An automatic counting system for transparent pelagic fish eggs based on computer vision

2015 ◽  
Vol 67 ◽  
pp. 8-13 ◽  
Author(s):  
Yane Duan ◽  
Lars Helge Stien ◽  
Anders Thorsen ◽  
Ørjan Karlsen ◽  
Nina Sandlund ◽  
...  
Keyword(s):  
Computer Vision ◽  
Pelagic Fish ◽  
Fish Eggs ◽  
Counting System ◽  
Automatic Counting

scholarly journals DNA identification and seasonal changes of pelagic fish eggs in Hiroshima Bay

2017 ◽  
Vol 83 (2) ◽  
pp. 215-217 ◽  
Author(s):  
KENTARO KAWAI ◽  
RYUMA OKAZAKI ◽  
SATOSHI TOMANO ◽  
TETSUYA UMINO
Keyword(s):  
Seasonal Changes ◽  
Pelagic Fish ◽  
Fish Eggs ◽  
Dna Identification

scholarly journals Optimization of an automatic counting system for the quantification ofStaphylococcus epidermidiscells in biofilms

2013 ◽  
Vol 54 (7) ◽  
pp. 750-757 ◽  
Author(s):  
Ana Isabel Freitas ◽  
Carlos Vasconcelos ◽  
Manuel Vilanova ◽  
Nuno Cerca
Keyword(s):  
Counting System ◽  
Automatic Counting

Efficient Rebar Count Scheme Based on Image Recognition in Mechanical Engineering

2013 ◽  
Vol 340 ◽  
pp. 805-808
Author(s):  
Yi Long Lei ◽  
Jiong Zhao Yang ◽  
Yu Huan Zhang
Keyword(s):  
Image Processing ◽  
Image Recognition ◽  
Enterprise Management ◽  
Labor Intensity ◽  
Root Number ◽  
Image Processing Algorithm ◽  
Counting System ◽  
Automatic Counting ◽  
Steel Bar ◽  
System Composition

Nowadays, along with the higher requirement of the customer and the standardization of enterprise management, the finished product of steel bar production must be standardization packaged by root number; management requirements of bar fixed bundle of sticks are more stringent. The artificial count is used into the most of the steel bar production recently. So there are many problems. Such as labor intensity, easy fatigue, less efficient, and error-prone. The image recognition technology for online automatic counting system for a given period of time. It also can improve the speed and make the product more accuracy. This paper mainly talks about the system composition and image processing algorithm.

Inter-annual variation of pelagic fish eggs, larvae and juveniles assembles in summer in the Yangtze River Estuary

2015 ◽  
Vol 35 (21) ◽  
Author(s):  
刘守海 LIU Shouhai ◽  
王金辉 WANG Jinhui ◽  
刘材材 LIU Caicai ◽  
秦玉涛 QIN Yutao ◽  
刘志国 LIU Zhiguo ◽  
...  
Keyword(s):  
Yangtze River ◽  
Annual Variation ◽  
River Estuary ◽  
Pelagic Fish ◽  
Yangtze River Estuary ◽  
The Yangtze River ◽  
Fish Eggs ◽  
Larvae And Juveniles ◽  
The Yangtze River Estuary

scholarly journals Technical note: AI-Track-tive: automated fission track recognition using computer vision (Artificial Intelligence)

2020 ◽  
Author(s):  
Simon Nachtergaele ◽  
Johan De Grave
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Neural Networks ◽  
Fission Track ◽  
Detection Algorithm ◽  
Technical Note ◽  
Success Rates ◽  
Automatic Counting ◽  
First Time

Abstract. Artificial intelligence techniques such as deep neural networks and computer vision are developed for fission track recognition and included in a computer program for the first time. These deep neural networks use the Yolov3 object detection algorithm, which is currently one of the most powerful and fastest object recognition algorithms. These deep neural networks can be used in new software called AI-Track-tive. The developed program successfully finds most of the fission tracks in the microscope images, however, the user still needs to supervise the automatic counting. The success rates of the automatic recognition range from 70 % to 100 % depending on the areal track densities in apatite and (muscovite) external detector. The success rate generally decreases for images with high areal track densities, because overlapping tracks are less easily recognizable for computer vision techniques.

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