Measuring radiata pine seedling morphological features using a machine vision system

2021 ◽  
Vol 189 ◽  
pp. 106355
Author(s):  
Benjamin McGuinness ◽  
Mike Duke ◽  
Chi Kit Au ◽  
Shen Hin Lim
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Radiata Pine ◽  
Morphological Features ◽  
Machine Vision System

Recognition and Classification of Food Grains, Fruits and Flowers Using Machine Vision

2009 ◽  
Vol 5 (4) ◽  
Author(s):  
Dayanand G Savakar ◽  
Basavaraj S Anami
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Texture Features ◽  
Morphological Features ◽  
Machine Vision System ◽  
Food Grains ◽  
Colour Features ◽  
Classification Of Images ◽  
Color Texture

In this paper, we have presented different methodologies devised for recognition and classification of images of agricultural/horticultural produce. A classifier based on BPNN is developed which uses the color, texture and morphological features to recognize and classify the different agricultural/horticultural produce. Even though these features have given different accuracies in isolation for varieties of food grains, mangoes and jasmine flowers, the combination of features proved to be very effective. The average recognition and classification accuracies using colour features are 87.5%, 78.4% and 75.7% for food grains, mango and jasmine flowers, respectively, and the average accuracies have increased to 90.8%, 80.2% and 85.8% for food grains, mangoes and jasmine flowers ,respectively, using texture features. The average accuracies have increased to 94.1%, 84.0% and 90.1% for food grains, mangoes and jasmine flowers, respectively. The results are encouraging and promise a good machine vision system in the area of recognition and classification of agricultural/horticultural produce.

Multifunctional stereoscopic machine vision system with multiple 3D outputs

2020 ◽  
Keyword(s):  
Machine Vision ◽  
International Symposium ◽  
Fast Track ◽  
Vision System ◽  
Machine Vision System ◽  
Stereoscopic Displays ◽  
Electronic Imaging

Fast track article for IS&T International Symposium on Electronic Imaging 2020: Stereoscopic Displays and Applications proceedings.

scholarly journals A real-time machine-vision system for monitoring the textile raising process

2005 ◽  
Vol 56 (8-9) ◽  
pp. 831-842 ◽  
Author(s):  
Monica Carfagni ◽  
Rocco Furferi ◽  
Lapo Governi
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Vision System ◽  
Machine Vision System ◽  
Time Machine

Application of Machine Vision System in the Precision Inspection

2012 ◽  
Vol 546-547 ◽  
pp. 1382-1386
Author(s):  
Yin Xia Liu ◽  
Ping Zhou
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Machine Vision System ◽  
Technical Problems ◽  
Application Program ◽  
Precision Inspection ◽  
Machine Parts

In order to promote the application and development of machine vision, The paper introduces the components of a machine vision system、common lighting technique and machine vision process. And the key technical problems are also briefly discussed in the application. A reference idea for application program of testing the quality of the machine parts is offered.

A machine vision system for on-line removal of contaminants in wool

Mechatronics ◽  
2006 ◽  
Vol 16 (5) ◽  
pp. 243-247 ◽  
Author(s):  
Zhenwei Su ◽  
Gui Yun Tian ◽  
Chunhua Gao
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Machine Vision System ◽  
On Line ◽  
Removal Of Contaminants

Perfect vision [machine vision system]

2006 ◽  
Vol 85 (5) ◽  
pp. 42-45 ◽  
Author(s):  
K. Lu
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Machine Vision System

A novel method based on machine vision system and deep learning to detect fraud in turmeric powder

2021 ◽  
pp. 104728
Author(s):  
Ahmad Jahanbakhshi ◽  
Yousef Abbaspour-Gilandeh ◽  
Kobra Heidarbeigi ◽  
Mohammad Momeny
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Vision System ◽  
Machine Vision System ◽  
Novel Method

Research on Machine Vision in Forestry Pluck System

2010 ◽  
Vol 139-141 ◽  
pp. 2199-2202
Author(s):  
Xin Li ◽  
Chun Liang Zhang ◽  
Li Jun Li ◽  
Zhi Hu
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Area Ratio ◽  
Self Organization ◽  
Machine Vision System ◽  
Camellia Oleifera ◽  
Threshold Method ◽  
Input Feature ◽  
Ga Algorithm ◽  
Forestry Industry

Forestry industry is an important part of nation's economy. In this paper, a machine vision system is presented as a key module of Camellia oleifera pluck robot. In order to cut fruit image up from complicate background, SOFM neural network and gray thresh is used in image segmentation. In SOFM method, take R-B,G-R,G-B and hue H tunnel as input feature vectors, use self-organization network to clustering can get the best effect. in gray threshold method can take various of method to get the best threshold, such as PSO and GA algorithm, and MATLAB includes the toolboxes. At last use noise ratio, area ratio, divided time, Fourier boundary descriptors and other indicators to assess the accuracy of segmentation. The methods have the significance to the current and subsequent research of forestry pluck device.

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