, but also for Internet service providers. Therefore, the design of new algorithms detecting the spam is currently one of the research hot-topics. We define two requirements and use them simultaneously. The first requirement is a low rate of falsely detected emails which has an impact on the algorithm performance...
Starting from the last century, animals identification became important for several purposes, e.g. tracking, controlling livestock transaction, and illness control. Invasive and traditional ways used to achieve such animal identification in farms or laboratories. To avoid such invasiveness and to get more accurate identification results, biometric identification methods have appeared. This paper presents...
We present newly added modules for our autonomous load handling system. The stereo camera system provides information about the scene in front of the automated forklift. A new alternative module for pallet detection is described. Several processing modules for unloading operations are also presented. Our system is evaluated by means of detection rate and by performing field tests. The tests show that...
Previous polarimetric synthetic aperture radar (PolSAR) images change detection methods are generally undertaken in the pixel scale, resulting in overlooking the semantic information. To solve this problem, this paper presents a superpixel-based PolSAR images change detection methods. Different from some previous methods, an improved SLIC superpixel segmentation method is introduced in polarimetric...
Feature selection and learning through selected features are the two steps that are generally taken in classification applications. Commonly, each of these tasks are dealt with separately. In this paper, we introduce a method that optimally combines feature selection and learning through feature-based models. Our proposed method implicitly removes redundant and irrelevant features as it searches through...
Cry segmentation is an essential preprocessing step in any infant crying diagnosis system. Besides crying sounds consisting of expiration phases followed by short periods of inspiration episodes, each recording of newborn cries also includes silence sections as well as other sounds such as speech of caregivers, noise and sound of medical equipments. This paper is devoted to a newly developed Empirical...
Uni-modal analysis of palmprint and palmvein has been investigated for human recognition. One of the problems encountered with such system is that the Uni-modal biometric is less perfect, reliable and vulnerable to spoofing, as the data can be imitated or forged. In this paper, we present a multi-modal Personal identification system using palmprint and palmvein images with their fusion applied at...
A method for sentiment polarity assignment for textual content written in Polish using supervised machine learning approach with transfer learning scheme is proposed in the paper. It has been shown that performing simple natural language processing steps prior to classification, provides inspiring results without redundant computation overhead. The documents containing subjective opinions were classified...
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