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Artificial Intelligence-Based Approaches for Brain Tumor Segmentation in MRI: A Review. [PDF]

NMR Biomed
Bibi K, Nawaz M, Khan S, Daud M, Masood A, Abdelgawad MA, Abbasi SMT, Rizwan, Khan A, Yuan W.   +9 more
europepmc   +1 more source

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M-FCN: Effective Fully Convolutional Network-Based Airplane Detection Framework

IEEE Geoscience and Remote Sensing Letters, 2017
Airplane detection is a challenging problem in complex remote sensing imaging. In this letter, an effective airplane detection framework called Markov random field-fully convolutional network (M-FCN) is proposed. The M-FCN uses a cascade strategy that consists of an FCN-based coarse candidate extraction stage, a multi-Markov random field (multi-MRF ...
Yiding Yang, Yin Zhuang, Hao Shi
exaly   +3 more sources

R-FCN++: Towards Accurate Region-Based Fully Convolutional Networks for Object Detection

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
Region based detectors like Faster R-CNN and R-FCN have achieved leading performance on object detection benchmarks. However, in Faster R-CNN, RoI pooling is used to extract feature of each region, which might harm the classification as the RoI pooling loses spatial resolution.
Zeming Li, Yilun Chen, Gang Yu 0002, Yangdong Deng   +3 more
openaire   +3 more sources

L-FCN: A lightweight fully convolutional network for biomedical semantic segmentation

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018
For the past few years, deep learning-based methods have been widely used in the field of biomedical imaging. In biomedical image processing, the typical application of deep learning is semantic segmentation. However, the classical deep learning methods require higher hardware consumption and computational costs.
Kaiyue Li, Guangtai Ding, Haitao Wang
exaly   +3 more sources

NB-FCN: Real-Time Accurate Crack Detection in Inspection Videos Using Deep Fully Convolutional Network and Parametric Data Fusion

IEEE Transactions on Instrumentation and Measurement, 2020
For the safe operations of nuclear power plants, it is important to inspect the reactor internal components frequently. However, current practice involves human technicians who review the inspection videos and identify cracks on metallic surfaces of underwater components, which is costly, time-consuming, and subjective.
Mohammad Reza Jahanshahi, Fu-Chen Chen
exaly   +2 more sources

HG-FCN: Hierarchical Grid Fully Convolutional Network for Fast VVC Intra Coding

IEEE Transactions on Circuits and Systems for Video Technology, 2022
Zhibo Chen, Shilin Wu
exaly   +3 more sources

Sparse fully convolutional network for face labeling

Neurocomputing, 2019
© 2018 Elsevier B.V. This paper proposes a sparse fully convolutional network (FCN) for face labeling. FCN has demonstrated strong capabilities in learning representations for semantic segmentation.
Shiping Wen, Tingwen Huang, Zhigang Zeng   +2 more
exaly   +2 more sources

C-FCN: Corners-based fully convolutional network for visual object detection

Multimedia Tools and Applications, 2020
Object detection has achieved significantly progresses in recent years. Proposal-based methods have become the mainstream object detectors, achieving excellent performance on accurate recognition and localization of objects. However, region proposal generation is still a bottleneck.
Lin Jiao, Rujing Wang, Chengjun Xie
openaire   +2 more sources

Text or Non-text Image Classification using Fully Convolution Network (FCN)

2020 International Conference on Contemporary Computing and Applications (IC3A), 2020
The semantic information in a natural scene plays a vital role in image understanding. One of the semantic information present in a natural image is the text. It can be utilized for analyzing several computer vision applications. The proposed work focuses on the new task of classifying the text images from a bulk of natural images.
Neeraj Gupta, Anand Singh Jalal
openaire   +1 more source

TER-FCN: Tensor Einstein Reduced Fully Connected Network applied to Region-based Convolutional Neural Networks for object detection

Signal, Image and Video Processing
Alaa El Ichi, Wissam Kaddah, Marwa El Bouz, Isabelle Badoc   +3 more
exaly   +2 more sources