scholarly journals Peer Review #2 of "A novel fully convolutional network for visual saliency prediction (v0.1)"

2020 ◽  
Keyword(s):  
Peer Review ◽  
Visual Saliency ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Saliency Prediction

scholarly journals A novel fully convolutional network for visual saliency prediction

2020 ◽  
Vol 6 ◽  
pp. e280
Author(s):  
Bashir Muftah Ghariba ◽  
Mohamed S. Shehata ◽  
Peter McGuire
Keyword(s):  
Deep Learning ◽  
Visual Saliency ◽  
Superior Performance ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Proposed Model ◽  
Saliency Prediction ◽  
Benchmark Datasets ◽  
The Many ◽  
Deep Learning Model

A human Visual System (HVS) has the ability to pay visual attention, which is one of the many functions of the HVS. Despite the many advancements being made in visual saliency prediction, there continues to be room for improvement. Deep learning has recently been used to deal with this task. This study proposes a novel deep learning model based on a Fully Convolutional Network (FCN) architecture. The proposed model is trained in an end-to-end style and designed to predict visual saliency. The entire proposed model is fully training style from scratch to extract distinguishing features. The proposed model is evaluated using several benchmark datasets, such as MIT300, MIT1003, TORONTO, and DUT-OMRON. The quantitative and qualitative experiment analyses demonstrate that the proposed model achieves superior performance for predicting visual saliency.

scholarly journals Fully Convolutional Network and Visual Saliency-Based Automatic Optic Disc Detection in Retinal Fundus Images

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xiaosheng Yu ◽  
Ying Wang ◽  
Siqi Wang ◽  
Nan Hu
Keyword(s):  
Optic Disc ◽  
Prior Information ◽  
Detection Method ◽  
Visual Saliency ◽  
Fundus Images ◽  
Image Patch ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Retinal Fundus Images ◽  
Retinal Fundus

We present in this paper a novel optic disc detection method based on a fully convolutional network and visual saliency in retinal fundus images. Firstly, we employ the morphological reconstruction-based object detection method to locate the optic disc region roughly. According to the location result, a 400 × 400 image patch that covers the whole optic disc is obtained by cropping the original retinal fundus image. Secondly, the Simple Linear Iterative Cluster approach is utilized to segment such an image patch into many smaller superpixels. Thirdly, each superpixel is assigned a uniform initial saliency value according to the background prior information based on the assumption that the superpixels located on the boundary of the image belong to the background. Meanwhile, we use a pretrained fully convolutional network to extract the deep features from different layers of the network and design the strategy to represent each superpixel by the deep features. Finally, both the background prior information and the deep features are integrated into the single-layer cellular automata framework to gain the accurate optic disc detection result. We utilize the DRISHTI-GS dataset and RIM-ONE r3 dataset to evaluate the performance of our method. The experimental results demonstrate that the proposed method can overcome the influence of intensity inhomogeneity, weak contrast, and the complex surroundings of the optic disc effectively and has superior performance in terms of accuracy and robustness.

scholarly journals MaskNet: A Fully-Convolutional Network to Estimate Inlier Points

2020 ◽  
Author(s):  
Vinit Sarode ◽  
Animesh Dhagat ◽  
Rangaprasad Arun Srivatsan ◽  
Nicolas Zevallos ◽  
Simon Lucey ◽  
...  
Keyword(s):  
Convolutional Network ◽  
Fully Convolutional Network

Lighter and Faster Cross-Concatenated Multi-Scale Residual Block Based Network for Visual Saliency Prediction

2021 ◽  
Author(s):  
Sai Phani Kumar Malladi ◽  
Jayanta Mukhopadhyay ◽  
Chaker Larabi ◽  
Santanu Chaudhury
Keyword(s):  
Visual Saliency ◽  
Multi Scale ◽  
Saliency Prediction ◽  
Block Based ◽  
Residual Block

scholarly journals Parallel Fully Convolutional Network for Semantic Segmentation

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 673-682
Author(s):  
Jian Ji ◽  
Xiaocong Lu ◽  
Mai Luo ◽  
Minghui Yin ◽  
Qiguang Miao ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Convolutional Network ◽  
Fully Convolutional Network
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