Fusing Attention Network Based on Dilated Convolution for Superresolution

Song, Zhaoyang; Zhao, Xiaoqiang; Hui, Yongyong; Jiang, Hongmei

doi:10.1109/tcds.2022.3153090

Cited by 9 publications

(5 citation statements)

References 38 publications

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“…𝑠𝑒𝑔(𝑒𝑑𝑔 𝑎,𝑏 ) = { 𝛿 𝑖𝑓 𝑅𝑒𝑔 𝑖𝑑 (𝑝) = 𝑅𝑒𝑔 𝑖𝑑 (𝑞) 100 × (1 − 𝛿 ) 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 (9) using 𝛿 ∈ [0, 1], 𝛿 = 0 for the initial period of overlap, , 𝛿 = 1 for the most recent frame of crossover, and exponential somewhere between. The operator 𝑅𝑒𝑔 𝑖𝑑 (𝑝) provides the regional id from the preceding clip's segment to every voxel p in the overlapping.…”

Section: Segmentation Of Playersmentioning

confidence: 99%

“…These methods, nevertheless, call for the placement of numerous cameras all throughout the playing area, which is significantly more costly than single perspective collection systems and seems challenging to implement in many settings. Inertial device sign language [9] and picture gestures [10] are the two primary kinds of basketball gesture recognition techniques. The inertial camera pose cohesion necessitates the use of the sensor by the athlete, who then sends the data obtained to the data processing terminal for action recognition analysis.…”

Section: Introductionmentioning

confidence: 99%

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Basketball Tactics Analysis Based on Improved Openpose Algorithm and Its Application

2024

jes

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The use of information technology has currently resulted in a change in people's production and lifestyles as well as a catalyst for the sports industry's evolution. Currently, there is a growing use of digitization in the sports industry's basketball realm. Using 2D posture estimation, a novel lightweight deep learning architecture (LDLA) is built to accomplish the automatic analysis of basketball game footage. We offer a real-time method to identify numerous people's 2D poses in a video. Additionally, it examines how 2D pose estimation might be used to analyse basketball shooting videos. First, group and global motion features are extracted to represent semantic events. A full basketball game video is broken down into three stages: clip-based segmentation, classification of semantics occurrences utilizing audio and video, and temporal sequence characteristics employing GRU CNN. Video is first processed to create some basic video categorization and segmentation using the visual, movement, and auditory data. Further use of domain knowledge is made to find important events in the basketball video. Shot and image threshold recognition algorithms for videos have used both optical and kinematic prediction information; scene classification comes next. The placements of probable semantic events, such as "fouling" and "shooting at the basket," are then discovered by comparing the multidimensional data with supplementary domain expertise. Experimental results demonstrate the proposed LDLA method achieves 99.6% of accuracy, 93.2% of precision, 93.5% of recall and 89.5% of F1-score.

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Section: Segmentation Of Playersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Basketball Tactics Analysis Based on Improved Openpose Algorithm and Its Application

2024

jes

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“…The higher the rate of expansion, the larger the receptive field is. Since the original vibration signal of a bearing has weak coupling properties and is often drowned by noise, it is necessary to use the convolution of a large receptive field [35].…”

Section: Icnn Structuresmentioning

confidence: 99%

Intelligent Fault Diagnosis Method through ACCC-Based Improved Convolutional Neural Network

Zhang

Huang

Yang³

et al. 2023

Actuators

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Fault diagnosis plays an important role in improving the safety and reliability of complex equipment. Convolutional neural networks (CNN) have been widely used to diagnose faults due to their powerful feature extraction and learning capabilities. In practical industrial applications, the obtained signals always are disturbed by strong and highly non-stationary noise, so the timing relationships of the signals should be highlighted more. However, most CNN-based fault diagnosis methods directly use a pooling layer, which may corrupt the timing relationship of the signals easily. More importantly, due to a lack of an attention mechanism, it is difficult to extract deep informative features from noisy signals. To solve the shortcomings, an intelligent fault diagnosis method is proposed in this paper by using an improved convolutional neural network (ICNN) model. Three innovations are developed. Firstly, the receptive field is used as a guideline to design diagnosis network structures, and the receptive field of the last layer is close to the length of the original signal, which can enable the network to fully learn each sample. Secondly, the dilated convolution is adopted instead of standard convolution to obtain larger-scale information and preserves the internal structure and temporal relation of the signal when performing down-sampling. Thirdly, an attention mechanism block named advanced convolution and channel calibration (ACCC) is presented to calibrate the feature channels, thus the deep informative features are distributed in larger weights while noise-related features are effectively suppressed. Finally, two experiments show the ICNN-based fault diagnosis method can not only process strong noise signals but also diagnose early and minor faults. Compared with other methods, it achieves the highest average accuracy at 94.78% and 90.26%, which are 6.53% and 7.70% higher than the CNN methods, respectively. In complex machine bearing failure conditions, this method can be used to better diagnose the type of failure; in voice calls, this method can be used to better distinguish between voice and noisy background sounds to improve call quality.

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“…Chen et al (2017) propose a spatial pyramid pooling module to obtain multi-scale feature information through dilated convolutions of different rates in parallel. Dilated convolutions can also be applied to computer vision fields such as object tracking Hsu and Chen (2022), audio generation Oord et al (2016), and image super-resolution Song et al (2022).…”

Section: Development Of Medical Image Segmentationmentioning

confidence: 99%

“…Dilated convolutions can also be applied to computer vision fields such as object tracking Hsu and Chen (2022) , audio generation Oord et al. (2016) , and image super-resolution Song et al. (2022) .…”

Section: Related Workmentioning

confidence: 99%

Dilated convolution network with edge fusion block and directional feature maps for cardiac MRI segmentation

Chen

Bai

2023

Front. Physiol.

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Cardiac magnetic resonance imaging (MRI) segmentation task refers to the accurate segmentation of ventricle and myocardium, which is a prerequisite for evaluating the soundness of cardiac function. With the development of deep learning in medical imaging, more and more heart segmentation methods based on deep learning have been proposed. Due to the fuzzy boundary and uneven intensity distribution of cardiac MRI, some existing methods do not make full use of multi-scale characteristic information and have the problem of ambiguity between classes. In this paper, we propose a dilated convolution network with edge fusion block and directional feature maps for cardiac MRI segmentation. The network uses feature fusion module to preserve boundary information, and adopts the direction field module to obtain the feature maps to improve the original segmentation features. Firstly, multi-scale feature information is obtained and fused through dilated convolutional layers of different scales while downsampling. Secondly, in the decoding stage, the edge fusion block integrates the edge features into the side output of the encoder and concatenates them with the upsampled features. Finally, the concatenated features utilize the direction field to improve the original segmentation features and generate the final result. Our propose method conducts comprehensive comparative experiments on the automated cardiac diagnosis challenge (ACDC) and myocardial pathological segmentation (MyoPS) datasets. The results show that the proposed cardiac MRI segmentation method has better performance compared to other existing methods.

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Fusing Attention Network Based on Dilated Convolution for Superresolution

Cited by 9 publications

References 38 publications

Basketball Tactics Analysis Based on Improved Openpose Algorithm and Its Application

Basketball Tactics Analysis Based on Improved Openpose Algorithm and Its Application

Intelligent Fault Diagnosis Method through ACCC-Based Improved Convolutional Neural Network

Dilated convolution network with edge fusion block and directional feature maps for cardiac MRI segmentation

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