The Kirsch-Laplacian edge detection algorithm for predicting iris-based desease

Cho, Young-Bok; Lee, Sang-Ho; Woo, Sung-Hee，

doi:10.1109/cscwd.2017.8066753

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Computer vision technology has been implemented in various industries nowadays. Starting from simply logging you in to your smartphone [1], detecting disease through your eyes [2], to autonomous driving. It has been an exciting technology since its inception in 1970 [3] that helps automate tasks that the human visual system can do through high level understanding from digital images.…”

Section: Introductionmentioning

confidence: 99%

Simple Computer Vision Algorithm Production using OpenCV for “Virtual Ecosystem” project.

Willis

Hasan

2020

International Journal of New Media Technology

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The main purposes of this research is to produce aComputer Vision (CV) algorithm using various library in pythonprogramming language (mainly OpenCV, Numpy, and ZBar) toautomate extraction and process information from an analogdrawing into a digital image to be used in the “Virtual Ecosystem”project. The Computer Vision step will include detecting datawithin the analog drawing using QR-code, determine drawingarea, replace white background with certain threshold withtransparency, and finally save the digital image following requiredratio.

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Section: Introductionmentioning

confidence: 99%

Simple Computer Vision Algorithm Production using OpenCV for “Virtual Ecosystem” project.

Willis

Hasan

2020

International Journal of New Media Technology

View full text Add to dashboard Cite

show abstract

“…Image classification, which can be defined as categorizing images into one of several predefined classes, is a fundamental problem in computer vision CNNs are a type of feedforward neural network in machine learning. A CNN is formed by a stack of layers [12,13,16] or a directed acyclic graph of layers [17,18,19]. A CNN usually combines the following five types of layers.…”

Section: Deep Convolution Neural Networkmentioning

confidence: 99%