The Experts below are selected from a list of 97587 Experts worldwide ranked by ideXlab platform
Rangaprasad Arun Srivatsan - One of the best experts on this subject based on the ideXlab platform.
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a multi Domain Feature learning method for visual place recognition
International Conference on Robotics and Automation, 2019Co-Authors: Peng Yin, Chen Yin, Rangaprasad Arun SrivatsanAbstract:Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of environmental conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the environmental factors, leading to decreased accuracy decreases when environmental conditions change significantly, such as day versus night. To this end, we propose an end-to-end conditional visual place recognition method. Specifically, we introduce the multi-Domain Feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a Feature detaching module to separate the environmental condition-related Features from those that are not. The only label required within this Feature learning pipeline is the environmental condition. Evaluation of the proposed method is conducted on the multi-season NORDLAND dataset, and the multi-weather GTAV dataset. Experimental results show that our method improves the Feature robustness against variant environmental conditions.
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A Multi-Domain Feature Learning Method for Visual Place Recognition
arXiv: Robotics, 2019Co-Authors: Peng Yin, Chen Yin, Rangaprasad Arun SrivatsanAbstract:Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of environmental conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the environmental factors, leading to decreased accuracy decreases when environmental conditions change significantly, such as day versus night. To this end, we propose an end-to-end conditional visual place recognition method. Specifically, we introduce the multi-Domain Feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a Feature detaching module to separate the environmental condition-related Features from those that are not. The only label required within this Feature learning pipeline is the environmental condition. Evaluation of the proposed method is conducted on the multi-season \textit{NORDLAND} dataset, and the multi-weather \textit{GTAV} dataset. Experimental results show that our method improves the Feature robustness against variant environmental conditions.
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ICRA - A Multi-Domain Feature Learning Method for Visual Place Recognition
2019 International Conference on Robotics and Automation (ICRA), 2019Co-Authors: Peng Yin, Chen Yin, Rangaprasad Arun SrivatsanAbstract:Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of environmental conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the environmental factors, leading to decreased accuracy decreases when environmental conditions change significantly, such as day versus night. To this end, we propose an end-to-end conditional visual place recognition method. Specifically, we introduce the multi-Domain Feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a Feature detaching module to separate the environmental condition-related Features from those that are not. The only label required within this Feature learning pipeline is the environmental condition. Evaluation of the proposed method is conducted on the multi-season NORDLAND dataset, and the multi-weather GTAV dataset. Experimental results show that our method improves the Feature robustness against variant environmental conditions.
Peng Yin - One of the best experts on this subject based on the ideXlab platform.
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a multi Domain Feature learning method for visual place recognition
International Conference on Robotics and Automation, 2019Co-Authors: Peng Yin, Chen Yin, Rangaprasad Arun SrivatsanAbstract:Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of environmental conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the environmental factors, leading to decreased accuracy decreases when environmental conditions change significantly, such as day versus night. To this end, we propose an end-to-end conditional visual place recognition method. Specifically, we introduce the multi-Domain Feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a Feature detaching module to separate the environmental condition-related Features from those that are not. The only label required within this Feature learning pipeline is the environmental condition. Evaluation of the proposed method is conducted on the multi-season NORDLAND dataset, and the multi-weather GTAV dataset. Experimental results show that our method improves the Feature robustness against variant environmental conditions.
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A Multi-Domain Feature Learning Method for Visual Place Recognition
arXiv: Robotics, 2019Co-Authors: Peng Yin, Chen Yin, Rangaprasad Arun SrivatsanAbstract:Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of environmental conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the environmental factors, leading to decreased accuracy decreases when environmental conditions change significantly, such as day versus night. To this end, we propose an end-to-end conditional visual place recognition method. Specifically, we introduce the multi-Domain Feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a Feature detaching module to separate the environmental condition-related Features from those that are not. The only label required within this Feature learning pipeline is the environmental condition. Evaluation of the proposed method is conducted on the multi-season \textit{NORDLAND} dataset, and the multi-weather \textit{GTAV} dataset. Experimental results show that our method improves the Feature robustness against variant environmental conditions.
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ICRA - A Multi-Domain Feature Learning Method for Visual Place Recognition
2019 International Conference on Robotics and Automation (ICRA), 2019Co-Authors: Peng Yin, Chen Yin, Rangaprasad Arun SrivatsanAbstract:Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of environmental conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the environmental factors, leading to decreased accuracy decreases when environmental conditions change significantly, such as day versus night. To this end, we propose an end-to-end conditional visual place recognition method. Specifically, we introduce the multi-Domain Feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a Feature detaching module to separate the environmental condition-related Features from those that are not. The only label required within this Feature learning pipeline is the environmental condition. Evaluation of the proposed method is conducted on the multi-season NORDLAND dataset, and the multi-weather GTAV dataset. Experimental results show that our method improves the Feature robustness against variant environmental conditions.
Andrzej Cichocki - One of the best experts on this subject based on the ideXlab platform.
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ICONIP (3) - Nonnegative Tensor Train Decompositions for Multi-Domain Feature Extraction and Clustering
Neural Information Processing, 2016Co-Authors: Namgil Lee, Fengyu Cong, Anh Huy Phan, Andrzej CichockiAbstract:Tensor train (TT) is one of the modern tensor decomposition models for low-rank approximation of high-order tensors. For nonnegative multiway array data analysis, we propose a nonnegative TT (NTT) decomposition algorithm for the NTT model and a hybrid model called the NTT-Tucker model. By employing the hierarchical alternating least squares approach, each fiber vector of core tensors is optimized efficiently at each iteration. We compared the performances of the proposed method with a standard nonnegative Tucker decomposition (NTD) algorithm by using benchmark data sets including event-related potential data and facial image data in multi-Domain Feature extraction and clustering tasks. It is illustrated that the proposed algorithm extracts physically meaningful Features with relatively low storage and computational costs compared to the standard NTD model.
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multi Domain Feature extraction for small event related potentials through nonnegative multi way array decomposition from low dense array eeg
International Journal of Neural Systems, 2013Co-Authors: Fengyu Cong, Anh Huy Phan, Piia Astikainen, Qibin Zhao, Jari K. Hietanen, Tapani Ristaniemi, Andrzej Cichocki, Qiang WuAbstract:Non-negative Canonical Polyadic decomposition (NCPD) and non-negative Tucker decomposition (NTD) were compared for extracting the multi-Domain Feature of visual mismatch negativity (vMMN), a small event-related potential (ERP), for the cognitive research. Since signal-to-noise ratio in vMMN is low, NTD outperformed NCPD. Moreover, we proposed an approach to select the multi-Domain Feature of an ERP among all extracted Features and discussed determination of numbers of extracted components in NCPD and NTD regarding the ERP context.
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benefits of multi Domain Feature of mismatch negativity extracted by non negative tensor factorization from eeg collected by low density array
International Journal of Neural Systems, 2012Co-Authors: Fengyu Cong, Anh Huy Phan, Qibin Zhao, Tapani Ristaniemi, Tiina Huttunenscott, Jukka Kaartinen, Heikki Lyytinen, Andrzej CichockiAbstract:Through exploiting temporal, spectral, time-frequency representations, and spatial properties of mismatch negativity (MMN) simultaneously, this study extracts a multi-Domain Feature of MMN mainly using non-negative tensor factorization. In our experiment, the peak amplitude of MMN between children with reading disability and children with attention deficit was not significantly different, whereas the new Feature of MMN significantly discriminated the two groups of children. This is because the Feature was derived from multi-Domain information with significant reduction of the heterogeneous effect of datasets.
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LVA/ICA - Multi-Domain Feature of event-related potential extracted by nonnegative tensor factorization: 5 vs. 14 electrodes EEG data
Latent Variable Analysis and Signal Separation, 2012Co-Authors: Fengyu Cong, Anh Huy Phan, Piia Astikainen, Qibin Zhao, Jari K. Hietanen, Tapani Ristaniemi, Andrzej CichockiAbstract:As nonnegative tensor factorization (NTF) is particularly useful for the problem of underdetermined linear transform model, we performed NTF on the EEG data recorded from 14 electrodes to extract the multi-Domain Feature of N170 which is a visual event-related potential (ERP), as well as 5 typical electrodes in occipital-temporal sites for N170 and in frontal-central sites for vertex positive potential (VPP) which is the counterpart of N170, respectively. We found that the multi-Domain Feature of N170 from 5 electrodes was very similar to that from 14 electrodes and more discriminative for different groups of participants than that of VPP from 5 electrodes. Hence, we conclude that when the data of typical electrodes for an ERP are decomposed by NTF, the estimated multi-Domain Feature of this ERP keeps identical to its counterpart extracted from the data of all electrodes used in one ERP experiment.
Chen Yin - One of the best experts on this subject based on the ideXlab platform.
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a multi Domain Feature learning method for visual place recognition
International Conference on Robotics and Automation, 2019Co-Authors: Peng Yin, Chen Yin, Rangaprasad Arun SrivatsanAbstract:Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of environmental conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the environmental factors, leading to decreased accuracy decreases when environmental conditions change significantly, such as day versus night. To this end, we propose an end-to-end conditional visual place recognition method. Specifically, we introduce the multi-Domain Feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a Feature detaching module to separate the environmental condition-related Features from those that are not. The only label required within this Feature learning pipeline is the environmental condition. Evaluation of the proposed method is conducted on the multi-season NORDLAND dataset, and the multi-weather GTAV dataset. Experimental results show that our method improves the Feature robustness against variant environmental conditions.
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A Multi-Domain Feature Learning Method for Visual Place Recognition
arXiv: Robotics, 2019Co-Authors: Peng Yin, Chen Yin, Rangaprasad Arun SrivatsanAbstract:Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of environmental conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the environmental factors, leading to decreased accuracy decreases when environmental conditions change significantly, such as day versus night. To this end, we propose an end-to-end conditional visual place recognition method. Specifically, we introduce the multi-Domain Feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a Feature detaching module to separate the environmental condition-related Features from those that are not. The only label required within this Feature learning pipeline is the environmental condition. Evaluation of the proposed method is conducted on the multi-season \textit{NORDLAND} dataset, and the multi-weather \textit{GTAV} dataset. Experimental results show that our method improves the Feature robustness against variant environmental conditions.
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ICRA - A Multi-Domain Feature Learning Method for Visual Place Recognition
2019 International Conference on Robotics and Automation (ICRA), 2019Co-Authors: Peng Yin, Chen Yin, Rangaprasad Arun SrivatsanAbstract:Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of environmental conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the environmental factors, leading to decreased accuracy decreases when environmental conditions change significantly, such as day versus night. To this end, we propose an end-to-end conditional visual place recognition method. Specifically, we introduce the multi-Domain Feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a Feature detaching module to separate the environmental condition-related Features from those that are not. The only label required within this Feature learning pipeline is the environmental condition. Evaluation of the proposed method is conducted on the multi-season NORDLAND dataset, and the multi-weather GTAV dataset. Experimental results show that our method improves the Feature robustness against variant environmental conditions.
Minping Jia - One of the best experts on this subject based on the ideXlab platform.
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A novel optimized SVM classification algorithm with multi-Domain Feature and its application to fault diagnosis of rolling bearing
Neurocomputing, 2018Co-Authors: Xiaoan Yan, Minping JiaAbstract:Abstract Sensitive Feature extraction from the raw vibration signal is still a great challenge for intelligent fault diagnosis of rolling bearing. Current fault classification framework generally concentrates on the pattern of classifier with single-Domain Feature, which is easy to induce insufficient Feature extraction and low recognition accuracy. Therefore, to address this issue and improve intelligent diagnostic accuracy of rolling bearing, this paper proposes a novel fault classification algorithm based on optimized SVM with multi-Domain Feature, which mainly consists of three stages (i.e. multi-Domain Feature extraction, Feature selection and fault identification). In this first stage, three approaches (i.e. statistical analysis, FFT and VMD) are separately applied to extract the fault Feature information from multi-Domain aspect (e.g. time-Domain, frequency-Domain and time-frequency Domain), which can excavate comprehensively the condition information and intrinsic property of the raw vibration signal. Secondly, Laplace score algorithm is introduced to select automatically the meaningful sensitive Feature according to the importance of each Feature, which is aimed at removing some redundant information and improving the calculation efficiency. Finally, particle swarm optimization-based support vector machine (PSO-SVM) classification model is employed to implement the identification of multiple fault condition of rolling bearing. Performance of the proposed method is evaluated on two experimental examples of rolling bearing fault diagnosis. Experimental results show that the proposed method achieves high diagnosis accuracy for different working conditions of rolling bearing and outperforms some traditional methods both mentioned in this paper and published in other literature.