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Human Action Recognition using 3d convolutional neural networks with 3d Motion Cuboids in Surveillance Videos

Procedia Computer Science 2018年 133卷 471-477页

作者： J. Arunnehru G. Chamundeeswari S. Prasanna Bharathi Department of CSE SRM Institute of Science and Technology Vadapalani Campus Chennai - 26 Department of ECE SRM Institute of Science and Technology Vadapalani Campus Chennai - 26

In recent days, suspicious action recognition is a significant topic in intelligent video surveillance and computer vision research. Action recognition methodologies are specially needed for surveillance systems which are required to prevent crimes and treacherous actions before occurring. In this paper, we present 3d - convolutional neural networks (3d-CNN) with 3d motion cuboid for action detection and recognizing in videos. The experiments are conducted on benchmark KTH and Weizmann dataset. The proposed method is compared with the existing methods in terms of accuracy. The results show that this approach is outperforms previously published results.

关键词： Human action recognition 3d convolutional neural network 3d motion information Temporal difference Classification

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Crop yield prediction from multi-spectral, multi-temporal remotely sensed imagery using recurrent 3d convolutional neural networks*

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INTERNATIONAL JOURNAL OF APPLIEd EARTH OBSERVATION ANd GEOINFORMATION 2021年 102卷

作者： Qiao, Mengjia He, Xiaohui Cheng, Xijie Li, Panle Luo, Haotian Zhang, Lehan Tian, Zhihui Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Peoples R China Zhengzhou Univ Sch Geosci & Technol Zhengzhou 450001 Peoples R China Zhengzhou Univ Ecometeorol Joint Lab Zhengzhou 450001 Peoples R China Chinese Acad Meteorol Sci Beijing 450001 Peoples R China

Crop yield prediction has played a vital role in maintaining food security and has been extensively investigated in recent decades. Most research has focused on excavating fixed spectral information from remote sensing images. However, the growth of crops is a highly complex trait determined by diverse features. To maximally explore these heterogeneous features, we aim to simultaneously exploit spatial, spectral, and temporal information from multi-spectral and multi-temporal remotely sensed imagery. Therefore, in this paper, we propose a novel deep learning architecture for crop yield prediction, namely, SSTNN (Spatial-Spectral-Temporal neural network), which combines 3d convolutional and recurrent neural networks to exploit their complementarity. Specifically, the SSTNN incorporates a spatial-spectral learning module and a temporal dependency capturing module into a unified convolutional network to recognize the joint spatial-spectral-temporal representation. The novel spatialspectral feature learning module first exploits sufficient spatial-spectral features from the multi-spectral images. Then, the temporal dependency capturing module is concatenated on top of the spatial-spectral feature learning module to mine the temporal relationship from the long time-series images. Furthermore, we introduce a new loss function that eliminates the influence of an imbalanced distribution of crop yield labels. Finally, the proposed SSTNN is validated on winter wheat and corn yield predictions from China. The results are compared with widely used machine learning methods as well as state-of-art deep learning methods. The experimental results demonstrate that the proposed method can provide better prediction performance than the competitive methods.

关键词： Crop yield prediction 3d convolutional neural network Long short-term memory Multi-temporal images

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Thai Sign Language Recognition Using 3d convolutional neural networks

Thai Sign Language Recognition Using 3D Convolutional Neural...

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2019第七届计算机与通信管理国际会议

作者： Nutisa Sripairojthikoon Jaturon Harnsomburana Department of Computer Engineering King Mongkut's University of Technology

Translating sign language is a complicating and challenging task due to complexity of sign language structures including hand shapes,hand orientation,hand movements,and facial *** approaches generally used complex handcrafted features to recognize a sign ***,to build a model based on those features is a difficult *** deal with this problem,we proposed a model of Thai sign language recognition using 3d convolutional neural network(3dCNN) which can automatically learn both temporal and spatial features from *** data is a collection of 64 isolated Thai signs language vocabulary as video stream using Microsoft Kinect to acquire information of color,depth,skeleton,hand shapes,and whole body *** evaluate our proposed approach,different kind of stream and information are tested with the best empirical *** selected model is 3dCNN with kernel 3×3×*** experimental results demonstrated that the accuracy of different input and information is as high as the highest 97.7% which belong to skeleton dataset.

关键词： Sign Language Recognition 3d convolutional neural network Thai Sign Language deep Learning

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ALZHEIMER'S dISEASE dIAGNOSTICS BY AdAPTATION OF 3d convolutional network 23

ALZHEIMER'S DISEASE DIAGNOSTICS BY ADAPTATION OF 3D CONVOLUT...

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23rd IEEE International Conference on Image Processing (ICIP)

作者： Hosseini-Asl, Ehsan Keynton, Robert El-Baz, Ayman Univ Louisville Elect & Comp Engn Dept Louisville KY 40292 USA Univ Louisville Dept Bioengn Louisville KY 40292 USA

ISBN: (纸本)9781467399616

Early diagnosis, playing an important role in preventing progress and treating the Alzheimer's disease (Ad), is based on classification of features extracted from brain images. The features have to accurately capture main Ad-related variations of anatomical brain structures, such as, e.g., ventricles size, hippocampus shape, cortical thickness, and brain volume. This paper proposed to predict the Ad with a deep 3d convolutional neural network (3d-CNN), which can learn generic features capturing Ad biomarkers and adapt to different domain datasets. The 3d-CNN is built upon a 3d convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans. Fully connected upper layers of the 3d-CNN are then fine-tuned for each task-specific Ad classification. Experiments on the CAddementia MRI dataset with no skull-stripping preprocessing have shown our 3d-CNN outperforms several conventional classifiers by accuracy. Abilities of the 3d-CNN to generalize the features learnt and adapt to other domains have been validated on the AdNI dataset.

关键词： Alzheimer's disease deep learning 3d convolutional neural network autoencoder brain MRI

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Automatic Cerebrospinal Fluid Segmentation in Non-Contrast CT Images Using a 3d convolutional network

Automatic Cerebrospinal Fluid Segmentation in Non-Contrast C...

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Conference on Medical Imaging - Computer-Aided diagnosis

作者： Patel, Ajay van de Leemput, Sil C. Prokop, Mathias van Ginneken, Bram Manniesing, Rashindra Radboud Univ Nijmegen Diagnost Image Anal Grp Dept Radiol & Nucl Med Med Ctr Nijmegen Netherlands

ISBN: (纸本)9781510607132;9781510607149

Segmentation of anatomical structures is fundamental in the development of computer aided diagnosis systems for cerebral pathologies. Manual annotations are laborious, time consuming and subject to human error and observer variability. Accurate quantification of cerebrospinal fluid (CSF) can be employed as a morphometric measure for diagnosis and patient outcome prediction. However, segmenting CSF in non-contrast CT images is complicated by low soft tissue contrast and image noise. In this paper we propose a state-of-the-art method using a multi-scale three-dimensional (3d) fully convolutional neural network (CNN) to automatically segment all CSF within the cranial cavity. The method is trained on a small dataset comprised of four manually annotated cerebral CT images. Quantitative evaluation of a separate test dataset of four images shows a mean dice similarity coefficient of 0.87 +/- 0.01 and mean absolute volume difference of 4.77 +/- 2.70 %. The average prediction time was 68 seconds. Our method allows for fast and fully automated 3d segmentation of cerebral CSF in non-contrast CT, and shows promising results despite a limited amount of training data.

关键词： Cerebrospinal fluid CSF computed tomography CT segmentation 3d convolutional neural network

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mmWave path loss modeling for urban scenarios based on 3d-convolutional neural networks 36

mmWave path loss modeling for urban scenarios based on 3D-co...

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36th International Conference on Information networking (ICOIN)

作者： Kim, Hyeonjin Jin, Woobeen Lee, Hyukjoon Kwangwoon Univ Sch Comp & Informat Engn Seoul South Korea

ISBN: (纸本)9781665413329

This paper introduces an accurate and efficient urban area 5th Generation millimeter wave path loss model using 3d convolutional neural network (CNN). In this paper, we describe how to create a 3d image that is mainly used as an input for a 3d-CNN, and what are the model structures and hyperparameters used. The proposed path loss prediction model achieved a final Root Mean Square Error (RMSE) of 5.84 dBm.

关键词： deep Learning 5G mmWave path loss modeling 3d convolutional neural network

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3d convolutional Long-Short Term Memory network for Spatiotemporal Modeling of fMRI data 4th

3D Convolutional Long-Short Term Memory Network for Spatiote...

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4th International Workshop on Multimodal Brain Image Analysis (MBIA) / 7th International Workshop on Mathematical Foundations of Computational Anatomy (MFCA)

作者： Suo, Wei Hu, Xintao Yan, Bowei Sun, Mengyang Guo, Lei Han, Junwei Liu, Tianming Northwestern Polytech Univ Sch Automat Xian Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Univ Georgia Dept Comp Sci Athens GA 30602 USA Univ Georgia Bioimaging Res Ctr Athens GA 30602 USA

ISBN: (纸本)9783030332266;9783030332259

Complex spatiotemporal correlation and dependency embedded in functional magnetic resonance imaging (fMRI) data introduce critical challenges in related analytical methodologies. despite remarkable successes, most of existing approaches only model spatial or temporal dependency alone and the development of a unified spatiotemporal model is still a challenge. Meanwhile, the recent emergence of deep neural networks has provided powerful models for interpreting complex spatiotemporal data. Here, we proposed a novel convolutional long-short term memory network (3dCLN) for spatiotemporal modeling of fMRI data. The proposed model is designed to decode fMRI volumes belonging to different task events by joint training a 3d convolutional neural network (CNN) for spatial dependency modeling and a long short-term memory (LSTM) network for temporal dependency modeling. We also designed a 3d deconvolution scheme for fMRI sequence reconstruction to inspect the feature learning process in the 3dCLN. The experimental results on the motor task-fMRI data from Human Connectome Project (HCP) showed that fMRI volumes can be decoded with a relatively high accuracy (76.38%). More importantly, the proposed 3dCLN can dramatically remove noises and highlights signals of interest in the reconstructed fMRI sequence and hence improve the performance of activation detection, validating the spatiotemporal feature learning in the proposed 3dCLN model.

关键词： Functional magnetic resonance imaging 3d convolutional neural network Long short-term memory network Spatiotemporal modeling

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dCR-GAT: 3d convolutional Residual Graph Attention network for Emotion Classification

DCR-GAT: 3D Convolutional Residual Graph Attention Network f...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Huang, Yurui Liu, Tianyue Wu, Qingfeng Xiamen University Fujian Xiamen China

ISBN: (纸本)9798350386226

Electroencephalogram (EEG) emotion recognition has become a central research focus within brain-computer interface (BCI) studies, where efficient emotion classification models can enable intelligent emotion regulation. In this paper, we propose a novel model based on three-dimensional convolutional neural networks (3d-CNN), residual networks (ResNet), and graph attention networks (GAT), named 3dCR-GAT, for EEG emotion recognition. The main advantage of the 3dCR-GAT model is its ability to simultaneously process multi-channel EEG signals, capturing complex spatiotemporal dependencies and local features within a unified network framework. Additionally, the ResNet employs skip connections to address the vanishing gradient problem during deep network training, improving the stability and expressiveness of the model. The attention mechanism within the model dynamically adjusts feature weights, captures global features, and enhances the accuracy of EEG emotion classification. Experimental results on public dataset, demonstrate that 3dCR-GAT significantly outperforms traditional methods in classification accuracy and feature extraction capability. Ablation studies reveal that each component of the model contributes substantially to its overall performance. Specifically, our model achieve an average accuracy of 92% on SEEd for multi-state emotion recognition tasks. The 3dCR-GAT model represents an advanced and effective tool for EEG emotion classification, with broad potential applications. © 2024 IEEE.

关键词： 3d convolutional neural network Electroencephalography Emotion Classification Graph Attention network Residual network

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3d PostureNet: A unified framework for skeleton-based posture recognition

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PATTERN RECOGNITION LETTERS 2020年 140卷 143-149页

作者： Liu, Jianbo Wang, Ying Liu, Yongcheng Xiang, Shiming Pan, Chunhong Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

Image-based posture recognition is a very challenging problem as it is difficult to acquire rich 3d information from postures in 2d images. Existing methods founded on 3d skeleton cues could alleviate this issue, but they are not particularly efficient due to the application of handcrafted features and traditional classifiers. This paper presents a novel and unified framework for skeleton-based posture recognition, applying powerful 3d convolutional neural network (CNN) to this issue. Technically, bounding-box-based normalization for the raw skeleton data is proposed to eliminate the coordinate differences caused by diverse recording environments and posture displacements. Moreover, Gaussian voxelization for the skeleton is employed to expressively represent the posture configuration. Thereby, an end-to-end framework based on 3d CNN, called 3d PostureNet, is developed for robust posture recognition. To verify its effectiveness, a large-scale writing posture dataset is created and released in this work, including 113,400 samples of 30 subjects with 15 postures. Extensive experiments on the public MSRA hand gesture dataset, body pose dataset and the proposed writing posture dataset demonstrate that 3d PostureNet achieves significantly superior performance on both skeleton-based human posture and hand posture recognition tasks. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Human posture recognition Static hand gesture recognition Skeleton-based 3d convolutional neural network

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MCMC Guided CNN Training and Segmentation for Pancreas Extraction

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IEEE ACCESS 2021年 9卷 90539-90554页

作者： Tian, Mu He, Jinchan Yu, Xiaxia Cai, Chudong Gao, Yi Shenzhen Univ Sch Biomed Engn Hlth Sci Ctr Shenzhen 518060 Peoples R China Pengcheng Lab Shenzhen 518060 Peoples R China Marshall Lab Biomed Engn Shenzhen 518060 Peoples R China Sun Yat Sen Univ Affiliated Shantou Hosp Shantou Cent Hosp Dept Gen Surg Shantou 515031 Peoples R China

Efficient organ segmentation is the precondition of various quantitative analysis. Segmenting the pancreas from abdominal CT images is a challenging task because of its high anatomical variability in shape, size and location. What's more, the pancreas only occupies a small portion in abdomen, and the organ border is very fuzzy. All these factors make the segmentation methods of other organs less suitable for pancreas. In this work, we propose a Markov Chain Monte Carlo (MCMC) guided convolutional neural network (CNN) approach, in order to handle such difficulties in morphological and photometric variabilities. Specifically, the proposed method mainly consists of three steps: First, registration is carried out to mitigate the body weight and location variability. Then, an MCMC scheme is designed to guide the adaptive selection of 3d patches, which are fed to the CNN for training. At the same time, the pancreas distribution is also learned for subsequent segmentation. Eventually, the same MCMC process guides the segmentation process with patch-wise predictions fused using a Bayesian voting scheme. This method is evaluated on the NIH pancreatic dataset including 82 abdominal contrast-enhanced CT volumes. We have achieved a competitive result of 78.13% dice Similarity Coefficient value and 82.65% Recall value in testing data.

关键词： Pancreas Image segmentation Three-dimensional displays Training Computed tomography Two dimensional displays Testing Pancreas segmentation image registration MCMC 3d convolutional neural network

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