A Model for Identifying Historical Landmarks of Bangladesh from Image Content Using a Depth-Wise Convolutional Neural Network

2019 ◽  
pp. 444-454 ◽  
Author(s):  
Afsana Ahsan Jeny ◽  
Masum Shah Junayed ◽  
Syeda Tanjila Atik ◽  
Sazzad Mahamd
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Content

scholarly journals A Saliency Detection and Gram Matrix Transform-Based Convolutional Neural Network for Image Emotion Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zelin Deng ◽  
Qiran Zhu ◽  
Pei He ◽  
Dengyong Zhang ◽  
Yuansheng Luo
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Saliency Detection ◽  
Supplementary Information ◽  
Gram Matrix ◽  
Visual Features ◽  
Feature Maps ◽  
Image Content ◽  
Content Information

Using the convolutional neural network (CNN) method for image emotion recognition is a research hotspot of deep learning. Previous studies tend to use visual features obtained from a global perspective and ignore the role of local visual features in emotional arousal. Moreover, the CNN shallow feature maps contain image content information; such maps obtained from shallow layers directly to describe low-level visual features may lead to redundancy. In order to enhance image emotion recognition performance, an improved CNN is proposed in this work. Firstly, the saliency detection algorithm is used to locate the emotional region of the image, which is served as the supplementary information to conduct emotion recognition better. Secondly, the Gram matrix transform is performed on the CNN shallow feature maps to decrease the redundancy of image content information. Finally, a new loss function is designed by using hard labels and probability labels of image emotion category to reduce the influence of image emotion subjectivity. Extensive experiments have been conducted on benchmark datasets, including FI (Flickr and Instagram), IAPSsubset, ArtPhoto, and Abstract. The experimental results show that compared with the existing approaches, our method has a good application prospect.

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

No-Reference Utility Estimation with a Convolutional Neural Network

2018 ◽  
Vol 2018 (9) ◽  
pp. 202-1-202-6 ◽  
Author(s):  
Edward T. Scott ◽  
Sheila S. Hemami
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Utility Estimation

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

Hierarchical Auto-associative Polynomial Convolutional Neural Network (HAP-CNN)

2018 ◽  
Vol 2018 (10) ◽  
pp. 338-1-338-6
Author(s):  
Patrick Martell ◽  
Vijayan Asari
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Defect Detection of Axle Box Cover Device Fixing Bolts in Metro Based on Convolutional Neural Network

2020 ◽  
Author(s):  
Yao Yang ◽  
Yuanjiang Hu ◽  
Lingling Chen ◽  
Xiaoman Liu ◽  
Na Qin ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Defect Detection

Convolutional Neural Network for Determining Sensitivity Matrix in ETI

2020 ◽  
Author(s):  
Haitao Ma ◽  
Shihong Yue ◽  
Jian Lu ◽  
Sidolla Yem ◽  
Huaxiang Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sensitivity Matrix
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