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The high-probability ictus of cardiovascular diseases as well as the rapid proliferation of personal wearable devices points to the emergence of machine aided automatic identification and diagnosis with electrocardiograph (ECG). Most of existing algorithms of the identification of normal/abnormal ECG waveforms are computationally complicated and time-consuming, which cannot couple with the constant...
Recently, CNN (Convolutional Neural Network) based trackers have achieved promising results benefited from their robust feature representation. However, most trackers only use features from a certain layer, which limits their performance. In this paper, we propose a novel CNN based tracker. Firstly, we use local detection and global detection network for target localization. In local detection network,...
This paper presents a supervoxel-based approach for automated localization and extraction of street light poles in point clouds acquired by a mobile LiDAR system. The method consists of five steps: preprocessing, localization, segmentation, feature extraction, and classification. First, the raw point clouds are divided into segments along the trajectory, the ground points are removed, and the remaining...
Recently, graph ranking-based methods have been introduced to visual tracking and achieved promising results due to the local structure preserving property. However, existing graph ranking-based trackers use holistic templates to construct the graphs which makes the trackers sensitive to occlusions. In this paper, we propose a part-based multi-graph ranking algorithm for robust visual tracking. In...
This paper presents a framework for anomaly detection in videos which considers both motion and appearance features. For motion cues, we propose a new feature called 3D-HOF, which effectively extracts both velocity and orientation from the optical flow map. At the same time, we introduce the concept of “depth of field” problem to make the detection more accurate when the velocity of an object may...
Image retrieval and classification are hot topics in computer vision and have attracted great attention nowadays with the emergence of large-scale data. We propose a new scheme to use both deep learning models and large-scale computing platform and jointly learn powerful feature representations in image classification and retrieval. We achieve a superior performance on the ImageNet dataset, where...
This study investigates the use of a chest-worn wearable computer, the eButton, to assess physical performance of older adults. The Short Physical Performance Battery (SPPB), a standard cliniucal test, is first conducted on older human subjects. Then, a triaxial accelerometer and a triaxial gyroscope within the eButton are utilized to record acceleration and angular velocity of body motion on the...
Periodic transitions from place to place are inherent in human movements. Through visual examination we detect these periodic movements in traces of user tracking data. However such user tracking data sets tend to be sparse and incomplete. In addition, periodic movements are surrounded by noise: transitions to and from less frequently visited places and transitions to one of a kind visits. In this...
Almost all unbalanced classification algorithms focus on how to maximize the balance degree of the data set, which means to remove those negative samples that are useless for classifier training while keeping the positive samples and useful samples as many as possible. However, we find that the best balance degree is not necessary with the highest classification accuracy. In this paper, we propose...
Rotten and greasy coating on tongue is one of the most salient features reflecting inner health of body, which is widely used in observe diagnosis in Traditional Chinese Medicine (TCM). This paper is mainly concentrated on the classification of two tongue coating states: rotten and greasy. As it is an unbalanced classification and texture recognition problem, method of random oversampling, Gabor feature...
This paper proposes a simple and effective human action recognition algorithm based on projection and mass center movement features. Firstly, divide the original video into equal length subsequences with overlapping time window, and make moving parts detection using adjacent frame difference. Then, horizontal projection and vertical projection of binary image are made, and in order to get ride of...
Good feature extraction scheme and classifiers are the key to face recognition algorithms. A general and efficient face feature extraction approach is presented which utilizes linear discriminant information and global search strategy. In order to get rid of redundant information and meanwhile reduce computational burden, we first compute the nonzero feature space of scatter matrix of the training...
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