Automated detection and reacquisition of motion‐degraded images in fetal HASTE imaging at 3 T

2021 ◽  
Author(s):  
Borjan Gagoski ◽  
Junshen Xu ◽  
Paul Wighton ◽  
M. Dylan Tisdall ◽  
Robert Frost ◽  
...  
Keyword(s):  
Automated Detection ◽  
Degraded Images

Automated detection of epithelial changes in colorectal carcinoma

2012 ◽  
Vol 50 (05) ◽  
Author(s):  
G Valcz ◽  
I Bándi ◽  
B Wichmann ◽  
A Patai ◽  
D Szabó ◽  
...  
Keyword(s):  
Colorectal Carcinoma ◽  
Automated Detection

Study on Intelligent Security Camera Systems for the Automated Detection of Nighttime Snatching Incidents using Deep Neural Networks

2016 ◽  
Vol 136 (10) ◽  
pp. 727-734 ◽  
Author(s):  
Itaru Nagayama ◽  
Akira Miyahara ◽  
Koichi Shimabukuro
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Automated Detection ◽  
Camera Systems ◽  
Intelligent Security

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Automated Detection of Pin Defects on Counterfeit Microelectronics

2018 ◽  
Author(s):  
Pallabi Ghosh ◽  
Domenic Forte ◽  
Damon L. Woodard ◽  
Rajat Subhra Chakraborty
Keyword(s):  
Integrated Circuits ◽  
Depth Map ◽  
Optical Microscope ◽  
Automated Detection ◽  
Detection Methods ◽  
Global Supply Chains ◽  
Subject Matter Experts ◽  
Security Breaches ◽  
The Past ◽  
Skilled Subject

Abstract Counterfeit electronics constitute a fast-growing threat to global supply chains as well as national security. With rapid globalization, the supply chain is growing more and more complex with components coming from a diverse set of suppliers. Counterfeiters are taking advantage of this complexity and replacing original parts with fake ones. Moreover, counterfeit integrated circuits (ICs) may contain circuit modifications that cause security breaches. Out of all types of counterfeit ICs, recycled and remarked ICs are the most common. Over the past few years, a plethora of counterfeit IC detection methods have been created; however, most of these methods are manual and require highly-skilled subject matter experts (SME). In this paper, an automated bent and corroded pin detection methodology using image processing is proposed to identify recycled ICs. Here, depth map of images acquired using an optical microscope are used to detect bent pins, and segmented side view pin images are used to detect corroded pins.

Automated Detection for Road Marking Quality, using Visual Based Machine Learning

2020 ◽  
Author(s):  
Firas Habbal ◽  
Fawaz Habbal ◽  
Abdualla Alnuaimi ◽  
Shafia Alkheyaili ◽  
Ammar Safi
Keyword(s):  
Machine Learning ◽  
Automated Detection ◽  
Road Marking

Automated detection and classification of shoulder arthroplasty models using deep learning

2020 ◽  
Vol 49 (10) ◽  
pp. 1623-1632
Author(s):  
Paul H. Yi ◽  
Tae Kyung Kim ◽  
Jinchi Wei ◽  
Xinning Li ◽  
Gregory D. Hager ◽  
...  
Keyword(s):  
Deep Learning ◽  
Shoulder Arthroplasty ◽  
Automated Detection
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