scholarly journals X-Ray Images Processing to Detect Packaging Defects in Canned Food Industry Using Fuzzy Logic

2020 ◽  
Vol 11 (29) ◽  
pp. 6-23
Author(s):  
Marzieh Tafazoli
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Feature Extraction ◽  
Food Industry ◽  
Extraction Methods ◽  
Canned Food ◽  
X Ray ◽  
Images Processing ◽  
Logic Functions ◽  
Detection Of Defects

Taking into account the importance of packaging in the food industry and the need to maintain the health of the materials in the packaging, and with the help of the experiences obtained from previous research with their strengths and weaknesses, we propose a method for the detection of defects in canned packaging, through the use of X-ray radiography, image processing and feature extraction methods, based on fuzzy logic functions for them.

scholarly journals Research on Rolling Bearing Fault Feature Extraction and Diagnosis Method Based on Image Processing

2021 ◽  
Vol 5 (2) ◽  
pp. 1-7
Author(s):  
Sun Y
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Working Conditions ◽  
Rolling Bearing ◽  
Extraction Methods ◽  
Working Environment ◽  
Ring Rolling ◽  
Rolling Bearings ◽  
Binary Matrix ◽  
The Time Domain

In economic construction, there are many large and important machinery and equipment. Some equipment will continue to work in a harsh working environment, so many and various failures will occur. Rolling bearings are one of the widely used parts in rotating machinery. They are generally composed of inner ring, outer ring, rolling element and holding. The frame is composed of four parts, the failure of the bearing is particularly important, and its safe operation has a vital impact on the entire equipment, Feature extraction is the key link in the subsequent identification of fault types, Although feature extraction in the time domain and frequency domain is effective, it is also necessary to find new feature extraction methods in new areas. On the basis of the snowflake image obtained by using the principle of SDP(Symmetrized Dot Pattern), a method for extracting fault features of rolling bearings based on image processing is proposed, and the snowflake standard map for different working conditions is constructed. The number of snowflake images under different working conditions is different. The binary matrix of the test image is compared with it, and then classified and identified. Finally, the algorithm is validated, and the ideal result is obtained to verify its rationality and effectiveness.

scholarly journals Target Positioning and Sorting Strategy of Fruit Sorting Robot Based on Image Processing

2021 ◽  
Vol 38 (3) ◽  
pp. 797-805
Author(s):  
Jianhong Yu ◽  
Weijie Miao ◽  
Guangben Zhang ◽  
Kai Li ◽  
Yinggang Shi ◽  
...  
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Spatial Model ◽  
Food Industry ◽  
Three Dimensional ◽  
Automated Production ◽  
Program Flow ◽  
Sorting System ◽  
3D Scene ◽  
Fruit Sorting

To a certain extent, automated fruit sorting systems reflect the degree of automated production in modern food industry, and boast a certain theoretical and application value. The previous studies mostly concentrate on the design of robot structure, and the control of robot motions. There is little report on the feature extraction of fruits in specific applications of fruit sorting. For this reason, this paper explores the target positioning and sorting strategy of fruit sorting robot based on image processing. Firstly, the authors constructed a visual sorting system for fruit sorting robot, and explained the way to recognize objects in three-dimensional (3D) scene and to reconstruct the spatial model based on sorting robot. Next, the maturity of the identified fruits was considered the prerequisite of dynamic sorting of fruit sorting robot. Finally, the program flow of the fruit sorting robot was given. The effectiveness of our strategy was verified through experiments.

scholarly journals Classification of Coronavirus Images using Shrunken Features

2020 ◽  
Author(s):  
Saban Ozturk ◽  
Umut Ozkaya ◽  
Mucahid Barstugan
Keyword(s):  
Feature Extraction ◽  
Feature Vector ◽  
Principal Component ◽  
Extraction Methods ◽  
X Ray ◽  
Effective Interventions ◽  
Long Duration ◽  
Clinical Diagnoses ◽  
Short Time

AbstractNecessary screenings must be performed to control the spread of the Corona Virus (COVID-19) in daily life and to make a preliminary diagnosis of suspicious cases. The long duration of pathological laboratory tests and the wrong test results led the researchers to focus on different fields. Fast and accurate diagnoses are essential for effective interventions with COVID-19. The information obtained by using X-ray and Computed Tomography (CT) images is vital in making clinical diagnoses. Therefore it was aimed to develop a machine learning method for the detection of viral epidemics by analyzing X-ray images. In this study, images belonging to 6 situations, including coronavirus images, are classified. Since the number of images in the dataset is deficient and unbalanced, it is more convenient to analyze these images with hand-crafted feature extraction methods. For this purpose, firstly, all the images in the dataset are extracted with the help of four feature extraction algorithms. These extracted features are combined in raw form. The unbalanced data problem is eliminated by producing feature vectors with the SMOTE algorithm. Finally, the feature vector is reduced in size by using a stacked auto-encoder and principal component analysis to remove interconnected features in the feature vector. According to the obtained results, it is seen that the proposed method has leveraging performance, especially in order to make the diagnosis of COVID-19 in a short time and effectively.

Research on the Feature Parameter Extraction of Wheat Seeds’ Bad Point Based on Image Processing

2014 ◽  
Vol 644-650 ◽  
pp. 4140-4143
Author(s):  
Zhe Wen ◽  
Qian Dong ◽  
Jie Zhu ◽  
Ya Bin Fan
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Parameter Extraction ◽  
Extraction Methods ◽  
Automatic Extraction ◽  
Wheat Seed ◽  
Manual Operation ◽  
Feature Parameter ◽  
Wheat Seeds

It is very important that study the feature parameter extraction of bad point of wheat seeds based on image processing for judging the quality of wheat. Using image processing extract and analyze the collected images information, and based on the collected information analyze the bad point information of wheat seed, then extract the feature parameters. Traditional bad point’s feature extraction methods are completed by the manual operation, and the efficient is lower. Currently, by means of image processing technology can extract the bad point’s feature of wheat seed automatically. To this end, the research status of seed feature extraction based on image processing are reviewed and prospected. Experiments show that the method can better complete the bad point’s feature automatic extraction and recognition of wheat seeds.

scholarly journals Traffic Sign Detection Using Region And Corner Feature Extraction Method

2021 ◽  
Vol 3 (1) ◽  
pp. 21-24
Author(s):  
Hendra Maulana ◽  
Dhian Satria Yudha Kartika ◽  
Agung Mustika Riski ◽  
Afina Lina Nurlaili
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Safety Information ◽  
Extraction Methods ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Feature Extraction Method ◽  
Analysis Process ◽  
Sign Detection ◽  
Traffic Sign Detection

Traffic signs are an important feature in providing safety information for drivers about road conditions. Recognition of traffic signs can reduce the burden on drivers remembering signs and improve safety. One solution that can reduce these violations is by building a system that can recognize traffic signs as reminders to motorists. The process applied to traffic sign detection is image processing. Image processing is an image processing and analysis process that involves a lot of visual perception. Traffic signs can be detected and recognized visually by using a camera as a medium for retrieving information from a traffic sign. The layout of different traffic signs can affect the identification process. Several studies related to the detection and recognition of traffic signs have been carried out before, one of the problems that arises is the difficulty in knowing the kinds of traffic signs. This study proposes a combination of region and corner point feature extraction methods. Based on the test results obtained an accuracy value of 76.2%, a precision of 67.3 and a recall value of 78.6.

Image Processing Based Automatic Inspection for Assembly Line of Automobiles

2006 ◽  
Vol 321-323 ◽  
pp. 1288-1292
Author(s):  
Yun Koo Chung ◽  
Ki Hong Kim
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Production Cost ◽  
Assembly Line ◽  
Extraction Methods ◽  
Experimental Results ◽  
Inspection System ◽  
The Sun ◽  
Automatic Inspection ◽  
Automobile Assembly

Many visual automatic inspection methods are used in the assembly line of vehicles for improving quality and reducing a production cost. In order to resolve uneasy inspection problems, new useful methods and devices have been developed. A real factory environment has very poor optical conditions. Illuminations are affected by the sun lights passing through the windows of the factories. The floor is continuously vibrating, which could affect the posture of installed cameras and illumination devices. This paper describes how to overcome these problems and suggests the automatic inspection system applicable to the automobile assembly line. It introduces two feature extraction methods and algorithms to detect the opened car in the automobile assembly line. The methods for extracting these features are discussed in this paper with the experimental results.

Concealed Weapon Detection using Image Processing and Machine Learning

2021 ◽  
Vol 9 (12) ◽  
pp. 1374-1384
Author(s):  
Saksham Gosain
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Feature Extraction ◽  
Image Fusion ◽  
Thermal Imaging ◽  
Facial Feature ◽  
Ray Method ◽  
X Ray ◽  
Facial Feature Extraction ◽  
Concealed Weapon Detection

Abstract: This research paper presents a study of concealed weapon detection using image processing and machine learning. In order to attempt to replace the traditional method of detecting hidden weapons i.e. x-ray method with an automated and possibly a less error prone procedure, potential alternate techniques such as neural networks and image fusion have been studied and explored to identify the best possible solution. We propose a method to fuse Thermal/IR image with the conventional RGB image or HSV image in order to reduce image noise and retain all the critical features of the image to achieve both weapon detection and facial feature extraction. Keywords: Image fusion; concealed weapon; feature extraction; neural network; thermal imaging

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