Segmentation Method of Food Packaging Inkjet Characters Based on Computer Vision

2021 ◽  
pp. 734-739
Author(s):  
Suyu Jin ◽  
Tao Jia ◽  
Zonghua Zhou ◽  
Delong Lu ◽  
Qingguo Lin
Keyword(s):  
Computer Vision ◽  
Food Packaging ◽  
Segmentation Method

A Segmentation Method of Traffic Marking Based on Average Gray Approximating the Optimal Threshold

2014 ◽  
Vol 556-562 ◽  
pp. 3510-3513 ◽  
Author(s):  
Zu Sheng Chen ◽  
You Fu Wu
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Segmentation ◽  
Crucial Role ◽  
Road Traffic ◽  
Image Database ◽  
Identification Problem ◽  
Optimal Threshold ◽  
Segmentation Method ◽  
Local Average

Image segmentation technique was used widely for computer vision and image processing. A robust technique of image segmentation plays a crucial role in identification problem. In this paper, a nonparametric and unsupervised method of automatic threshold for segmenting image was proposed, i.e. the optimal threshold is approximated by global average gray and local average gray, and this method was compared with other methods by using standard image. The experimental results show that our method proposed in this paper is robust. In addition, an image database of road traffic marking (www.ananth.in/RoadMarkingdetection.html) is provided to do this experiment for testing our method, the results show that our method is excellent.

Calculation of flexible printed circuit boards (FPC) global and local defect detection based on computer vision

Circuit World ◽  
2016 ◽  
Vol 42 (2) ◽  
pp. 49-54 ◽  
Author(s):  
Liya Wang ◽  
Yang Zhao ◽  
Yaoming Zhou ◽  
Jingbin Hao
Keyword(s):  
Computer Vision ◽  
Defect Detection ◽  
New Method ◽  
Segmentation Result ◽  
Segmentation Method ◽  
Watershed Segmentation ◽  
Content Type ◽  
Printed Circuit ◽  
Flexible Printed Circuit ◽  
Human Operators

Purpose The purpose of this paper is to present a detection method based on computer vision for automatic flexible printed circuit (FPC) defect detection. Design/methodology/approach This paper proposes a new method of watershed segmentation based on morphology. A dimensional increment matrix calculation method and an image segmentation method combined with a fuzzy clustering algorithm are provided. The visibility of the segmented image and the segmentation accuracy of a defective image are guaranteed. Findings Compared with the traditional one, the segmentation result obtained in this study is superior in aspects of noise control and defect segmentation. It completely proves that the segmentation method proposed in this study is better matches the requirements of FPC defect extraction and can more effectively provide the segmentation result. Compared with traditional human operators, this system ensures greater accuracy and more objective detection results. Research limitations/implications The extraction of FPC defect characteristics contains some obvious characteristics as well as many implied characteristics. These characteristics can be extracted through specific space conversion and arithmetical operation. Therefore, more images are required for analysis and foresight to establish a more widely used FPC defect detection sorting algorithm. Originality/value This paper proposes a new method of watershed segmentation based on morphology. It combines a traditional edge detection algorithm and mathematical morphology. The FPC surface defect detection system can meet the requirements of online detection through constant design and improvement. Therefore, human operators will be replaced by machine vision, which can preferably reduce the production costs and improve the efficiency of FPC production.

scholarly journals A Character Segmentation Method to Increase Character Recognition Accuracy for Turkish Number Plates

2021 ◽  
Author(s):  
Gülsüm Çiğdem Çavdaroğlu ◽  
Mehmet Gökmen
Keyword(s):  
Computer Vision ◽  
Character Recognition ◽  
Recognition Accuracy ◽  
Character Segmentation ◽  
Segmentation Method ◽  
Test Dataset ◽  
Computer Vision Technology ◽  
Recognition Systems ◽  
Recognition Errors ◽  
Accuracy Rates

Automatic Number Plate Recognition is a computer vision technology that provides a way to recognize the vehicles number plates without direct human intervention. Developing Automatic Number Plate Recognition methodologies is a widely studied topic among the computer vision community to increase the accuracy rates. Although there are many studies, the research in character segmentation and improving the recognition accuracy remains limited. In this study, a new methodology is proposed to reduce the character recognition errors of Automatic Number Plate Recognition systems. To achieve this, it will be determined whether the characters are letters or numbers, and the number plates will be expressed in the form of letters - digit. The method suggested for segmenting blobs correctly worked with an accuracy of 96.12% on the test dataset. The method suggested for generating letter-digit expression for the number plates correctly worked with an accuracy of 99.28% on the test dataset. The proposed methodology can work only on Turkish number plates. In the future studies, the proposed methodology can be expanded by using the number plate dataset of a different country.

scholarly journals Measuring the Geometrical Parameters of Slub Yarn Using a Computer Vision Based Image Sequencing Technique

2019 ◽  
Vol 27 (3(135)) ◽  
pp. 26-35
Author(s):  
Zhongjian Li ◽  
Jun Xiang ◽  
Lei Wang ◽  
Ning Zhang ◽  
Jing-an Wang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Segmentation ◽  
Curve Fitting ◽  
Cross Correlation ◽  
Image Stitching ◽  
Geometrical Parameters ◽  
Segmentation Method ◽  
Segment Sequence ◽  
Sequencing Technique ◽  
Slub Yarn

This article presents a computer vision method for measuring the geometrical parameters of slub yarn based on yarn sequence images captured from a moving slub yarn. An image segmentation method proposed by our earlier work was applied to segment sequence slub yarn images to obtain overlapping diameter data. Then an image stitching method was proposed to remove the overlapped data based on the normalised cross correlation (NCC) method. In order to detect the geometrical parameters of slub yarn, the frequency histogram , curve fitting, and spectrogram methods were adopted to analyse the sequence diameter data obtained. Four kinds of slub yarn with different geometrical parameters were tested using the method proposed and Uster method. The experimental results show that the detection results for slub amplitude, slub length, slub distance, and slub period obtained using the method proposed were consistent with the set values and Uster results.

scholarly journals Corn Classification System based on Computer Vision

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 591 ◽  
Author(s):  
Xiaoming Li ◽  
Baisheng Dai ◽  
Hongmin Sun ◽  
Weina Li
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Maximum Likelihood ◽  
Classification System ◽  
Automated Classification ◽  
Shape Features ◽  
Segmentation Method ◽  
Likelihood Estimator ◽  
Color Features

Automated classification of corn is important for corn sorting in intelligent agriculture. This paper presents a reliable corn classification method based on techniques of computer vision and machine learning. To discriminate different damaged types of corns, a line profile segmentation method is firstly used to segment and separate a group of touching corns. Then, twelve color features and five shape features are extracted for each individual corn object. Finally, a maximum likelihood estimator is trained to classify normal and damaged corns. To evaluate the performance of the proposed method, a private dataset consisting of images of normal corn and six kinds of damage corns, including heat-damaged, germ-damaged, cob-rot-damaged, blue eye mold-damaged, insect-damaged, and surface mold-damaged, were collected in this work. The proposed method achieved an accuracy of 96.67% for the classification between normal corns and the first four common damaged corns, and an accuracy of 74.76% was achieved for the classification between normal corns and six kinds of damaged corns. The experimental results demonstrated the effectiveness of the proposed corn classification system.

scholarly journals A Computer Vision Based System for Classification of Chemically and Naturally Ripened Mangoes

2020 ◽  
Vol 10 (1) ◽  
pp. 15-19
Keyword(s):  
Computer Vision ◽  
Experimental Study ◽  
Color Space ◽  
Texture Features ◽  
Calcium Carbide ◽  
Experimental Result ◽  
Segmentation Method ◽  
Proposed Model ◽  
F Measure

Recently there was news indicating that mangoes might cause cancer. The news was based on the fact that mangoes were being artificially ripened using a chemical- calcium carbide and Ethrel, a well- known carcinogenic. The consumers hence have to be careful in buying the mangoes. In this study, we have proposed a model for classification of artificially and naturally ripened mangoes using k-NN and SVM classifiers. In order to improve the efficacy of the model, color space features such like RGB, HSV, L*a*b are extracted. Along with the color space features, 14 Haralick texture features are also extracted here. An mango is automatically segmented in an image using modified K-means clustering segmentation method. For the experimental study, mangoes of 2 varieties such as Badami and Raspuri have been taken. In each variety, three different classes of ripened mangoes are taken such as naturally and in chemical, two artificial ripening treatments were applied like calcium carbide and Ethrel solution. The obtained experimental result in terms of F-measure is ranging from 64% to 84% for two different varieties of mangoes using two different chemicals. Further this proposed model can be implemented for different variety of mangoes.

Macho Nachos

2015 ◽  
Vol 46 (4) ◽  
pp. 182-196 ◽  
Author(s):  
Luke (Lei) Zhu ◽  
Victoria L. Brescoll ◽  
George E. Newman ◽  
Eric Luis Uhlmann
Keyword(s):  
Gender Stereotypes ◽  
Food Packaging ◽  
Food Preferences ◽  
Healthy Food ◽  
Psychological Reactance ◽  
Unhealthy Food ◽  
Healthy Foods ◽  
Gender Schema ◽  
Male And Female ◽  
Schema Congruity

Abstract. The present studies examine how culturally held stereotypes about gender (that women eat more healthfully than men) implicitly influence food preferences. In Study 1, priming masculinity led both male and female participants to prefer unhealthy foods, while priming femininity led both male and female participants to prefer healthy foods. Study 2 extended these effects to gendered food packaging. When the packaging and healthiness of the food were gender schema congruent (i.e., feminine packaging for a healthy food, masculine packaging for an unhealthy food) both male and female participants rated the product as more attractive, said that they would be more likely to purchase it, and even rated it as tasting better compared to when the product was stereotype incongruent. In Study 3, packaging that explicitly appealed to gender stereotypes (“The muffin for real men”) reversed the schema congruity effect, but only among participants who scored high in psychological reactance.

Human Vision and Computer Vision

1985 ◽  
Vol 30 (1) ◽  
pp. 47-47
Author(s):  
Herman Bouma
Keyword(s):  
Computer Vision ◽  
Human Vision
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