Computer vision algorithm for barley kernel identification, orientation estimation and surface structure assessment

2012 ◽  
Vol 87 ◽  
pp. 32-38 ◽  
Author(s):  
Piotr M. Szczypiński ◽  
Piotr Zapotoczny
Keyword(s):  
Computer Vision ◽  
Surface Structure ◽  
Orientation Estimation ◽  
Structure Assessment ◽  
Barley Kernel ◽  
Vision Algorithm

Measurement of cemented carbide-PCD microdrill geometry error based on Computer Vision algorithm

Measurement ◽  
2021 ◽  
pp. 110186
Author(s):  
Siti Nurfadilah Binti Jaini ◽  
Deug-Woo Lee ◽  
Kang-Seok Kim ◽  
Seung-Jun Lee
Keyword(s):  
Computer Vision ◽  
Cemented Carbide ◽  
Vision Algorithm ◽  
Geometry Error

Computer vision algorithm for identifying the post-irradiated nuclear fuel in a hotcell

2021 ◽  
Author(s):  
Helmi Fauzi R. ◽  
Prawito Prajitno ◽  
Sungkono ◽  
Refa Artika
Keyword(s):  
Computer Vision ◽  
Nuclear Fuel ◽  
Vision Algorithm ◽  
Irradiated Nuclear Fuel

scholarly journals Deep Neural Networks and Kernel Density Estimation for Detecting Human Activity Patterns from Geo-Tagged Images: A Case Study of Birdwatching on Flickr

2019 ◽  
Vol 8 (1) ◽  
pp. 45 ◽  
Author(s):  
Caglar Koylu ◽  
Chang Zhao ◽  
Wei Shao
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Social Media ◽  
Spatial Analysis ◽  
Citizen Science ◽  
Human Activity ◽  
Activity Patterns ◽  
Kernel Density ◽  
Human Activity Patterns ◽  
Vision Algorithm

Thanks to recent advances in high-performance computing and deep learning, computer vision algorithms coupled with spatial analysis methods provide a unique opportunity for extracting human activity patterns from geo-tagged social media images. However, there are only a handful of studies that evaluate the utility of computer vision algorithms for studying large-scale human activity patterns. In this article, we introduce an analytical framework that integrates a computer vision algorithm based on convolutional neural networks (CNN) with kernel density estimation to identify objects, and infer human activity patterns from geo-tagged photographs. To demonstrate our framework, we identify bird images to infer birdwatching activity from approximately 20 million publicly shared images on Flickr, across a three-year period from December 2013 to December 2016. In order to assess the accuracy of object detection, we compared results from the computer vision algorithm to concept-based image retrieval, which is based on keyword search on image metadata such as textual description, tags, and titles of images. We then compared patterns in birding activity generated using Flickr bird photographs with patterns identified using eBird data—an online citizen science bird observation application. The results of our eBird comparison highlight the potential differences and biases in casual and serious birdwatching, and similarities and differences among behaviors of social media and citizen science users. Our analysis results provide valuable insights into assessing the credibility and utility of geo-tagged photographs in studying human activity patterns through object detection and spatial analysis.

scholarly journals Deep learning computer vision algorithm for detecting kidney stone composition

2020 ◽  
Vol 125 (6) ◽  
pp. 920-924 ◽  
Author(s):  
Kristian M. Black ◽  
Hei Law ◽  
Ali Aldoukhi ◽  
Jia Deng ◽  
Khurshid R. Ghani
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Kidney Stone ◽  
Stone Composition ◽  
Vision Algorithm
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