Convolution Neural Network

List of Articles Convolution Neural Network

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1 - Edge Detection and Identification using Deep Learning to Identify Vehicles
Zohreh Dorrani Hassan Farsi Sajad Mohammadzadeh
10.52547/jist.16385.10.39.201

20.1001.1.23221437.2022.10.39.2.6

A deep convolution neural network (CNN) is used to detect the edge. First, the initial features are extracted using VGG-16, which consists of 5 convolutions, each step is connected to a pooling layer. For edge detection of the image, it is necessary to extract informati More

A deep convolution neural network (CNN) is used to detect the edge. First, the initial features are extracted using VGG-16, which consists of 5 convolutions, each step is connected to a pooling layer. For edge detection of the image, it is necessary to extract information of different levels from each layer to the pixel space of the edge, and then re-extract the feature, and perform sampling. The attributes are mapped to the pixel space of the edge and a threshold extractor of the edges. It is then compared with a background model. Using background subtraction, foreground objects are detected. The Gaussian mixture model is used to detect the vehicle. This method is performed on three videos, and compared with other methods; the results show higher accuracy. Therefore, the proposed method is stable against sharpness, light, and traffic. Moreover, to improve the detection accuracy of the vehicle, shadow removal conducted, which uses a combination of color and contour features to identify the shadow. For this purpose, the moving target is extracted, and the connected domain is marked to be compared with the background. The moving target contour is extracted, and the direction of the shadow is checked according to the contour trend to obtain shadow points and remove these points. The results show that the proposed method is very resistant to changes in light, high-traffic environments, and the presence of shadows, and has the best performance compared to the current methods. Manuscript profile
Open Access Article
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2 - Hierarchical Weighted Framework for Emotional Distress Detection using Personalized Affective Cues
Nagesh Jadhav
10.52547/jist.16499.10.38.89

20.1001.1.23221437.2022.10.38.8.0

Emotional distress detection has become a hot topic of research in recent years due to concerns related to mental health and complex nature distress identification. One of the challenging tasks is to use non-invasive technology to understand and detect emotional distres More

Emotional distress detection has become a hot topic of research in recent years due to concerns related to mental health and complex nature distress identification. One of the challenging tasks is to use non-invasive technology to understand and detect emotional distress in humans. Personalized affective cues provide a non-invasive approach considering visual, vocal, and verbal cues to recognize the affective state. In this paper, we are proposing a multimodal hierarchical weighted framework to recognize emotional distress. We are utilizing negative emotions to detect the unapparent behavior of the person. To capture facial cues, we have employed hybrid models consisting of a transfer learned residual network and CNN models. Extracted facial cue features are processed and fused at decision using a weighted approach. For audio cues, we employed two different models exploiting the LSTM and CNN capabilities fusing the results at the decision level. For textual cues, we used a BERT transformer to learn extracted features. We have proposed a novel decision level adaptive hierarchical weighted algorithm to fuse the results of the different modalities. The proposed algorithm has been used to detect the emotional distress of a person. Hence, we have proposed a novel algorithm for the detection of emotional distress based on visual, verbal, and vocal cues. Experiments on multiple datasets like FER2013, JAFFE, CK+, RAVDESS, TESS, ISEAR, Emotion Stimulus dataset, and Daily-Dialog dataset demonstrates the effectiveness and usability of the proposed architecture. Experiments on the enterface'05 dataset for distress detection has demonstrated significant results. Manuscript profile

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List of Articles Convolution Neural Network