Particle Shape Analysis Using Digital Image Processing

2019 ◽  
pp. 377-391
Author(s):  
Katia Tannous ◽  
Fillipe de Souza Silva
Keyword(s):  
Image Processing ◽  
Aspect Ratio ◽  
Digital Image Processing ◽  
Shape Analysis ◽  
Processing Technique ◽  
Solid Particles ◽  
Three Dimensions ◽  
Image Processing Technique ◽  
Prolate Spheroids

This chapter will discuss new software, Particles and Geometric Shapes Analyzer (APOGEO), aiming the determination of aspect ratio and sphericity of solid particles by image processing technique without any manual work. This software can quantify the major and minor axes correlating two or three dimensions of particles (e.g., biomass, mineral, pharmaceutical, and food products) to obtain their shape. The particles can be associated with different geometries, such as rectangular parallelepiped, cylinder, oblate and prolate spheroids, and irregular. The results are presented in histograms and tables, but also can be saved in a spreadsheet.

Particle Shape Analysis Using Digital Image Processing

2018 ◽  
pp. 1331-1343
Author(s):  
Katia Tannous ◽  
Fillipe de Souza Silva
Keyword(s):  
Image Processing ◽  
Aspect Ratio ◽  
Digital Image Processing ◽  
Shape Analysis ◽  
Processing Technique ◽  
Solid Particles ◽  
Three Dimensions ◽  
Image Processing Technique ◽  
Prolate Spheroids

This chapter will discuss new software, Particles and Geometric Shapes Analyzer (APOGEO), aiming the determination of aspect ratio and sphericity of solid particles by image processing technique without any manual work. This software can quantify the major and minor axes correlating two or three dimensions of particles (e.g.: biomass, mineral, pharmaceutical and food products) to obtain their shape. The particles can be associated with different geometries such as, rectangular parallelepiped, cylinder, oblate and prolate spheroids, and irregular. The results are presented in histograms and tables, but also can be saved in a spreadsheet.

Diagnosis of the state of N in wheat via digital image processing technique

2012 ◽  
Vol 19 (5) ◽  
pp. 1168-1174
Author(s):  
Li-Zhou ZHANG ◽  
Xiao-Yu HOU ◽  
Yu-Ming ZHANG ◽  
Hong-Jun LI ◽  
Yi-Song CHENG ◽  
...  
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Processing Technique ◽  
The State ◽  
Image Processing Technique ◽  
Digital Image Processing Technique

Diagnosis of N nutrient status of corn using digital image processing technique

2010 ◽  
Vol 18 (6) ◽  
pp. 1340-1344
Author(s):  
Li-Zhou ZHANG ◽  
Dian-Wu WANG ◽  
Yu-Ming ZHANG ◽  
Yi-Song CHENG ◽  
Hong-Jun LI ◽  
...  
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Nutrient Status ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Digital Image Processing Technique

Micro-Flow-Cytometer Integrated witch Optical Tweezer and DIP Technique for Cell/Particle Sorting and Manipulation

2007 ◽  
Vol 121-123 ◽  
pp. 1351-1354
Author(s):  
Yu Sheng Chien ◽  
Che Hsin Lin ◽  
Fu Jen Kao ◽  
Cheng Wen Ko
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
High Performance ◽  
Optical Tweezer ◽  
Processing Technique ◽  
Microfluidic System ◽  
Image Processing Technique ◽  
Target Cells ◽  
Digital Image Processing Technique

This paper proposes a novel microfluidic system for cell/microparticle recognition and manipulation utilizing digital image processing technique (DIP) and optical tweezer under microfluidic configuration. Digital image processing technique is used to count and recognize the cell/particle samples and then sends a control signal to generate a laser pulse to manipulate the target cell/particle optically. The optical tweezer system is capable of catching, moving and switching the target cells at the downstream of the microchannel. The trapping force of the optical tweezer is also demonstrated utilizing Stocks-drag method and electroosmotic flow. The proposed system provides a simple but high-performance solution for microparticle manipulation in a microfluidic device.

scholarly journals Predict Growth Stages of Wheat Crop using Digital Image Processing

2020 ◽  
Vol 8 (5) ◽  
pp. 3026-3035
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Processing Technique ◽  
Mean Value ◽  
Image Processing Technique ◽  
Wheat Crop ◽  
Growth Stages ◽  
Increase Productivity ◽  
Crop Growth Stages

Manual examination is not as accurate to examine crop growing stages because of the possibility of the human mistake and errors. While machine examination or automatic examination can easily examine crop growing stages and increase productivity because it provides fast and accurate examine result. This study provide a solution to finding the wheat crop growth stages, Once the growing stages are established, farmers can take suitable and measured steps to improve the production of wheat or other agricultural crops. For finding the growth stages of wheat digital image processing technique is used. RGB model, HSI model, mean value of green colour, hue and saturation images use for examining wheat crop.

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