Challenges and Applications for Implementing Machine Learning in Computer Vision

2020 ◽  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Learning In Computer Vision

scholarly journals Machine Learning in Computer Vision

2020 ◽  
Vol 167 ◽  
pp. 1444-1451 ◽  
Author(s):  
Asharul Islam Khan ◽  
Salim Al-Habsi
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Learning In Computer Vision

Machine learning in computer vision

2001 ◽  
Vol 15 (8) ◽  
pp. 693-705 ◽  
Author(s):  
Floriana Esposito ◽  
Donato Malerba
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Learning In Computer Vision

Challenges and Applications for Implementing Machine Learning in Computer Vision

2020 ◽  
pp. 119-135
Author(s):  
Hiral R. Patel ◽  
Ajay M Patel ◽  
Satyen M. Parikh
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Learning In Computer Vision

The chapter introduces machine learning and why it is important. Machine learning is generally used to find knowledge from unknown data. There are many approaches and algorithms available for performing machine learning. Different kinds of algorithms are available to find different patterns from the data. This chapter focuses on different approaches with different usage.

scholarly journals MeshCut data augmentation for deep learning in computer vision

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243613
Author(s):  
Wei Jiang ◽  
Kai Zhang ◽  
Nan Wang ◽  
Miao Yu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Data Augmentation ◽  
State Of The Art ◽  
Network Models ◽  
Neural Network Models ◽  
Augmentation Strategies ◽  
Future Data ◽  
Learning In Computer Vision

To solve overfitting in machine learning, we propose a novel data augmentation method called MeshCut, which uses a mesh-like mask to segment the whole image to achieve more partial diversified information. In our experiments, this strategy outperformed the existing augmentation strategies and achieved state-of-the-art results in a variety of computer vision tasks. MeshCut is also an easy-to-implement strategy that can efficiently improve the performance of the existing convolutional neural network models by a good margin without careful hand-tuning. The performance of such a strategy can be further improved by incorporating it into other augmentation strategies, which can make MeshCut a promising baseline strategy for future data augmentation algorithms.

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