Pose-invariant facial expression recognition(FER)is an active but challenging research topic in computer vision.Especially with the involvement of diverse observation angles,FER makes the training parameter models inc...Pose-invariant facial expression recognition(FER)is an active but challenging research topic in computer vision.Especially with the involvement of diverse observation angles,FER makes the training parameter models inconsistent from one view to another.This study develops a deep global multiple-scale and local patches attention(GMS-LPA)dual-branch network for pose-invariant FER to weaken the influence of pose variation and selfocclusion on recognition accuracy.In this research,the designed GMS-LPA network contains four main parts,i.e.,the feature extraction module,the global multiple-scale(GMS)module,the local patches attention(LPA)module,and the model-level fusion model.The feature extraction module is designed to extract and normalize texture information to the same size.The GMS model can extract deep global features with different receptive fields,releasing the sensitivity of deeper convolution layers to pose-variant and self-occlusion.The LPA module is built to force the network to focus on local salient features,which can lower the effect of pose variation and self-occlusion on recognition results.Subsequently,the extracted features are fused with a model-level strategy to improve recognition accuracy.Extensive experimentswere conducted on four public databases,and the recognition results demonstrated the feasibility and validity of the proposed methods.展开更多
Modem product development becomes increasingly collaborative and integrated, which raises the need for effectively and efficiently sharing and re-using design knowledge in a distributed and collaborative environment. ...Modem product development becomes increasingly collaborative and integrated, which raises the need for effectively and efficiently sharing and re-using design knowledge in a distributed and collaborative environment. To address this need, a framework is developed in this research to support design knowledge representation, retrieval, reasoning and fusion, which takes account of structural, functional and behavioral data, various design attributes and knowledge reasoning cases. Specifically, a multi-level knowledge representation based on the Base Object Model （BOM） is proposed to enable knowledge sharing using Web services technologies. On this basis, a multi-level knowledge reuse method is developed to support the retrieval, matching and assembly of knowledge records. Due to the tree structure of BOM, both depth-first and breadth-first searching strategies are employed in the retrieval algorithm while a novel measure is proposed to evaluate similarity. Moreover, a method based on the D-S evidence theory is developed to enable knowledge fusion and thus support effective decision-making. The framework has been implemented and integrated into an HLA-based simulation platform on which the development of a missile simulation example is conducted. It is demonstrated in the case study that the proposed framework and methods are useful and effective for design knowledge representation and reuse.展开更多
Multimodal biometric fusion is gaining more attention among researchers in recent days. As multimodal biometric system consolidates the information from multiple biometric sources, the effective fusion of information ...Multimodal biometric fusion is gaining more attention among researchers in recent days. As multimodal biometric system consolidates the information from multiple biometric sources, the effective fusion of information obtained at score level is a challenging task. In this paper, we propose a framework for optimal fusion of match scores based on Gaussian Mixture Mode] （GMM） and Monte Carlo sampling based hypothesis testing. The proposed fusion approach has the ability to handle： 1） small size of match scores as is more commonly encountered in biometric fusion, and 2） arbitrary distribution of match scores which is more pronounced when discrete scores and multimodal features are present. The proposed fusion scheme is compared with well established schemes such as Likelihood Ratio （LR） method and weighted SUM rule. Extensive experiments carried out on five different multimodal biometric databases indicate that the proposed fusion scheme achieves higher performance as compared with other contemporary state of art fusion techniques.展开更多
Focusing on the problem of goal event detection in soccer videos,a novel method based on Hidden Markov Model(HMM) and the semantic rule is proposed.Firstly,a HMM for a goal event is constructed.Then a Normalized Seman...Focusing on the problem of goal event detection in soccer videos,a novel method based on Hidden Markov Model(HMM) and the semantic rule is proposed.Firstly,a HMM for a goal event is constructed.Then a Normalized Semantic Weighted Sum(NSWS) rule is established by defining a new feature of shots,semantic observation weight.The test video is detected based on the HMM and the NSWS rule,respectively.Finally,a fusion scheme based on logic distance is proposed and the detection results of the HMM and the NSWS rule are fused by optimal weights in the decision level,obtaining the final result.Experimental results indicate that the proposed method achieves 96.43% precision and 100% recall,which shows the effectiveness of this letter.展开更多
基金supported by the National Natural Science Foundation of China (No.31872399)Advantage Discipline Construction Project (PAPD,No.6-2018)of Jiangsu University。
文摘Pose-invariant facial expression recognition(FER)is an active but challenging research topic in computer vision.Especially with the involvement of diverse observation angles,FER makes the training parameter models inconsistent from one view to another.This study develops a deep global multiple-scale and local patches attention(GMS-LPA)dual-branch network for pose-invariant FER to weaken the influence of pose variation and selfocclusion on recognition accuracy.In this research,the designed GMS-LPA network contains four main parts,i.e.,the feature extraction module,the global multiple-scale(GMS)module,the local patches attention(LPA)module,and the model-level fusion model.The feature extraction module is designed to extract and normalize texture information to the same size.The GMS model can extract deep global features with different receptive fields,releasing the sensitivity of deeper convolution layers to pose-variant and self-occlusion.The LPA module is built to force the network to focus on local salient features,which can lower the effect of pose variation and self-occlusion on recognition results.Subsequently,the extracted features are fused with a model-level strategy to improve recognition accuracy.Extensive experimentswere conducted on four public databases,and the recognition results demonstrated the feasibility and validity of the proposed methods.
基金This research is supported by the National Natural Science Foundation of China （Grant No.61374163）, the National Key Technology R＆D Program （Grant No. 2012BAF15G00）, the National High Technology Research and Development Program （863 Program） of China （Grant No.2013AA041302）. Acknowledgments This research is supported by the National Natural Science Foundation of China （Grant No.61374163 ） , the National Key Technology R＆D Program （Grant No. 2012BAF 15G00）, the National High Technology Research and Development Program （863 Program） of China （Grant No.2013AA041302）. The original version of this paper was presented at the 18th 1EEE CSCWD Conference held in Taiwan, China in May 2014.
文摘Modem product development becomes increasingly collaborative and integrated, which raises the need for effectively and efficiently sharing and re-using design knowledge in a distributed and collaborative environment. To address this need, a framework is developed in this research to support design knowledge representation, retrieval, reasoning and fusion, which takes account of structural, functional and behavioral data, various design attributes and knowledge reasoning cases. Specifically, a multi-level knowledge representation based on the Base Object Model （BOM） is proposed to enable knowledge sharing using Web services technologies. On this basis, a multi-level knowledge reuse method is developed to support the retrieval, matching and assembly of knowledge records. Due to the tree structure of BOM, both depth-first and breadth-first searching strategies are employed in the retrieval algorithm while a novel measure is proposed to evaluate similarity. Moreover, a method based on the D-S evidence theory is developed to enable knowledge fusion and thus support effective decision-making. The framework has been implemented and integrated into an HLA-based simulation platform on which the development of a missile simulation example is conducted. It is demonstrated in the case study that the proposed framework and methods are useful and effective for design knowledge representation and reuse.
文摘Multimodal biometric fusion is gaining more attention among researchers in recent days. As multimodal biometric system consolidates the information from multiple biometric sources, the effective fusion of information obtained at score level is a challenging task. In this paper, we propose a framework for optimal fusion of match scores based on Gaussian Mixture Mode] （GMM） and Monte Carlo sampling based hypothesis testing. The proposed fusion approach has the ability to handle： 1） small size of match scores as is more commonly encountered in biometric fusion, and 2） arbitrary distribution of match scores which is more pronounced when discrete scores and multimodal features are present. The proposed fusion scheme is compared with well established schemes such as Likelihood Ratio （LR） method and weighted SUM rule. Extensive experiments carried out on five different multimodal biometric databases indicate that the proposed fusion scheme achieves higher performance as compared with other contemporary state of art fusion techniques.
基金Supported by the National Natural Science Foundation of China (No. 61072110)the Industrial Tackling Project of Shaanxi Province (2010K06-20)the Natural Science Foundation of Shaanxi Province (SJ08F15)
文摘Focusing on the problem of goal event detection in soccer videos,a novel method based on Hidden Markov Model(HMM) and the semantic rule is proposed.Firstly,a HMM for a goal event is constructed.Then a Normalized Semantic Weighted Sum(NSWS) rule is established by defining a new feature of shots,semantic observation weight.The test video is detected based on the HMM and the NSWS rule,respectively.Finally,a fusion scheme based on logic distance is proposed and the detection results of the HMM and the NSWS rule are fused by optimal weights in the decision level,obtaining the final result.Experimental results indicate that the proposed method achieves 96.43% precision and 100% recall,which shows the effectiveness of this letter.
