scholarly journals Flotation froth feature analysis using computer vision technology

2020 ◽  
Vol 192 ◽  
pp. 02022
Author(s):  
Artem Romachev ◽  
Valentin Kuznetsov ◽  
Egor Ivanov ◽  
Benndorf Jörg
Keyword(s):  
Image Processing ◽  
Bubble Diameter ◽  
Feature Analysis ◽  
Efficiency Evaluation ◽  
Air Flow Rate ◽  
Sulfide Flotation ◽  
Machine Vision Application ◽  
Computer Vision Technology ◽  
Froth Image ◽  
Vision Application

The possibility of machine vision application in the field of flotation efficiency evaluation was studied. Algorithm for froth image analysis was developed with aim of obtaining bubble’s size distribution. Algorithm consists of two parts: image processing and object detection. Algorithm’s work was verified on the sulfide flotation froth. As result, mathematical correlations for air flow rate, mean bubble diameter and surface area bubble flux were established.

scholarly journals Survey of rice seed quality analysis for varietal purity estimation by using image processing techniques

2018 ◽  
Vol 7 (1.7) ◽  
pp. 34
Author(s):  
S. Durai ◽  
C. Mahesh ◽  
T. Sujithra ◽  
A. Suresh
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
South India ◽  
Seed Quality ◽  
Quality Analysis ◽  
Rice Seed ◽  
Two Factors ◽  
Computer Vision Technology ◽  
Processing Techniques ◽  
Varietal Purity

 In south India rice is the major food source and in agriculture, rice production covers more than 70 percentages of entire forming. But in recent the production only from south India not enough to satisfy the need of all, such a huge demand is there. The better production comes from the selection of good seeds. Up to now formers depend on two factors for selecting better seeds, One is the brand which is approved by some quality standards and second one is analyzed manually by experienced people. Both are risky one, we are not pretty much sure the accuracy of analyze. The second one is seeing and feeling. The inspection is not consistent also very time consuming. In the other way we can use computer vision technology to analyze the quality of the seeds. In recent years many of the big industries they are using computer vision technology with Digital Image Processing for many of the applications. In this Paper we are going to discuss the different seed quality analyzing methods and accuracy of result also. Moreover there are different factors and features are there for it, here we are going to study about varietal purity estimation by different methods.

Vehicle Security Distance Control Based on Binocular Parallax Vision

2014 ◽  
Vol 644-650 ◽  
pp. 207-210
Author(s):  
Shuang Liu ◽  
Xiang Jie Kong ◽  
Ming Cai Shan
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
High Precision ◽  
Stereo Matching ◽  
Vision System ◽  
Intelligent Transportation ◽  
Distance Control ◽  
Calibration Methods ◽  
Computer Vision Technology ◽  
Vehicle Security

Binocular parallax vision system is a kind of computer vision technology. Two cameras on different locations can get two different pictures of same object. The space position of the object can be calculated by the parallax information of two different pictures. The binocular parallax vision technology includes cameras calibration, image processing, and stereo matching analysis. The paper will introduce the inside and outside parameters calibration methods, and combing the traffic applications, designed the calibrating scheme. The parameters that obtained according to the scheme can meet the demands of measuring the vehicle distance. The high precision can meet the needs of intelligent transportation vehicles in a security vehicles spacing survey, which is an effective way for measuring the front car distance.

scholarly journals A Hybrid Methodology for Automation the Diagnosis of Leukemia Based on Quantitative and Morphological Feature Analysis

2018 ◽  
Vol 12 (3) ◽  
pp. 56 ◽  
Author(s):  
Hussam N. Fakhouri ◽  
Saleh H. Al-Sharaeh
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
White Blood Cells ◽  
Medical Image Processing ◽  
Initial Diagnosis ◽  
Feature Analysis ◽  
Microscopic Images ◽  
Different Types ◽  
The Status ◽  
Blast Cells

Recent year’s witnessed a huge revolution for developing an automated diagnosis for different disease such as cancer using medical image processing. Many researches have been dedicated to achieve this goal. Analyzing medical microscopic histology images provide us with large information about the status of patient and the progress of diseases, help to determine if the tissue have any pathological changes. Automation of the diagnosis of these images will lead to better, faster and enhanced diagnosis for different hematological and histological tissue images such as cancer. This paper propose an automated methodology for analyzing cancer histology and hematology microscopic images to detect leukemia using image processing by combining two diagnosis procedures initial and advance; the initial diagnosis depend on the percentage of the white blood cells in microscopic images affected by leukemia as indicator for the existence of leukemia in the blood smear sample. Whereas, the advance diagnosis classifying the leukemia according into different types using feature bag classifier. The experimental results showed that the proposed methodology initial diagnosis is able to detect leukemia images and differentiate it from samples that do not have leukemia. While, advance diagnosis it is able to detect and classify most leukemia types and differentiate between acute and chronic, but in some cases in the chronic leukemia where the percent of blast cells and shape are similar; it gave a diagnosis of the type of leukemia to the most similar type.

scholarly journals Machine Learning for Diabetic Retinopathy Detection using Image Processing

2021 ◽  
Vol 9 (5) ◽  
pp. 209-215
Author(s):  
Ujwala W. Wasekar ◽  
R. K. Bathla
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Early Stage ◽  
Haar Wavelet ◽  
Feature Analysis ◽  
Median Filtering ◽  
Wavelet Filters ◽  
Knn Classifier ◽  
Form Features ◽  
Processing Techniques

he disorder of Diabetic Retinopathy (DR), a complication of Diabetes that may lead to blindness if not treated at an early stage, is diagnosed by evaluating the retina images of eye. However, the manual grading of images for identifying the seriousness of DR disease requires many resources and it also takes a lot of time. Automated systems give accurate results along with saving time. Ophthalmologists may find it useful in reducing their workload. Proposed work presents the method to correctly identify the lesions and classify DR images efficiently. Blood leaking out of veins form features such as exudates, microaneurysms and haemorrhages, on retina. Image processing techniques assist in DR detection. Median filtering is used on gray scale converted image to reduce noise. The features of the pre-processed images are extracted by textural feature analysis. Optic disc (OD) segmentation methodology is implemented for the removal of OD. Blood vessels are extracted using haar wavelet filters. KNN classifier is applied for classifying retinal image into diseased or healthy .The proposed algorithm is executed in MATLAB software and analyze results with regard to certain parameters such as accuracy, sensitivity, and specificity. The outcomes prove the superiority of the new method with sensitivity of 92.6%, specificity of 87.56% and accuracy of 95% on Diaretdb1 database.

Separation and Hydrodynamic Performance of Air-Kerosene-Water System by Bubble Column

2010 ◽  
Vol 8 (1) ◽  
Author(s):  
Abbas H. Sulaymon ◽  
Ahmed Abed Mohammed
Keyword(s):  
Removal Efficiency ◽  
Flow Rate ◽  
Bubble Column ◽  
Removal Rate ◽  
Air Flow ◽  
Bubble Diameter ◽  
Water Concentration ◽  
Hydrodynamic Performance ◽  
Air Flow Rate ◽  
Flotation Process

The separation of emulsified kerosene in water (concentration 250-750ppm) was investigated in a bubble column15.6 cm diameter and 120 cm height. The effective behaviors of bubble characteristics (bubble diameter, bubble rise velocity and air hold-up) on the removal efficiency were measured by electroresistivity probe. The effects of kerosene concentration, air flow rate,bubble diameter, liquid height, liquid viscosity, NaCl concentration, and alum on the removal rate were found. The experimental results showed that the removal efficiency increased with increasing air flow rate (1.09-2.6cm/s) and decreased with increasing CMC concentration. The results also showed that adding anionic surfactants (SLES and SDBS) leads to increase removal rate. The the flotation process was found to be first order kinetics. New correlations of air holdup and bubble diameter using dimensionless groups were derived.

A Modelling of Stereo Matching Algorithm for Machine Vision Application

2019 ◽  
pp. 499-508
Author(s):  
Rostam Affendi Hamzah ◽  
A. F. Kadmin ◽  
S. F. Abd Gani ◽  
N. Mohamood ◽  
A N. A. Jahari ◽  
...  
Keyword(s):  
Machine Vision ◽  
Stereo Matching ◽  
Matching Algorithm ◽  
Machine Vision Application ◽  
Vision Application

Machine Vision Application on Science and Industry

2019 ◽  
pp. 233-254
Author(s):  
Bassem S. M. Zohdy ◽  
Mahmood A. Mahmood ◽  
Nagy Ramadan Darwish ◽  
Hesham A. Hefny
Keyword(s):  
Machine Vision ◽  
Medical Research ◽  
Real Time ◽  
Medical Images ◽  
Deep Understanding ◽  
Great Opportunity ◽  
Material Systems ◽  
Machine Vision Application ◽  
Real Time Detection ◽  
Vision Application

Machine vision studies opens a great opportunity for different domains as manufacturing, agriculture, aquaculture, medical research, also research studies and applications for better understanding of processes and operations. As scientists' efforts had been directed towards deep understanding of the particular material systems or particular classes of types of specific fruits, or diagnosis of patients through medical images classification and analysis, also real time detection and inspection of malfunction piece, or process, as various domains witnessed advancement through using machine vision techniques and methods.

Influence of Sample Defocus and Large Thickness on Measurement Error in Machine Vision Application

2015 ◽  
Vol 35 (8) ◽  
pp. 0815002
Author(s):  
来先家 Lai Xianjia ◽  
徐文东 Xu Wendong ◽  
赵成强 Zhao Chengqiang ◽  
肖阳 Xiao Yang
Keyword(s):  
Measurement Error ◽  
Machine Vision ◽  
Large Thickness ◽  
Machine Vision Application ◽  
Vision Application
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