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Semantic image inpainting is a challenging task where large missing regions have to be filled based on the available visual data. Existing methods which extract information from only a single image generally produce unsatisfactory results due to the lack of high level context. In this paper, we propose a novel method for semantic image inpainting, which generates the missing content by conditioning...
Pedestrian detection, as an important task in video surveillance and forensics applications, has been widely studied. However, its performance is unsatisfactory especially in the low resolution conditions. In realistic scenarios, the size of pedestrians in the images is often small, and detection can be challenging. To solve this problem, this paper proposes a novel resolution-score discriminative...
This paper presents an approach to detect and recognize actions of interest in real-time from a continuous stream of data that are captured simultaneously from a Kinect depth camera and a wearable inertial sensor. Actions of interest are considered to appear continuously and in a random order among actions of non-interest. Skeleton depth images are first used to separate actions of interest from actions...
We propose a novel channel estimation technique for intensity modulation/direct detection (IM/DD) based orthogonal frequency division multiplexing visible light communication (OFDM-VLC) systems, utilizing sparse Bayesian dual-variate relevance vector machine (RVM) regression. By exploiting sparse Bayesian framework, dual-variate RVM regression can provide accurate estimation of the real and imaginary...
Metric learning is an important issue in person re-identification, and Mahalanobis-distance based metric learning methods prevail in this field. All of these approaches can be considered as equivalently projecting all samples to a new metric space and calculating the Euclidean distance there. However, the performance of distinguishing similar samples from dissimilar ones via absolute distance is limited...
In this paper, we propose an indoor positioning system (IPS) that achieves centimeter accuracy in a complex indoor environment using time-reversal (TR) technique with a single pair of off-the-shelf multi-antenna WiFi devices. The proposed IPS can work under both line-of-sight (LOS) and non-line-of-sight (NLOS) environment. Leveraging the spatial diversity on the multi-antenna WiFi device, the proposed...
Hybrid digital/analog precoding has been widely utilized in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. Compared to the digital precoder, it is more challenging to design the analog precoder because of the constant amplitude (CA) constraints on the entries. Although high-resolution analog phase shifters (APSs) can provide more choices in phase adjustment, it also adds the...
Millimeter wave (mmWave) communication is favored for its abundant bandwidth. However, the sampling rate scales up with the large bandwidth, thus making it difficult to implement high-resolution analog-to-digital converters (ADCs). In addition, a large-scale array is employed to overcome the high path loss of the mmWave channels. Due to the large number of antennas and the directional feature of the...
Fault diagnosis is an important procedure to ensure the equipment efficiency and stability. The diagnosis process is actually a pattern recognition process, and usually, the fault samples are lack of tags of fault types. In this case, the non-supervised learning method is more available, and kernel clustering is one of the most effective methods. In this paper, a novel electromagnetic particle swarm...
This paper presents a novel extended multi-structure local binary pattern (EMSLBP) approach for high-resolution image classification, generalizing the well-known local binary pattern (LBP) approach. In the proposed EMSLBP approach, three-coupled descriptors with multi-structure sampling are proposed to extract complementary features (pixel value and radial difference) from local image patches. The...
With the popularization of large-complex machinery, the maintenance personnel demand of such equipment is growing as well. Traditional way of training equipment maintenance means large cost of space and funds. We design an equipment maintenance training system based on Web3D technology. Compared with the traditional way, the virtual maintenance teaching system based on virtual reality technology can...
Spatial information has been verified to be helpful in hyperspectral image classification. In this paper, a spatial feature extraction method utilizing spatial and orientational auto-correlations of image local gradients is presented for hyperspectral imagery (HSI) classification. The Gradient Local Auto-Correlations (GLAC) method employs second order statistics (i.e., auto-correlations) to capture...
This paper, for the first time, introduces a multiple-class boosting scheme (MBS) to combine depth motion maps (DMMs) and completed local binary patterns (CLBP) for action recognition. DMMs derive from projecting depth frames onto three orthogonal Cartesian planes (front, side and top) and characterize the motion energy of an action, on which the CLBP features are further extracted. And then a new...
This paper presents a computationally efficient method for action recognition from depth video sequences. It employs the so called depth motion maps (DMMs) from three projection views (front, side and top) to capture motion cues and uses local binary patterns (LBPs) to gain a compact feature representation. Two types of fusion consisting of feature-level fusion and decision-level fusion are considered...
In this paper, we present an efficient and robust image representation method that can handle misalignment, occlusion and big noises with lower computational cost. It is motivated by the sub-selection technique, which uses partial observations to efficiently approximate the original high dimensional problems. While it is very efficient, their method can not handle many real problems in practical applications,...
This paper presents a medication adherence monitoring system for pill bottles based on a wearable inertial sensor. Signal templates corresponding to the two actions of twist-cap and hand-to-mouth are created using a camera-assisted training phase. The act of pill intake is then identified by performing a moving window dynamic time warping in real-time between signal templates and the signals acquired...
Diabetes, as the 4th death cause, has become a vital issue in the 21th century. However, blood sugar levels of most diabetic patients are not well controlled. For a patient with chronic disease, treatment efficiency could be influenced by the disease, remedy, and mental condition, in addition to his/her physiological status. Therefore, there is a great deal of difficulty in establishing a guideline...
The prediction of stock markets is an important and widely research issue since it could be had significant benefits and impacts, and the fuzzy time-series models have been often utilized to be the forecast models to make reasonably accurate predictions. For promoting the forecasting performance of fuzzy time-series models, this paper proposed a new model, which incorporates the concept of the equal-frequency...
Orthogonal frequency division multiplexing (OFDM) combining with the coordinated multi-point (CoMP) transmission technique has been proposed to improve performance of the receivers located at the cell border. However, the inevitable carrier frequency offset (CFO) will destroy the orthogonality between subcarriers and induce strong inter-carrier interference (ICI) in OFDM systems. In a multi-point...
Given a training set of images and a binary classifier, we introduce the notion of an exaggerated image stereotype for some image class of interest, which emphasizes/exaggerates the characteristic patterns in an image and visualizes which visual information the classification relies on. This is useful for gaining insight into the classification mechanism. The exaggerated image stereotypes results...
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