Search results for: Zhenyang Wu

Items from 1 to 20 out of 49 results

article

Single nucleotide polymorphisms in candidate genes are significantly associated with resistance to Haemonchus contortus infection in goats

Mahmuda Bilkis Bintee Alam, Abdullah Ibne Omar, Md. Omar Faruque, David Russell Notter, more

Journal of Animal Science and Biotechnology > 2019 > 10 > 1 > 1-15

Background Haemonchosis is a major economic problem in goat production in humid, tropical and subtropical regions. The disease is caused by an abomasal nematode, Haemonchus contortus, which is highly pathogenic in small ruminants. The aim of this study was to identifying single-nucleotide polymorphisms (SNP) that were associated with fecal egg counts (FEC) and could be used as markers to identify...

chapter

A smart-phone based hand-held indoor tracking system

Dihong Wu, Ao Peng, Lingxiang Zheng, Zhenyang Wu, more

2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN) > 1 - 7

2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

A smart-phone based hand-held indoor positioning system is presented in this paper. The system collects data using the accelerometers, gyroscopes, barometers and gravity sensors embedded in the smart-phone. The accelerometer and gravity data are used for zero-velocity detection and calculating the vertical displacement of each walking step, and then the inverted pendulum model is applied to calculate...

chapter

VTS feature compensation based on two-layer GMM structure for robust speech recognition

Lin Zhou, Haijing Li, Ying Chen, Zhenyang Wu, more

2016 8th International Conference on Wireless Communications & Signal Processing (WCSP) > 1 - 5

2016 8th International Conference on Wireless Communications & Signal Processing (WCSP)

In this paper, a two-layer Gaussian Mixed Model (GMM) structure for Vector Taylor Series (VTS) feature compensation is proposed for robust speech recognition. Since GMM with the numerous mixture components is used for VTS, the computation complexity of VTS is extremely huge. To deal with this issue, we propose two-layer GMM structure for VTS. In detail, the GMM with fewer mixture components is utilized...

chapter

Exploring encoding and normalization methods on probabilistic latent semantic analysis model for action recognition

Qinjun Xu, Tongchi Zhou, Lin Zhou, Zhenyang Wu

2016 8th International Conference on Wireless Communications & Signal Processing (WCSP) > 1 - 5

2016 8th International Conference on Wireless Communications & Signal Processing (WCSP)

Topic models have been wildly applied in the field of computer vision, through which superior performance was yielded in various recognizing tasks. Among them, probabilistic latent semantic analysis model has earned much attention due to its simplicity and effect. But the affection of encoding and normalization methods on topic models has been ignored during the period. This paper explores the impact...

article

Learning semantic context feature-tree for action recognition via nearest neighbor fusion

Tongchi Zhou, Nijun Li, Xu Cheng, Qinjun Xu, more

Neurocomputing > 2016 > 201 > C > 1-11

The spatio-temporal context learnt by the traditional methods for action recognition lacks the semantic meanings and temporal relationships. In order to deal with the drawbacks, we propose a novel semantic context feature-tree model to model the video clip for efficient human action recognition. The proposed method enforces spatio-temporal interest points (STIPs) within an irregular spatio-temporal...

chapter

Face Tracking Algorithm Based on Mean Shift and Ellipse Fitting

Jianpo Gao, Zhenyang Wu, Yujian Wang

Lecture Notes in Computer Science > Neural Information Processing > Face Analysis and Processing > 270-277

The mean shift algorithm is an efficient technique for object tracking. However, it has a shortcoming that it can’t adjust scale with object during tracking process. There are presently no effective ways to solve this problem. The kernel bandwidth of mean shift tracker in one frame is generally steered by the object scale obtained in the previous frame, so it is very important for mean shift tracker...

article

Recognizing human interactions by genetic algorithm-based random forest spatio-temporal correlation

Nijun Li, Xu Cheng, Haiyan Guo, Zhenyang Wu

Pattern Analysis and Applications > 2016 > 19 > 1 > 267-282

Recognizing human interactions is a more challenging task than recognizing single person activities and has attracted much attention of the computer vision community. This paper proposes an innovative and effective way to recognize human interactions, which incorporates the advantages of both global motion context (MC) feature and spatio-temporal (S-T) correlation of local spatio-temporal interest...

chapter

A study of relative motion point trajectories for action recognition

Tongchi Zhou, Nijun Li, Xu Cheng, Qinjun Xu, more

2015 International Conference on Wireless Communications & Signal Processing (WCSP) > 1 - 5

2015 International Conference on Wireless Communications & Signal Processing (WCSP)

Trajectories extracted by previous methods for human action recognition contain irrelevant changes, and the Orientation-Magnitude descriptors of their shapes lack the robustness to camera motion. To solve these problems, action recognition by tracking salient relative motion points is proposed in this paper. Firstly, motion boundary detector which suppresses the camera constant motion is utilized...

chapter

Action recognition by Huffman coding and implicit action model

Nijun Li, Tongchi Zhou, Lin Zhou, Zhenyang Wu

2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) > 1 - 6

2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)

Human action recognition is at the core of computer vision, and has great application value in intelligent human-computer interactions. On the basis of Bag-of-Words (BoW), this work presents a Huffman coding and Implicit Action Model (IAM) combined framework for action recognition. Specifically, Huffman coding, which outperforms naïve Bayesian method, is a robust estimation of visual words' conditional...

article

Object tracking via collaborative multi-task learning and appearance model updating

Xu Cheng, Nijun Li, Tongchi Zhou, Lin Zhou, more

Applied Soft Computing > 2015 > 31 > Complete > 81-90

In this paper, we propose a novel visual tracking algorithm using the collaboration of generative and discriminative trackers under the particle filter framework. Each particle denotes a single task, and we encode all the tasks simultaneously in a structured multi-task learning manner. Then, we implement generative and discriminative trackers, respectively. The discriminative tracker considers the...

article

Single-channel Speech Separation Using Dictionary-updated Orthogonal Matching Pursuit and Temporal Structure Information

Haiyan Guo, Xiaoxiong Li, Lin Zhou, Zhenyang Wu

Circuits, Systems, and Signal Processing > 2015 > 34 > 12 > 3861-3882

In this paper, we propose a two-stage sparse decomposition-based method for single-channel speech separation in time domain. First, we propose a Dictionary-updated orthogonal matching pursuit (DUOMP) algorithm which is used in both separation stages. In the proposed DUOMP algorithm, all atoms of each source-specific dictionary are updated by subtracting off the current approximation of each source...

chapter

Tracking deformable parts via dynamic conditional random fields

Suofei Zhang, Xu Cheng, Haiyan Guo, Lin Zhou, more

2014 IEEE International Conference on Image Processing (ICIP) > 476 - 480

2014 IEEE International Conference on Image Processing (ICIP)

Despite the success of many advanced tracking methods in this area, tracking targets with drastic variation of appearance such as deformation, view change and partial occlusion in video sequences is still a challenge in practical applications. In this paper, we take these serious tracking problems into account simultaneously, proposing a dynamic graph based model to track object and its deformable...

chapter

A Hybrid Method for Human Interaction Recognition Using Spatio-temporal Interest Points

Nijun Li, Xu Cheng, Haiyan Guo, Zhenyang Wu

2014 22nd International Conference on Pattern Recognition > 2513 - 2518

2014 22nd International Conference on Pattern Recognition (ICPR)

This paper proposes an innovative and effective hybrid way to recognize human interactions, which incorporates the advantages of both global feature (Motion Context, MC) and Spatio-Temporal (S-T) correlation of local Spatio-Temporal Interest Points (STIPs). The MC feature, which also derives from STIPs, is used to train a random forest where Genetic Algorithm (GA) is applied to the training phase...

article

Realistic human action recognition by Fast HOG3D and self-organization feature map

Nijun Li, Xu Cheng, Suofei Zhang, Zhenyang Wu

Machine Vision and Applications > 2014 > 25 > 7 > 1793-1812

Nowadays, local features are very popular in vision-based human action recognition, especially in “wild” or unconstrained videos. This paper proposes a novel framework that combines Fast HOG3D and self-organization feature map (SOM) network for action recognition from unconstrained videos, bypassing the demanding preprocessing such as human detection, tracking or contour extraction. The contributions...

article

Robust Visual Tracking with SIFT Features and Fragments Based on Particle Swarm Optimization

Xu Cheng, Nijun Li, Suofei Zhang, Zhenyang Wu

Circuits, Systems, and Signal Processing > 2014 > 33 > 5 > 1507-1526

We propose a novel approach for visual tracking based on a particle swarm optimization (PSO) framework using SIFT feature points correspondence and multiple fragments in a candidate target region to cope with the problems of partial occlusions, illumination changes, and large motion changes of the tracked target. Firstly, optimal search in the successive frame tracking process is performed by the...

article

Adaptive object detection by implicit sub-class sharing features

Suofei Zhang, Nijun Li, Xu Cheng, Zhenyang Wu

Signal Processing > 2013 > 93 > 6 > 1458-1470

In this paper, we describe an adaptive object detection system based on boosted weak classifiers. We formulate the learning of object detection as to find the representative sharing features over the examples of object. Objects with low intra-class variation imply that more generic features for detection can be selected. In contrast, more specific features for only subset of examples should be encouraged...

chapter

Recognizing human actions by BP-AdaBoost algorithm under a hierarchical recognition framework

Nijun Li, Xu Cheng, Suofei Zhang, Zhenyang Wu

2013 IEEE International Conference on Acoustics, Speech and Signal Processing > 3407 - 3411

ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

This paper explores the performance of Neural Network (NN) for human action recognition and proposes a novel hierarchical and boosting-based action recognition system. Specifically, the main contributions of our work are three-fold: (1) A boosted NN based scheme is applied to the human action recognition task for the first time, during which we extend the standard binary AdaBoost algorithm to a multiclass...

article

Maximum likelihood subband polynomial regression for robust speech recognition

Yong Lü, Zhenyang Wu

Applied Acoustics > 2013 > 74 > 5 > 640-646

In this paper, we propose a model adaptation algorithm based on maximum likelihood subband polynomial regression (MLSPR) for robust speech recognition. In this algorithm, the cepstral mean vectors of prior trained hidden Markov models (HMMs) are converted to the log-spectral domain by the inverse discrete cosine transform (DCT) and each log-spectral mean vector is divided into several subband vectors...

article

A fast search method of steered response power with small-aperture microphone array for sound source localization

Xiaoyan Zhao, Jie Tang, Lin Zhou, Zhenyang Wu

Journal of Electronics (China) > 2013 > 30 > 5 > 483-490

The Steered Response Power (SRP) method works well for sound source localization in noisy and reverberant environment. However, the large computation complexity limits its practical application. In this paper, a fast SRP search method is proposed to reduce the computational complexity using small-aperture microphone array. The proposed method inspired by the SRP spatial spectrum includes two steps:...

article

Sound source localization based on discrimination of cross-correlation functions

Xinwang Wan, Zhenyang Wu

Applied Acoustics > 2013 > 74 > 1 > 28-37

Sound source localization plays a crucial role in many microphone arrays application, ranging from speech enhancement to human–computer interface in a reverberant noisy environment. The steered response power (SRP) using the phase transform (SRP-PHAT) method is one of the most popular modern localization algorithms. The SRP-based source localizers have been proved robust, however, the methods may...

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INFONA - science communication portal

Search results for: Zhenyang Wu

Single nucleotide polymorphisms in candidate genes are significantly associated with resistance to Haemonchus contortus infection in goats

A smart-phone based hand-held indoor tracking system

VTS feature compensation based on two-layer GMM structure for robust speech recognition

Exploring encoding and normalization methods on probabilistic latent semantic analysis model for action recognition

Learning semantic context feature-tree for action recognition via nearest neighbor fusion

Face Tracking Algorithm Based on Mean Shift and Ellipse Fitting

Recognizing human interactions by genetic algorithm-based random forest spatio-temporal correlation

A study of relative motion point trajectories for action recognition

Action recognition by Huffman coding and implicit action model

Object tracking via collaborative multi-task learning and appearance model updating

Single-channel Speech Separation Using Dictionary-updated Orthogonal Matching Pursuit and Temporal Structure Information

Tracking deformable parts via dynamic conditional random fields

A Hybrid Method for Human Interaction Recognition Using Spatio-temporal Interest Points

Realistic human action recognition by Fast HOG3D and self-organization feature map

Robust Visual Tracking with SIFT Features and Fragments Based on Particle Swarm Optimization

Adaptive object detection by implicit sub-class sharing features

Recognizing human actions by BP-AdaBoost algorithm under a hierarchical recognition framework

Maximum likelihood subband polynomial regression for robust speech recognition

A fast search method of steered response power with small-aperture microphone array for sound source localization

Sound source localization based on discrimination of cross-correlation functions

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