DP3 signal as a neuro-indictor for attentional processing of stereoscopic contents in varied depths within the ‘comfort zone’

Ye, Peng; Wu, Xi; Gao, Dingguo; Deng, Shijie; Xu, Ning; Chen, Jun

doi:10.1016/j.displa.2020.101953

Cited by 9 publications

(5 citation statements)

References 61 publications

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“…Finally, dimensionally consistent feature maps obtained from the trunk branch and the mask branch are combined by the dot product to form the final output feature map. In the downsampling stage [ 27 ], the extracted feature maps are downsampled to a minimum size of 7 × 7 using the maximum pooling layer, and then in the upsampling stage, the feature map dimensions are expanded layer by layer using bilinear interpolation, and the feature maps obtained from downsampling are summed with them to obtain the final feature maps. The purpose of doing so is to combine global and local features to further enhance the characterization capability.…”

Section: Intersection Based On Resnet Network (A-resnet)mentioning

confidence: 99%

A New Generation of ResNet Model Based on Artificial Intelligence and Few Data Driven and Its Construction in Image Recognition Model

Wang¹,

Xu³

2022

Computational Intelligence and Neuroscience

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The paper proposes an A-ResNet model to improve ResNet. The residual attention module with shortcut connection is introduced to enhance the focus on the target object; the dropout layer is introduced to prevent the overfitting phenomenon and improve the recognition accuracy; the network architecture is adjusted to accelerate the training convergence speed and improve the recognition accuracy. The experimental results show that the A-ResNet model achieves a top-1 accuracy improvement of about 2% compared with the traditional ResNet network. Image recognition is one of the core technologies of computer vision, but its application in the field of tea is relatively small, and tea recognition still relies on sensory review methods. A total of 1,713 images of eight common green teas were collected, and the modeling effects of different network depths and different optimization algorithms were explored from the perspectives of predictive ability, convergence speed, model size, and recognition equilibrium of recognition models.

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Section: Intersection Based On Resnet Network (A-resnet)mentioning

confidence: 99%

A New Generation of ResNet Model Based on Artificial Intelligence and Few Data Driven and Its Construction in Image Recognition Model

Wang¹,

Xu³

2022

Computational Intelligence and Neuroscience

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show abstract

“…Since the Cobb angle became the gold standard for scoliosis, the focus of AIS treatment has been on how to reduce the scoliosis angle and slow the progression of the scoliosis curve, while the local or overall morphological imbalance of the trunk caused by scoliosis has been increasingly neglected. Because the visual impact of poor posture is more severe, it is important to assess the trunk imbalance in patients with AIS before developing a rehabilitation program and to design a customized program for the patient [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Study of Trunk Morphological Imbalance and Rehabilitation Outcome of Adolescent Idiopathic Scoliosis with Intelligent Medicine

Zhao

Huang

et al. 2022

Computational Intelligence and Neuroscience

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In recent years, artificial intelligence technology has been widely used in various medical fields to effectively assist physicians in patient treatment operations. In this paper, we design and implement a deep biblical network model-based orthotic design for adolescent idiopathic scoliosis to quickly and effectively assist physicians in designing orthotics for adolescent idiopathic scoliosis. A fuzzy set is used to express the knowledge of adolescent idiopathic scoliosis orthosis design, and a fuzzy reasoning based on the confidence level is implemented. Finally, the efficiency of the design of adolescent idiopathic scoliosis orthoses was improved by 50% through two cases of adolescent idiopathic scoliosis patients, and the deviation rate between the inference value and the actual operation value of the domain experts was less than 10%.

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“…The three-dimensional visualized images contain more detailed information, thus enabling efficient and accurate air pollution monitoring. The third application field is three-dimensional vision and display technologies (Gao et al, 2020 ; Ye et al, 2020 ). Compared with the ordinary two-dimensional screen display, three-dimensional technology can make the image is no longer confined to the plane of the screen (Sugita et al, 2019 ), as if it can come out of the screen, so that the audience has a feeling of immersion.…”

Section: Resultsmentioning

confidence: 99%

DIBR-Synthesized Image Quality Assessment With Texture and Depth Information

et al. 2021

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Accurately predicting the quality of depth-image-based-rendering (DIBR) synthesized images is of great significance in promoting DIBR techniques. Recently, many DIBR-synthesized image quality assessment (IQA) algorithms have been proposed to quantify the distortion that existed in texture images. However, these methods ignore the damage of DIBR algorithms on the depth structure of DIBR-synthesized images and thus fail to accurately evaluate the visual quality of DIBR-synthesized images. To this end, this paper presents a DIBR-synthesized image quality assessment metric with Texture and Depth Information, dubbed as TDI. TDI predicts the quality of DIBR-synthesized images by jointly measuring the synthesized image's colorfulness, texture structure, and depth structure. The design principle of our TDI includes two points: (1) DIBR technologies bring color deviation to DIBR-synthesized images, and so measuring colorfulness can effectively predict the quality of DIBR-synthesized images. (2) In the hole-filling process, DIBR technologies introduce the local geometric distortion, which destroys the texture structure of DIBR-synthesized images and affects the relationship between the foreground and background of DIBR-synthesized images. Thus, we can accurately evaluate DIBR-synthesized image quality through a joint representation of texture and depth structures. Experiments show that our TDI outperforms the competing state-of-the-art algorithms in predicting the visual quality of DIBR-synthesized images.

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DP3 signal as a neuro-indictor for attentional processing of stereoscopic contents in varied depths within the ‘comfort zone’

Cited by 9 publications

References 61 publications

A New Generation of ResNet Model Based on Artificial Intelligence and Few Data Driven and Its Construction in Image Recognition Model

A New Generation of ResNet Model Based on Artificial Intelligence and Few Data Driven and Its Construction in Image Recognition Model

Study of Trunk Morphological Imbalance and Rehabilitation Outcome of Adolescent Idiopathic Scoliosis with Intelligent Medicine

DIBR-Synthesized Image Quality Assessment With Texture and Depth Information

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