The early contact less detection of viral pneumonia is important as the virus have the ability to mutate and adapt frequently resulting in an epidemic situation or potential pandemic in a short time. This work unveils...
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The inconceivable volume of online images produced by websites and personal collections has made it difficult to retrieve images from vast databases accurately. Practically, consumers need help to get the relevant inf...
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This paper proposes the design and the validation through in-vivo measurements, of an innovative machine learning (ML) approach for a synchronous Brain Computer Interface (BCI). The here-proposed system analyzes EEG s...
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ISBN:
(纸本)9783030295134;9783030295127
This paper proposes the design and the validation through in-vivo measurements, of an innovative machine learning (ML) approach for a synchronous Brain Computer Interface (BCI). The here-proposed system analyzes EEG signals from 8 wireless smart electrodes, placed in motor, and sensory-motor cortex area. For its functioning, the BCI exploits a specific brain activity patterns (BAP) elicited during the measurements by using clinical-inspired stimulation protocol that is suitable for the evocation of the Movement-Related Cortical Potentials (MRCPs). The proposed BCI analyzes the EEGs through symbolization-based algorithm: the local binary patterning, which - due to its end-to-end binary nature - strongly reduces the computational complexity of the features extraction (FE) and real-time classification stages. As last step, the user intentions discrimination is entrusted to a weighted Support Vector Machine (wSVM) with linear kernel. The data have been collected from 3 subjects (aged 26 +/- 1), creating an overall dataset that consists of 391 +/- 106 observations per participant. The in-vivo real-time validation showed an intention recognition accuracy of 85.61 +/- 1.19%. The overall computing chain requests, on average, just 3 ms beyond the storage time.
local binary pattern from three orthogonal planes (LBPTOP) has been widely used in emotion recognition in the wild. However, it suffers from illumination and pose changes. This paper mainly focuses on the robustness o...
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ISBN:
(纸本)9781450328852
local binary pattern from three orthogonal planes (LBPTOP) has been widely used in emotion recognition in the wild. However, it suffers from illumination and pose changes. This paper mainly focuses on the robustness of LBP-TOP to unconstrained environment. Recent proposed method, spatiotemporal local monogenic binarypattern (STLMBP) [14], was verified to work promisingly in different illumination conditions. Thus this paper proposes an improved spatiotemporal feature descriptor based on STLMBP. The improved descriptor uses not only magnitude and orientation, but also the phase information, which provide complementary information. In detail, the magnitude, orientation and phase images are obtained by using an effective monogenic filter, and multiple feature vectors are finally fused by multiple kernel learning. STLMBP and the proposed method are evaluated in the Acted Facial Expression in the Wild as part of the 2014 Emotion Recognition in the Wild Challenge. They achieve competitive results, with an accuracy gain of 6.35% and 7.65% above the challenge baseline (LBP-TOP) over video. Copyright 2014 ACM.
Face is a very unique part of human body which consists unique features for every individual who has distinguishes an individual is their face. The facial recognition system can be developed by using facial landmarks ...
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Background The most common cancer affecting women globally is breast cancer. The most effective and extensively used tool for identifying breast abnormalities in the early stage is mammography screening. However, it i...
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Since the tremendous advancement of face recognition technology, how to detect real human faces from images has gradually captured the attention of researchers. To address the problem that most face anti-spoofing appr...
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In this present work, a new technique for content-based image retrieval is introduced using local tetra-directional pattern. In conventional local binary pattern (LBP), each pixel of an image is changed into a specifi...
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In this present work, a new technique for content-based image retrieval is introduced using local tetra-directional pattern. In conventional local binary pattern (LBP), each pixel of an image is changed into a specific binarypattern in accordance with their relationship with neighbouring pixels. Texture feature descriptor introduced in this work differs from local binary pattern as it exploits local intensity of pixels in four directions in the neighbourhood. Also, colour feature and gray level co-occurrence matrix have been applied in this work. Median of images have also been taken under consideration to keep the edge information preserved. The proposed technique has been validated experimentally by conducting experiments on two different sets of data, viz., Corel-1K and AT&T. Performance was measured using two well-known parameters, precision and recall, and further comparison was carried with some state-of-the-art localpatterns. Comparison of results show substantial improvement in the proposed technique over existing methods.
This paper proposes the design and the implementation of an innovative algorithm for a 2-choices synchronous Brain-Computer Interface (BCI). The proposed BCI operates on signals from eight EEG channels evenly distribu...
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ISBN:
(纸本)9781728105567
This paper proposes the design and the implementation of an innovative algorithm for a 2-choices synchronous Brain-Computer Interface (BCI). The proposed BCI operates on signals from eight EEG channels evenly distributed along the sensorimotor area. The acquired EEGs are then analyzed by using a symbolization method. Typically, the symbolization includes data-analysis algorithms that translate physical processes from experimental measurements into a series of discrete symbols (e.g., bit strings). For the BCI application, the chosen symbolization algorithm is the local binary pattern (LBP). Since the selected LBP method uses binary operations for the whole processing chain (end-to-end), the complexity and the computing timing of the features extraction (FE) and real-time classification stages have been strongly reduced. Finally, a time-continuous Support Vector Machine (tcSVM) classifies the LBP-extracted features. The here proposed BCI algorithm has been validated on 3 subjects (aged 26 +/- 1), who underwent a stimulation protocol oriented to Movement-Related Potentials (MRPs) elicitation. The in-vivo validation showed how the system is able to reach an intention recognition accuracy of 85.61 +/- 1.19 %. In addition, starting from the complete data storage, the whole implemented computing chain asks, on average, for just similar to 3ms to provide the classification. As a proof of concept, the tcSVM outcomes have been used to drive, via Bluetooth, a 3D printed robotic hand.
Ground-based cloud classification is challenging due to extreme variations in the appearance of clouds under different atmospheric conditions. Texture classification techniques have recently been introduced to deal wi...
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Ground-based cloud classification is challenging due to extreme variations in the appearance of clouds under different atmospheric conditions. Texture classification techniques have recently been introduced to deal with this issue. A novel texture descriptor, the salient local binary pattern （SLBP）, is proposed for ground-based cloud classification. The SLBP takes advantage of the most frequently occurring patterns （the salient patterns） to capture descriptive information. This feature makes the SLBP robust to noise. Experimental results using ground-based cloud images demonstrate that the proposed method can achieve better results than current state-of-the-art methods.
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