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Elderly care is one of the important issues in the medical mature of the time facing all over the world. Currently, the paper-based testing and recording methods are commonly used in various care centers which not only waste manpower but also may cause mistakes with different social workers in assessment. This study aims at measuring the cognitive function of elderly by the empirical research approach...
It's really important that how to keep cognitive functions of elderly. According to several research result shows, regular doing exercise or playing games may help elderly slowing down degeneration. In this study, we attempt to combine narrative concept and dynamic assessment theory to develop a kinect-based game for elderly on cognitive functions training. Moreover, a Web-based management system...
This paper proposes a real-time hand finger motion capturing method using Kinect. It consists of three modules: hand region segmentation, feature points extraction, and joint angle estimation. The first module extracts the hand region from the depth image. The second module applies a pixel classifier to segment the hand region into eight characteristic sub-regions and the residual sub-region. The...
Vehicle verification in two different views can be applied for Intelligent Transportation System. However, object appearance matching in two different views is difficult. The vehicle images captured in two views are represented as a feature pair which can be classified as the same/different pair. Sparse representation (SR) has been applied for reconstruction, recognition, and verification. However,...
This paper proposes a real-time abnormal behavior detection using Conditional Random Fields(CRFs). A normal behavior can be characterized by the spatial and temporal features obtained from the video of human activities. The difficult of abnormal behavior detection is that human behavior varies in both motion and appearance. It is a continuous action stream, interspersed with transitional activities...
We present an image classification method which consists of salient region (SR) detection, local feature extraction, and pairwise local observations based Naive Bayes classifier (NBPLO). Different from previous image classification algorithms, we propose a scale, translation, and rotation invariant image classification algorithm. Based on the discriminative pairwise local observations, we develop...
This paper presents a single sample per person (SSPP)-based face recognition method. Based on the Discriminative Multi-manifold Analysis (DMMA), we propose an accelerative face recognition method which consists of three modules. First, for one person one training image sample, we use a modified of K-means method to cluster two groups of people. Second, we divide the face images into non-overlapping...
This paper proposes a real-time upper human motion capturing method to estimate the positions of upper limb joints by using Kinect. For human articulated motion capturing, the body part self-occlusion is a nontrivial problem. The system consists of hybrid action type recognition, body part segmentation, and offset compensation. The hybrid action type classifier consists of Adaboost and Random Forest...
Traffic sign recognition is difficult due to the low resolution of image, illumination variation and shape distortion. On the public dataset GTSRB, the state-of-the-art performance have been obtained by convolutional neural networks (CNNs), which learn discriminative features automatically to achieve high accuracy but suffer from high computation costs in both training and classification. In this...
In overlaid handwriting, multiple characters are written sequentially in the same area. This needs special consideration for segmenting the stroke sequence into characters. We propose a learning-based model for scoring the candidate stroke cuts and segments for online overlaid Chinese handwriting recognition. Based on stroke cut classification using support vector machine (SVM), strokes are grouped...
Use of a linear projection (LP) function to transform multiple sets of acoustic models into a single set of acoustic models is proposed for characterizing testing environments for robust automatic speech recognition. The LP function is an extension of the linear regression (LR) function used in maximum likelihood linear regression (MLLR) and maximum a posteriori linear regression (MAPLR) by incorporating...
Limited historical data and large fluctuations are two important issues for forecasting time series. In this paper, a hybrid forecasting model based on adaptive fuzzy time series and particle swarm optimization is proposed to address these issues. In the training phase, the heuristic rules automatically adapt the forecasted values based on trend values and the particle swarm optimization is applied...
Real-time segmentation of scene into foreground and background is an important issue for many applications. Different from previous codebook (CB) methods, this paper introduces a hybrid CB model by combining the mixture of Gaussian (MOG) method and the CB method. It can be used to solve the problems of moving background and shadow/highlight on the background and background. Our method avoids extracting...
Biometric fusion is an essential procedure in any multi-modal biometric person recognition systems and it can be performed at sensor, feature, score and decision levels. This paper proposes a simulated annealing (SA) algorithm for the fusion of multi-modal biometric data. This method is applied to an Audio-Visual (AV) person recognition database that includes acoustic and visual information. Its superior...
The main purpose of this research is to develop an intelligent quality prediction system. We are proposing a parameter of quality prediction module that selected 12 factors from MEPH and 16 quality evaluation factors of manufacturing system. By using back-propagation neural network method, the module is built up. The module could be used by calculating the quality evaluation from the quality factors...
Although S1000D specification has made theory of information classification and coding, this paper takes the new way of analyzing object to make information coding. Firstly, establishing mapping between data module and analyzed product. Secondly, procuring SNS and DC/DCV of DMC according to product hierarchy and object needed code. Finally, taking bicycle coding for example to verify this paper's...
This paper presents a UBM data selection method for robust training. We know that there is no promise that more training data guarantee better results. Therefore, the way of sub-sampling and effective training become important. The proposed method uses the feature vector selection with the maximum-entropy criterion. The maximum-entropy shows the diverse characters of speaker and minimum redundant...
We present a topic mixture language modeling approach making use of the soft classification notion of topic models. Given a text document set, we first perform document soft classification by applying a topic modeling process such as probabilistic latent semantic analyses (PLSA) or latent Dirichlet allocation (LDA) on the dataset. Then we can derive topic-specific n-gram counts from the classified...
Most learning-based video semantic analysis methods require a large training set to achieve good performances. However, annotating a large video is laborintensive. This paper introduces how to construct the training set and reduce user involvement. There are four selection schemes proposed: clustering-based, spatial dispersiveness, temporal dispersiveness, and sample-based which can be used construct...
We present a semi-supervised learning (SSL) method for building domain-specific language models (LMs) from general-domain data using probabilistic latent semantic analysis (PLSA). The proposed technique first performs topic decomposition (TD) on the combined dataset of domain-specific and general-domain data. Then it derives latent topic distribution of the interested domain, and derives domain-specific...
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