Detection and Classification of Watermelon Seeds Exterior Quality Based on LS-SVM Using Machine Vision

2013 ◽  
Vol 278-280 ◽  
pp. 727-730
Author(s):  
Xiai Chen ◽  
Shuang Ke ◽  
Ling Wang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Machine Vision ◽  
Vision System ◽  
Least Square ◽  
Support Vector ◽  
Machine Vision System ◽  
High Quality ◽  
Surface Irregularities

A machine vision system was developed to investigate the detection of watermelon seeds exterior quality. The main characteristics of watermelon seeds appearance including area, perimeter, roughness and minimum enclosing rectangle were calculated by image analysis. Least square support vector machine optimized by genetic algorithm was applied for the classification of watermelon seeds exterior quality, and the broken seeds, normal seeds and high-quality seeds were distinguished finally. The surface irregularities defects of watermelon seeds were detected by machine vision grid laser. The experimental results show that the watermelon seeds exterior quality could be well detected and classified by machine vision based on least squares support vector machine.

scholarly journals The Development of Machine Vision System for Sorting Passion Fruit using MultiClass Support Vector Machine

2018 ◽  
Vol 11 (5) ◽  
pp. 178-184 ◽  
Author(s):  
Sitti Wetenriajeng Sidehabi ◽  
◽  
Ansar Suyuti ◽  
Intan Sari Areni ◽  
Ingrid Nurtanio ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Machine Vision ◽  
Vision System ◽  
Passion Fruit ◽  
Support Vector ◽  
Machine Vision System ◽  
Multiclass Support Vector Machine

scholarly journals Classification of Shape of Bell Pepper by Machine Vision System

2006 ◽  
Vol 15 (2) ◽  
pp. 113-122 ◽  
Author(s):  
Tsuyoshi Okayama ◽  
Jiao Qiao ◽  
Hiroe Tanaka ◽  
Naoshi Kondo ◽  
Sakae Shibusawa
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Bell Pepper ◽  
Machine Vision System

scholarly journals An intelligent machine vision system for detecting surface defects on packing boxes based on support vector machine

2019 ◽  
Vol 52 (7-8) ◽  
pp. 1102-1110 ◽  
Author(s):  
Yu Wu ◽  
Yanjie Lu
Keyword(s):  
Support Vector Machine ◽  
Machine Vision ◽  
Defect Detection ◽  
Surface Defects ◽  
Vision System ◽  
Rapid Development ◽  
Training Model ◽  
Support Vector ◽  
Ideal Condition ◽  
Product Packaging

Defects in product packaging are one of the key factors that affect product sales. Traditional defect detection depends primarily on artificial vision detection. With the rapid development of machine vision, image processing, pattern recognition, and other technologies, industrial automation detection has become an inevitable trend because machine vision technology can greatly improve accuracy and efficiency; therefore, it is of great practical value to study automatic detection technology of the surface defects encountered in packaging boxes. In this study, machine vision and machine learning were combined to examine a surface defect detection method based on support vector machine where defective products are eliminated by a sorting robot system. After testing, the support vector machine training model using radial basis function kernel detects three kinds of defects at the same time under the ideal condition of parameter selection, and the effective detection rate is 98.0296%.

scholarly journals Design and development of a machine vision system using artificial neural network-based algorithm for automated coal characterization

2020 ◽  
Author(s):  
Amit Kumar Gorai ◽  
Simit Raval ◽  
Ashok Kumar Patel ◽  
Snehamoy Chatterjee ◽  
Tarini Gautam
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Machine Vision ◽  
Vision System ◽  
Color Space ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Machine Vision System ◽  
Coal Characterization ◽  
Artificial Neural

Abstract Coal is heterogeneous in nature, and thus the characterization of coal is essential before its use for a specific purpose. Thus, the current study aims to develop a machine vision system for automated coal characterizations. The model was calibrated using 80 image samples that are captured for different coal samples in different angles. All the images were captured in RGB color space and converted into five other color spaces (HSI, CMYK, Lab, xyz, Gray) for feature extraction. The intensity component image of HSI color space was further transformed into four frequency components (discrete cosine transform, discrete wavelet transform, discrete Fourier transform, and Gabor filter) for the texture features extraction. A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development. The datasets of the optimized features were used as an input for the model, and their respective coal characteristics (analyzed in the laboratory) were used as outputs of the model. The R-squared values were found to be 0.89, 0.92, 0.92, and 0.84, respectively, for fixed carbon, ash content, volatile matter, and moisture content. The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression, support vector regression, and radial basis neural network models. The study demonstrates the potential of the machine vision system in automated coal characterization.

Machine Vision System for Automatic Inspection of Surface Defects in Aluminum Die Casting

2006 ◽  
Vol 10 (3) ◽  
pp. 281-286 ◽  
Author(s):  
Yakov Frayman ◽  
◽  
Hong Zheng ◽  
Saeid Nahavandi ◽  
Keyword(s):  
Genetic Algorithm ◽  
Machine Vision ◽  
Defect Detection ◽  
Surface Defects ◽  
Vision System ◽  
Die Casting ◽  
Noise Removal ◽  
Morphological Operations ◽  
Automatic Inspection ◽  
Machine Vision System

A camera based machine vision system for the automatic inspection of surface defects in aluminum die casting is presented. The system uses a hybrid image processing algorithm based on mathematic morphology to detect defects with different sizes and shapes. The defect inspection algorithm consists of two parts. One is a parameter learning algorithm, in which a genetic algorithm is used to extract optimal structuring element parameters, and segmentation and noise removal thresholds. The second part is a defect detection algorithm, in which the parameters obtained by a genetic algorithm are used for morphological operations. The machine vision system has been applied in an industrial setting to detect two types of casting defects: parts mix-up and any defects on the surface of castings. The system performs with a 99% or higher accuracy for both part mix-up and defect detection and is currently used in industry as part of normal production.

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