A computer vision system for objective fabric smoothness appearance assessment with an ensemble classifier

2019 ◽  
Vol 90 (3-4) ◽  
pp. 333-343 ◽  
Author(s):  
Jingan Wang ◽  
Kangjun Shi ◽  
Lei Wang ◽  
Ruru Pan ◽  
Weidong Gao
Keyword(s):  
Computer Vision ◽  
Information Gain ◽  
Vision System ◽  
Objective Assessment ◽  
Ensemble Classifier ◽  
Image Feature ◽  
Support Vector ◽  
Computer Vision System ◽  
Processing Technologies ◽  
Feature Sets

Fabric smoothness appearance assessment plays an important role in the textile and apparel industry. To evaluate fabric smoothness objectively, different methods have been proposed based on computer vision technology. To further improve the performance and promote the application of the assessment methods, this paper reports a hybrid computer vision system for objective assessment of fabric smoothness appearance with an ensemble classifier to integrate the advantages of the different image feature sets, which are extracted based on different image processing technologies. The image acquisition environment is established in this system with the selection of illumination parameters—intensity, position angle and altitudinal angle—by a designed strategy. The main steps of the strategy include determination of priority by information gain analysis and parameter selection by classifier performance analysis. The support vector machine classifiers trained by each feature sets are grouped into an ensemble by a self-adapting weighted voting method and the redundant feature sets are eliminated based on the weights of the feature sets. The final result shows evaluation accuracies with 82.86% under 0-degree error, 97.14% under 0.5-degree error and 100% under 1-degree error, which outperforms the other methods in the same environment and verifies the applicability of the proposed system.

Identification of Longjing Teas with Different Geographic Origins Based on E-Nose and Computer Vision System Combined with Data Fusion Strategies

2021 ◽  
Vol 64 (1) ◽  
pp. 327-340
Author(s):  
Min Xu ◽  
Jun Wang ◽  
Pengfei Jia ◽  
Yuting Dai
Keyword(s):  
Decision Making ◽  
Computer Vision ◽  
Feature Selection ◽  
Data Fusion ◽  
Electronic Nose ◽  
Vision System ◽  
Support Vector ◽  
List Type ◽  
Computer Vision System ◽  
Geographic Origins

HighlightsE-nose and computer vision combined with data fusion strategies were applied to trace tea origins.Pearson correlation analysis, IG, and F-scores were applied to modify the fusion strategies.The classification performances of different fusion strategies were compared.The strategies of IG_SVM_FL and IG_SVM_DS achieved the best results.Abstract. The traceability of tea origins is of great significance. In this study, an electronic nose (E-nose) and computer vision system (CVS) were jointly applied to acquire aroma and image signals of tea samples, aiming at identifying Longjing teas from different geographic origins including Jinyun (120° 7' E, 28° 65' N), Xihu (120° 13' E, 30° 27' N), Xinchang (120° 9' E, 29° 50' N), and Qian Daohu (119° 3' E, 29° 60' N). Data fusion was used to integrate the E-nose and CVS signals for comprehensively characterizing the tea samples. Four traditional fusion strategies including k-nearest neighbors (KNN) and support vector machine (SVM) based feature-level fusion (KNN_FL and SVM_FL) and Dempster-Shafer (D-S) evidence theory based decision-level strategies (KNN_DS and SVM_DS) were applied for classification modeling. Pearson analysis, information gain (IG), and F-scores were employed to modify the traditional fusion strategies to reduce inconsistent and redundant information in the fusion process. The results indicated that the original fusion strategies had no superiority over independent E-nose and CVS decision-making. With the feature selection methods, the modified fusion strategies generally exhibited better performance than the independent decision-making and original fusion strategies. Moreover, the IG-based fusion strategies, encompassing IG_SVM_FL and IG_SVM_DS, achieved the highest classification accuracy of 100%. Keywords: Computer vision, Electronic nose, Feature selection, Fusion strategies, Tea origins.

Prediction of chemical component contents of the fruit of Xanthium strumarium L. during processing based on a computer vision system combined with a support vector machine

2019 ◽  
Vol 11 (25) ◽  
pp. 3260-3268 ◽  
Author(s):  
Wenxiang Fan ◽  
Qiang Xu ◽  
Li Wang ◽  
Lin Li ◽  
Jiaolong Wang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Support Vector Machine ◽  
Vision System ◽  
Chemical Component ◽  
Xanthium Strumarium ◽  
Support Vector ◽  
Computer Vision System ◽  
Novel Method

A novel method for predicting the component contents of Xanthii Fructus during processing by using a computer vision system combined with a support vector machine.

scholarly journals Computer Vision Estimation of the Volume and Weight of Apples by Using 3D Reconstruction and Noncontact Measuring Methods

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Baohua Zhang ◽  
Ning Guo ◽  
Jichao Huang ◽  
Baoxing Gu ◽  
Jun Zhou
Keyword(s):  
Computer Vision ◽  
3D Reconstruction ◽  
Least Squares ◽  
Optimal Parameter ◽  
Vision System ◽  
Image Feature ◽  
Projection Area ◽  
Support Vector ◽  
Type I ◽  
Measuring Methods

A computer vision system for the estimation of apple volume and weight by using 3D reconstruction and noncontact measuring methods was investigated. The 3D surface of the apples could be reconstructed by using a single multispectral camera and near-infrared linear-array structured light. Both the traditional image feature and height information were extracted from the height maps. Two different type height features (Type I and II) were extracted, and both of them were fused with a projection area to form combination features (Combination Feature I and II). Partial least squares analysis and least squares-support vector machine were implemented for calibration models with projection area and combination features as inputs. Grid-Search Technique and Leave-One-Out Cross-Validation were also investigated to find out the optimal parameter values of the RBF kernel. The optimal LS-SVM models with Combination Feature II outperformed PLS models. The coefficient and root mean square error of prediction for the best prediction by LS-SVM were 0.9032 and 10.1155 for volume, whereas 0.8602 and 9.9556 for weight, respectively. The overall results indicated that height information can improve the prediction performance, and the proposed system could be applied as an alternative to the traditional methods for noncontract measurement of the volume and weight of apple fruits.

Colour assessment of milk and milk products using computer vision system and colorimeter

2021 ◽  
pp. 105084
Author(s):  
Bojana Milovanovic ◽  
Ilija Djekic ◽  
Jelena Miocinovic ◽  
Bartosz G. Solowiej ◽  
Jose M. Lorenzo ◽  
...  
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Computer Vision System ◽  
Milk Products

scholarly journals An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 343
Author(s):  
Kim Bjerge ◽  
Jakob Bonde Nielsen ◽  
Martin Videbæk Sepstrup ◽  
Flemming Helsing-Nielsen ◽  
Toke Thomas Høye
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Vision System ◽  
Low Cost ◽  
Light Trap ◽  
Automatic Monitoring ◽  
Light Sources ◽  
Monitoring Methods ◽  
Computer Vision System ◽  
Substantial Investment

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

scholarly journals Task Planner for Robotic Disassembly of Electric Vehicle Battery Pack

Metals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 387
Author(s):  
Martin Choux ◽  
Eduard Marti Bigorra ◽  
Ilya Tyapin
Keyword(s):  
Computer Vision ◽  
Electric Vehicles ◽  
Measurement Errors ◽  
Vision System ◽  
Lithium Ion ◽  
Battery Pack ◽  
Computer Vision System ◽  
Electric Vehicle Battery ◽  
Robotic Disassembly ◽  
Total Average

The rapidly growing deployment of Electric Vehicles (EV) put strong demands on the development of Lithium-Ion Batteries (LIBs) but also into its dismantling process, a necessary step for circular economy. The aim of this study is therefore to develop an autonomous task planner for the dismantling of EV Lithium-Ion Battery pack to a module level through the design and implementation of a computer vision system. This research contributes to moving closer towards fully automated EV battery robotic dismantling, an inevitable step for a sustainable world transition to an electric economy. For the proposed task planner the main functions consist in identifying LIB components and their locations, in creating a feasible dismantling plan, and lastly in moving the robot to the detected dismantling positions. Results show that the proposed method has measurement errors lower than 5 mm. In addition, the system is able to perform all the steps in the order and with a total average time of 34 s. The computer vision, robotics and battery disassembly have been successfully unified, resulting in a designed and tested task planner well suited for product with large variations and uncertainties.

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