Development of a new machine vision algorithm to estimate potato's shape and size based on support vector machine

2022 ◽  
Author(s):  
Dili Shen ◽  
Shengfei Zhang ◽  
Wuyi Ming ◽  
Wenbin He ◽  
Guojun Zhang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Machine Vision ◽  
Support Vector ◽  
Vision Algorithm ◽  
Shape And Size ◽  
New Machine

scholarly journals Oestrus Analysis of Sows Based on Bionic Boars and Machine Vision Technology

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1485
Author(s):  
Kaidong Lei ◽  
Chao Zong ◽  
Xiaodong Du ◽  
Guanghui Teng ◽  
Feiqi Feng
Keyword(s):  
Support Vector Machine ◽  
Machine Vision ◽  
Recognition Accuracy ◽  
Average Duration ◽  
Support Vector ◽  
Contact Duration ◽  
Innovative Design ◽  
Sparse Autoencoder ◽  
Accuracy Rates ◽  
Interactive Behaviour

This study proposes a method and device for the intelligent mobile monitoring of oestrus on a sow farm, applied in the field of sow production. A bionic boar model that imitates the sounds, smells, and touch of real boars was built to detect the oestrus of sows after weaning. Machine vision technology was used to identify the interactive behaviour between empty sows and bionic boars and to establish deep belief network (DBN), sparse autoencoder (SAE), and support vector machine (SVM) models, and the resulting recognition accuracy rates were 96.12%, 98.25%, and 90.00%, respectively. The interaction times and frequencies between the sow and the bionic boar and the static behaviours of both ears during heat were further analysed. The results show that there is a strong correlation between the duration of contact between the oestrus sow and the bionic boar and the static behaviours of both ears. The average contact duration between the sows in oestrus and the bionic boars was 29.7 s/3 min, and the average duration in which the ears of the oestrus sows remained static was 41.3 s/3 min. The interactions between the sow and the bionic boar were used as the basis for judging the sow’s oestrus states. In contrast with the methods of other studies, the proposed innovative design for recyclable bionic boars can be used to check emotions, and machine vision technology can be used to quickly identify oestrus behaviours. This approach can more accurately obtain the oestrus duration of a sow and provide a scientific reference for a sow’s conception time.

scholarly journals Grasp to See—Object Classification Using Flexion Glove with Support Vector Machine

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1461
Author(s):  
Shun-Hsin Yu ◽  
Jen-Shuo Chang ◽  
Chia-Hung Dylan Tsai
Keyword(s):  
Support Vector Machine ◽  
Daily Life ◽  
Object Classification ◽  
Target Object ◽  
The Other ◽  
Support Vector ◽  
Wide Range ◽  
Dark Space ◽  
Flex Sensors ◽  
Shape And Size

This paper proposes an object classification method using a flexion glove and machine learning. The classification is performed based on the information obtained from a single grasp on a target object. The flexion glove is developed with five flex sensors mounted on five finger sleeves, and is used for measuring the flexion of individual fingers while grasping an object. Flexion signals are divided into three phases, and they are the phases of picking, holding and releasing, respectively. Grasping features are extracted from the phase of holding for training the support vector machine. Two sets of objects are prepared for the classification test. One is printed-object set and the other is daily-life object set. The printed-object set is for investigating the patterns of grasping with specified shape and size, while the daily-life object set includes nine objects randomly chosen from daily life for demonstrating that the proposed method can be used to identify a wide range of objects. According to the results, the accuracy of the classifications are achieved 95.56% and 88.89% for the sets of printed objects and daily-life objects, respectively. A flexion glove which can perform object classification is successfully developed in this work and is aimed at potential grasp-to-see applications, such as visual impairment aid and recognition in dark space.

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%.

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.

143 Predicting pork color scores using machine vision and support vector machine technologies

2016 ◽  
Vol 94 (suppl_2) ◽  
pp. 67-67
Author(s):  
X. Sun ◽  
J. M. Young ◽  
J. H. Liu ◽  
L. A. Bachmeier ◽  
R. Somers ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Machine Vision ◽  
Support Vector

scholarly journals Speaker ethnic identification for continuous speech in malay language using pitch and MFCC

2020 ◽  
Vol 19 (1) ◽  
pp. 207
Author(s):  
Rafizah Mohd Hanifa ◽  
Khalid Isa ◽  
Shamsul Mohamad
Keyword(s):  
Support Vector Machine ◽  
Speaker Identification ◽  
Voice Recognition ◽  
Nearest Neighbors ◽  
Pitch Accent ◽  
Ethnic Identification ◽  
Support Vector ◽  
Continuous Speech ◽  
And Training ◽  
Shape And Size

<span>Voice recognition has evolved exponentially over the years. The purpose of voice recognition or sometimes called speaker identification, is to identify the person who is speaking. This can be done by extracting features of speech that differ between individuals due to physiology (shape and size of the mouth and throat) and also behavioral patterns (pitch, accent and style of speaking). This paper explains an approach of voice recognition to identify the ethnicity of Malaysian people. Pitch and 13 Mel-Frequency Cepstrum Coefficients (MFCCs) are extracted from 52 recorded continuous speech in Malay for use as features to train the classifiers using Tree, Naïve Bayes, Nearest Neighbors and Support Vector Machine (SVM) and another 10 recorded speeches are used for testing. The results reveal that the use of a combination of pitch and 13 coefficients for features extraction and training the data using SVM provide better accuracy (57.7%) than the use of only 13 coefficients (53.8%).</span>

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