scholarly journals Face Segmentation: A Journey From Classical to Deep Learning Paradigm, Approaches, Trends, and Directions

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 58683-58699
Author(s):  
Khalil Khan ◽  
Rehan Ullah Khan ◽  
Kashif Ahmad ◽  
Farman Ali ◽  
Kyung-Sup Kwak
Keyword(s):  
Deep Learning ◽  
Learning Paradigm ◽  
Face Segmentation

scholarly journals Expert camouflage-breakers can accurately localize search targets

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Fallon Branch ◽  
Allison JoAnna Lewis ◽  
Isabella Noel Santana ◽  
Jay Hegdé
Keyword(s):  
Deep Learning ◽  
Visual Search ◽  
Target Localization ◽  
Specific Training ◽  
Learning Paradigm ◽  
Accuracy And Precision ◽  
Per Se ◽  
Special Case

AbstractCamouflage-breaking is a special case of visual search where an object of interest, or target, can be hard to distinguish from the background even when in plain view. We have previously shown that naive, non-professional subjects can be trained using a deep learning paradigm to accurately perform a camouflage-breaking task in which they report whether or not a given camouflage scene contains a target. But it remains unclear whether such expert subjects can actually detect the target in this task, or just vaguely sense that the two classes of images are somehow different, without being able to find the target per se. Here, we show that when subjects break camouflage, they can also localize the camouflaged target accurately, even though they had received no specific training in localizing the target. The localization was significantly accurate when the subjects viewed the scene as briefly as 50 ms, but more so when the subjects were able to freely view the scenes. The accuracy and precision of target localization by expert subjects in the camouflage-breaking task were statistically indistinguishable from the accuracy and precision of target localization by naive subjects during a conventional visual search where the target ‘pops out’, i.e., is readily visible to the untrained eye. Together, these results indicate that when expert camouflage-breakers detect a camouflaged target, they can also localize it accurately.

A Deep Learning Paradigm for Automated Face Attendance

2019 ◽  
pp. 39-50
Author(s):  
Rahul Kumar Gupta ◽  
Shreeja Lakhlani ◽  
Zahabiya Khedawala ◽  
Vishal Chudasama ◽  
Kishor P. Upla
Keyword(s):  
Deep Learning ◽  
Learning Paradigm

Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm

2018 ◽  
Vol 155 ◽  
pp. 165-177 ◽  
Author(s):  
Mainak Biswas ◽  
Venkatanareshbabu Kuppili ◽  
Damodar Reddy Edla ◽  
Harman S. Suri ◽  
Luca Saba ◽  
...  
Keyword(s):  
Deep Learning ◽  
Risk Stratification ◽  
Tissue Characterization ◽  
Learning Paradigm ◽  
Ultrasound Tissue Characterization ◽  
Liver Ultrasound

scholarly journals A Review on a Deep Learning Perspective in Brain Cancer Classification

Cancers ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Gopal S. Tandel ◽  
Mainak Biswas ◽  
Omprakash G. Kakde ◽  
Ashish Tiwari ◽  
Harman S. Suri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Cancer ◽  
Cost Effective ◽  
World Health ◽  
Imaging Modalities ◽  
Computer Assisted ◽  
Learning Paradigm ◽  
Brain Scans ◽  
Magnetic Resonance Imaging Mri ◽  
Health Organization

A World Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. It is of critical importance that cancer be detected earlier so that many of these lives can be saved. Cancer grading is an important aspect for targeted therapy. As cancer diagnosis is highly invasive, time consuming and expensive, there is an immediate requirement to develop a non-invasive, cost-effective and efficient tools for brain cancer characterization and grade estimation. Brain scans using magnetic resonance imaging (MRI), computed tomography (CT), as well as other imaging modalities, are fast and safer methods for tumor detection. In this paper, we tried to summarize the pathophysiology of brain cancer, imaging modalities of brain cancer and automatic computer assisted methods for brain cancer characterization in a machine and deep learning paradigm. Another objective of this paper is to find the current issues in existing engineering methods and also project a future paradigm. Further, we have highlighted the relationship between brain cancer and other brain disorders like stroke, Alzheimer’s, Parkinson’s, and Wilson’s disease, leukoriaosis, and other neurological disorders in the context of machine learning and the deep learning paradigm.

Leveraging the Deep Learning Paradigm for Continuous Affect Estimation from Facial Expressions

2020 ◽  
pp. 1-1
Author(s):  
Meshia Cedric Oveneke ◽  
Yong Zhao ◽  
Ercheng Pei ◽  
Abel Diaz Berenguer ◽  
Dongmei Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Facial Expressions ◽  
Learning Paradigm

scholarly journals A Feasibility Test on Preventing PRMDs Based on Deep Learning

2019 ◽  
Vol 33 ◽  
pp. 10005-10006
Author(s):  
So-Hyun Park ◽  
Sun-Young Ihm ◽  
Aziz Nasridinov ◽  
Young-Ho Park
Keyword(s):  
Deep Learning ◽  
Musculoskeletal Disorders ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Experimental Results ◽  
Video Classification ◽  
Learning Paradigm ◽  
Feasibility Test ◽  
Piano Playing

This study proposes a method to reduce the playing-related musculoskeletal disorders (PRMDs) that often occur among pianists. Specifically, we propose a feasibility test that evaluates several state-of-the-art deep learning algorithms to prevent injuries of pianist. For this, we propose (1) a C3P dataset including various piano playing postures and show (2) the application of four learning algorithms, which demonstrated their superiority in video classification, to the proposed C3P datasets. To our knowledge, this is the first study that attempted to apply the deep learning paradigm to reduce the PRMDs in pianist. The experimental results demonstrated that the classification accuracy is 80% on average, indicating that the proposed hypothesis about the effectiveness of the deep learning algorithms to prevent injuries of pianist is true.

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