A Deep-Learning Based Model for Emotional Evaluation of Video Clips

Kim, Byoungjun; Lee, Joonwhoan

doi:10.5391/ijfis.2018.18.4.245

Cited by 18 publications

(8 citation statements)

References 17 publications

(22 reference statements)

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“…2 the same and replace the output layer with a 2-dimension fullyconnected layer to complete the regression task. In Table 2, the framework of [10] is a combination of 3DCNN and LSTM. However they gain poor performance due to the degradation problem of deep network.…”

Section: Number Of Framesmentioning

confidence: 99%

3DCNN-Based Palpation Localization with Temporal Attention Module

Huang

Zhu

2022

2022 IEEE International Conference on Image Processing (ICIP)

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Palpation is necessary in Traditional Chinese Medicine (TCM). In TCM, doctors need to touch patient's wrist and exert pressure on it to obtain physiological signal. Locating the pulse position is an important step in palpation. Nowadays, researchers have proposed numerous methods to locate the pulse position accurately. In this paper, we propose an accurate and effective framework to locate the pulse position. Our framework is based on 3dimensional convolutional neural network (3DCNN), and we propose a novel temporal attention module to further improve the performance. Our framework achieves superior results, the accuracy of locating the pulse position on a video with a resolution of 2048×1088 within 100 pixels is 97%. More and more cross-domain applications of computer science and medical science provides more ways for doctors to treat patients, such as telemedicine, computed tomography image analysis, and physiological signal analysis. Our research presents the great potential to be applied to palpation automation and intelligent medical treatment.

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Section: Number Of Framesmentioning

confidence: 99%

3DCNN-Based Palpation Localization with Temporal Attention Module

Huang

Zhu

2022

2022 IEEE International Conference on Image Processing (ICIP)

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“…First, the simulation function is used to observe the classification result of the sample data by the training network. The simulation result is Yc = [4,5,6,12,1,8,12,6,10,12], where the different numbers indicates different category.…”

Section: ) Creation and Training Of Networkmentioning

confidence: 99%

Construction of a Neural Network and its Application on Target Classification

Fan

et al. 2019

IEEE Access

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With the arrival of big data age, the deep convolutional neural networks (DCNNs) with more hidden layers have a more complex network structure and more powerful ability than traditional machine learning methods for feature learning and feature expression. This paper first proposes a model of the DCNN to discuss the basic structure of model, convolutional feature extraction and learning algorithm of convolutional neural network; then, mainly introduces several aspects, that is, the construction of the typical network structure, the training methods, and the parameter settings of network model to be improved and optimized. Moreover, the network model is applied to the classification and recognition of Antarctic hydrological features and compared with some existing classification methods. The novelty of this paper mainly includes two aspects, i.e., the one is that the design and construction of the structure of deep neural network based on deep learning method are performed, namely, connection, weight, calculator, learning training of network, and other design. The other is classifying hydrological characteristics of Pritz Bay in Antarctica's images by the DCNN. The results show that the correct recognition rate of the model method constructed by this paper is the highest. Finally, some problems in the current research are briefly summarized and discussed, and the new direction in the future development is forecasted. INDEX TERMS Deep learning, convolutional neural network, deep belief network, hydrological feature recognition.

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“…Although, there are many vision-based methods detecting the cars on the road [16], our proposed method apply a technique of convolutional network that was used widely in several applications [17]. The first step of the proposed method is to find the car distribution in the image under both daytime and nighttime illumination conditions.…”

Section: Car Detection Using Yolomentioning

confidence: 99%

Viewpoint Classification for the Bus-Waiting Blinds in Congested Traffic Environment

Tangsuksant

Noda

Kitagawa

et al. 2019

IJFIS

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To provide an effective notification service for the blinds awaiting the bus, it is crucial to have a viewpoint classification technique in which a viewpoint is defined with tilt and panning of camera. This paper proposes a viewpoint classification method using the car distribution information in the congested traffic environment. The proposed method takes four steps for classification. First, the YOLO algorithm is used to detect the car positions in the images. Second, the car positions are normalized for feature computation. Third, nineteen simple features are extracted and finally, the viewpoint classification is conducted. The proposed method uses the information gain measure to select relevant ones from the extracted features, and uses the Random Forest algorithm as a classifier. In the experiments, the proposed method has been tested for various roadside scenarios of congested traffic in day and night. The accuracies for car detection and viewpoint classification were 79.90% and 86.00%, respectively, which are improved compared to the prior work.

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A Deep-Learning Based Model for Emotional Evaluation of Video Clips

Cited by 18 publications

References 17 publications

3DCNN-Based Palpation Localization with Temporal Attention Module

3DCNN-Based Palpation Localization with Temporal Attention Module

Construction of a Neural Network and its Application on Target Classification

Viewpoint Classification for the Bus-Waiting Blinds in Congested Traffic Environment

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