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24th IEEE International Conference on Image Processing (ICIP)

作者： Jing, Longlong Ye, Yuancheng Yang, Xiaodong Tian, Yingli CUNY Grad Ctr New York NY 10031 USA CUNY City Coll New York NY USA NVIDIA Res Santa Clara CA USA

ISBN: (纸本)9781509021758

In this paper, we propose an efficient and effective action recognition framework by combining multiple feature models from dynamic image, optical flow and raw frame, with 3d convolutional neural network (CNN). dynamic image preserves the long-term temporal information, while optical flow captures short-term temporal information, and raw frame represents the appearance information. Experiments demonstrate that dynamic image provides complementary information to raw frame feature and optical flow feature. Furthermore, with the approximate rank pooling, the computation of dynamic images is about 360 times faster than optical flow, and the dynamic image requires far less memory than optical flow and raw frame.

关键词： Action Recognition 3d convolutional neural network Video Classification

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3d convolutional neural network for Segmentation of the Urethra in Volumetric Ultrasound of the Pelvic Floor

3D Convolutional Neural Network for Segmentation of the Uret...

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IEEE International Ultrasonics Symposium (IUS)

作者： Williams, Helena Cattani, Laura Li, Wenqi Tabassian, Mahdi Vercauteren, Tom deprest, Jan d'hooge, Jan Katholieke Univ Leuven Dept Dev & Regenerat Urogenital Abdominal & Plast Surg Dept Obstet & Gynaecol Leuven Belgium NVIDIA Corp Cambridge England Katholieke Univ Leuven Dept Cardiovasc Sci Leuven Belgium Kings Coll London Sch Biomed Engn & Imaging Sci London England

ISBN: (纸本)9781728145969

Pelvic organ prolapse (POP) decreases the quality of life for many women. To assess POP, the levator hiatus is segmented in a 2d plane of minimal hiatal dimensions, known as the C-plane. In order to automate plane detection, landmark information of key structures should be given to a plane detection algorithm. In this work, we present a fully automatic method to segment the urethra from a 3d transperineal ultrasound volume using a convolutional neural network (CNN). A dataset with 35 volumes from 20 patients during the Valsalva manoeuver (i.e. Valsalva, contraction and rest) labelled by an expert, was used for training and evaluation in a 5-fold cross-validation process. The 3d CNN model yielded an average robust Hausdorff distance of 4.68mm (95 percentile) which was comparable to intra-observer results.

关键词： Pelvic floor 3d convolutional neural network Semantic segmentation

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Learning Multiband-Temporal-Spatial EEG Representations of Emotions Using Lightweight Temporal Convolution and 3d convolutional neural network

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IEEE ACCESS 2024年 12卷 132016-132026页

作者： Wu, Fengjie Liu, Jiarui Yang, Jisen Zhang, Lihan He, Yan Lin, Zhaolong Guangzhou Univ Lab Ctr Guangzhou 510006 Guangdong Peoples R China Guangzhou Univ Sch Elect & Commun Engn Guangzhou 510006 Peoples R China

Emotion recognition based on electroencephalography (EEG) has become an important topic in affective computing. However, current research does not utilize the relationship between channel frequency bands, and the trainable parameters and FLOPs of the models of mainstream methods from the past two years are very large. To solve this problem, this paper proposes a new emotion recognition framework to learn the frequency band relationship of EEG and the temporal-spatial representation of the frequency domain. First, we extract the differential entropy features of the frequency band at a frequency resolution of 2 Hz to retain rich frequency band information. Second, we design a temporal convolution and 3d convolutional neural network (TC3dNet) that is a fusion of Temporal convolutional network (TCN) and 3d convolutional neural network (3dCNN) in series. TCN is used to learn frequency band relationships, and 3dCNN is further used to learn temporal-spatial representation. The average recognition accuracy of TC3dNet in the benchmark EEG emotion recognition SEEd dataset and SEEd-IV dataset is 98.48% and 95.30%, which is better than current state-of-the-art methods. The impact of different frequency band divisions on the proposed framework is also explored. Furthermore, this study visualizes the extracted features, thereby enhancing the interpretability of our work.

关键词： Electroencephalogram emotion recognition temporal convolutional neural network 3d convolutional neural network Electroencephalogram emotion recognition temporal convolutional neural network 3d convolutional neural network

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Violence detection in videos using interest frame extraction and 3d convolutional neural network

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MULTIMEdIA TOOLS ANd APPLICATIONS 2022年第15期81卷 20945-20961页

作者： Mahmoodi, Javad Nezamabadi-pour, Hossein Abbasi-Moghadam, dariush Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

With the rapid development of detecting violent behaviors in surveillance cameras, requests on systems that automatically recognize violent events are expanded. Nowadays, violence detection has become an active research field in image processing and machine learning. The relevant works in such a field are classified into hand-crafted and deep learning methods. despite the effectiveness of hand-crafted ones, their computational cost may be suppressive for practical applications. Additionally, deep learning techniques usually exploit 3d convolutional networks (3d ConvNets) to do this task. To improve the accuracy of these networks, meaningful regions and temporal changes in videos should be considered. Consequently, the performance of a 3d ConvNet can be reinforced by selecting significant temporal information and noticing to special regions in two spatial dimensions. In this work, we propose a novel 3d ConvNet along with a technique for extracting interest frames. The Structural Similarity Index Measure (SSIM) is exploited to extract interest frames as significant temporal information. Indeed, the SSIM uses the statistical features of two consecutive frames for this reason. In this way, sixteen video frames with the smallest SSIM are considered as dominant motion frames, which are then sent to a 3d CNN for classification. Moreover, a spatial attention module is exploited to make attention on the specific regions. Furthermore, three benchmark datasets are employed to evaluate the performance of the proposed method. The results show that in terms of accuracy, our scheme outperforms existing approaches.

关键词： Violence detection deep learning 3d convolutional neural network Interest frame extraction

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V-3dResNets: a 3d convolutional neural network based on residual network variants and slice grouping for pulmonary nodule detection

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MULTIMEdIA TOOLS ANd APPLICATIONS 2024年第31期83卷 76505-76528页

作者： Prithvika, P. C. Sarah Anbarasi, L. Jani Vellore Inst Technol Sch Comp Sci & Engn Chennai India

Many people are affected by lung cancer and it kills a lot of people worldwide. Often times, lung cancer does not have symptoms in the early stage. Abnormal growths called pulmonary nodules may be found in the lungs. Some of the pulmonary nodules may be cancerous. So, identifying pulmonary nodules will help detect lung cancer at an early stage. V-3dResNets based on variants of residual network is proposed to improve the generalization capability of the pulmonary nodule detection system. A U-shaped encoder-decoder is used to effectively extract the features. Residual networks help to reduce the vanishing gradient problem in deep neural networks. A Slice Grouped Non Local (SGNL) module studies the relation among entities across Computed Tomography (CT) slices. The content-aware mechanism of the squeeze and excitation block assigns different weights to the channels and improves the performance of the nodule detection system. The proposed work is analyzed using the PN9 dataset that includes 8,798 CT Scans with nine types of pulmonary nodules which attained a Competition Performance metric (CPM) of 65.51%.

关键词： 3d convolutional neural network deep learning Pulmonary nodule Segmentation Computer-aided detection Residual networks ResNext Pre-activation ResNet and SE-ResNet

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Video Visual Relation detection via 3d convolutional neural network

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IEEE ACCESS 2022年 10卷 23748-23756页

作者： Qu, Mingcheng Cui, Jianxun Su, Tonghua deng, Ganlin Shao, Wenkai Harbin Inst Technol Fac Comp Harbin 150040 Peoples R China HIT Chongqing Res Inst Chongqing 400000 Peoples R China Harbin Inst Technol Harbin 150040 Peoples R China

Video visual relation detection, which aims to detect the visual relations between objects in the form of relation triplet (e.g., "person-ride-bike", "dog-toward-car", etc.), is a significant and fundamental task in computer vision. However, most of the existing works about visual relation instances are focused on static images. Modeling the non-static relationships in videos has drawn little attention due to lacking large-scale video dataset support. In our work, we propose a video dataset named Video Predicate detection and Reasoning (VidPdR) for dynamic video visual relation detection, which consists of 1,000 videos with dense manually dynamic labeled annotations on 21 object classes and 37 predicates classes. Moreover, we propose a novel spatio-temporal feature extraction framework with 3d convolutional neural networks (ST3dCNN), which includes three modules 1) object trajectory, 2) short-term relation prediction, and 3) greedy relational association. We conducted appropriate experiments on public datasets and our own dataset (VidPdR). Results demonstrate that our proposed method has a great improvement in comparison to the state-of-the-art baselines.

关键词： Visualization Feature extraction Trajectory Three-dimensional displays convolutional neural networks Task analysis Object detection Computer vision 3d convolutional neural network video visual relation detection

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A new 3d convolutional neural network (3d-CNN) framework for multimedia event detection

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SIGNAL IMAGE ANd VIdEO PROCESSING 2021年第4期15卷 779-787页

作者： Kanagaraj, Kaavya Priya, G. G. Lakshmi VIT SITE Vellore Tamil Nadu India

Multimedia event detection has received a great deal of interest due to developments in video technology and an increase in multimedia data. However, complexities of video content such as noisy, overlapping, repeated interaction between individuals, and various scenes are becoming difficult for characterizing the subjects and concepts. In particular, Internet users find it difficult to search for a specified event. To solve the above problem, a method is proposed that best suits for event detection, demonstrating the 3d convolutional neural network (3d-CNN) structure to accomplish promising performance in multimedia event classification. To take an advantage of motion content of the event in the video, temporal axis is considered. Both the feature extraction and classification are incorporated in this model. Experiments are carried out on the Columbia Consumer Video benchmark dataset, and results are compared with other existing works.

关键词： Multimedia event detection 3d convolutional neural network Feature extraction Classification Mean average precision Columbia consumer video

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Learning microstructure-property mapping via label-free 3d convolutional neural network

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VISUAL COMPUTER 2024年第10期40卷 7025-7044页

作者： Zhu, Liangchao Wang, Xuwei Zhong, Weidong Li, Ming Zhejiang Univ State Key Lab CAD & CG Hangzhou 310027 Peoples R China

Predicting the physical property of a class of microstructures is crucial in material design, structural simulation, and design. Property prediction may be conducted millions of times in these studies and is better derived instantly for computational efficiency. This issue is addressed in this study via building a mapping from a 3d microstructure to its effective material property, or called structure-property mapping, using a 3d convolutional neural network (CNN). Unlike the direct approach using labeled simulation data, the mapping is based on the physical knowledge of the structure-property relationship determined by its underlying PdE equations. The knowledge is embedded in the loss function of the CNN framework, which is designed and tested under several different formulations to improve its training convergence. Ultimately, the derived structure-property mapping can instantly predict the associated material property for a given microstructure and has far better generalization ability than the data-labeled approach, as demonstrated via numerical examples.

关键词： Microstructure-property mapping Label-free 3d convolutional neural network Homogenization

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Hybrid network model based on 3d convolutional neural network and scalable graph convolutional network for hyperspectral image classification

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IET IMAGE PROCESSING 2023年第1期17卷 256-273页

作者： Wang, Xili Liang, Zhengyin Shaanxi Notmal Univ Sch Comp Sci Xian 710000 Peoples R China

Hyperspectral images (HSIs) contain hundreds of continuous spectral bands and are rich in spectral-spatial information. In terms of HSIs' classification, traditional convolutional neural networks (CNNs) extract features based on HSI's spectral-spatial information through 2d convolution. However, 2d convolution extracts features in 2d plane without considering the relationships between spectral bands, which inevitably leads to insufficient feature extraction. 3d convolutional neural networks (3dCNNs) take account of the correlations among spectral bands and outperform 2d convolutional networks in feature extraction, but the computational cost is rather expensive. To address the above problem, a light-weight three-layer 3d convolutional network Module (3d-M) for HSIs' spectral-spatial feature extraction is proposed. Another challenge is that neither 2d convolution nor 3d convolution utilizes the structural information inherent in the data. Graph convolution networks (GCNs) can model and utilize such information through the similarity matrix, also known as adjacency matrix. However, traditional GCNs cannot handle large-scale data because they construct adjacency matrix on all data, which results in high computational complexity and large storage requirement. To conquer this challenge, this article proposes a batch-graph strategy on which a scalable GCN is developed. Finally, a hybrid network model (HNM) based on the proposed light-weight 3d-M and scalable GCN is presented. HNM extracts spectral-spatial features of HSIs with low computational complexity through the light-weight 3d convolution network and leverages the structural information in data via the scalable GCN. The experimental results on three public datasets with different sizes demonstrate that the proposed HNM produces better classification results than other state-of-the-art hyperspectral images classification models in terms of overall accuracy (OA), average accuracy (AA) and kappa coefficient (Kappa).

关键词： hybrid network model feature extraction leverages spectral-spatial features image representation image classification Geophysics computing 3d convolutional neural network scalable GCN three-layer 3d convolutional network Module HSIs' classification 3dCNNs structural information HSI's spectral-spatial information state-of-the-art hyperspectral images classification models neural nets Computer vision and image processing techniques convolutional neural nets traditional convolutional neural networks outperform 2d convolutional networks hyperspectral imaging Image recognition graph theory spectral-spatial feature extraction scalable graph convolutional network continuous spectral bands graph convolution networks insufficient feature extraction Geophysical techniques and equipment hyperspectral image classification light-weight 3d-M geophysical image processing computational complexity Combinatorial mathematics

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An end-to-end 3d convolutional neural network for decoding attentive mental state

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neural networkS 2021年 144卷 129-137页

作者： Zhang, Yangsong Cai, Huan Nie, Li Xu, Peng Zhao, Sirui Guan, Cuntai Southwest Univ Sci & Technol Sch Comp Sci & Technol Mianyang Sichuan Peoples R China Univ Elect Sci & Technol China Sch Life Sci & Technol MOE Key Lab Neuroinformat Chengdu Peoples R China Nanyang Technol Univ NTU Sch Comp Sci & Engn Singapore Singapore Minist Educ Key Lab Cognit & Personal Chongqing Peoples R China

The detection of attentive mental state plays an essential role in the neurofeedback process and the treatment of Attention deficit and Hyperactivity disorder (AdHd). However, the performance of the detection methods is still not satisfactory. One of the challenges is to find a proper representation for the electroencephalogram (EEG) data, which could preserve the temporal information and maintain the spatial topological characteristics. Inspired by the deep learning (dL) methods in the research of brain-computer interface (BCI) field, a 3d representation of EEG signal was introduced into attention detection task, and a 3d convolutional neural network model with cascade and parallel convolution operations was proposed. The model utilized three cascade blocks, each consisting of two parallel 3d convolution branches, to simultaneously extract the multi-scale features. Evaluated on a public dataset containing twenty-six subjects, the proposed model achieved better performance compared with the baseline methods under the intra-subject, inter-subject and subject-adaptive classification scenarios. This study demonstrated the promising potential of the 3d CNN model for detecting attentive mental state. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： EEG Attention BCI deep learning 3d convolutional neural network

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