Evaluating the quality of pellet component by using image processing technique with neural networks

2002 ◽  
Author(s):  
Ning-Feng Zeng ◽  
T. Konaka ◽  
K. Taniguchi ◽  
S. Watanabe ◽  
H. Yamamoto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Processing Technique ◽  
Image Processing Technique

Compact Disc Visual Inspection Using Neural Network

2012 ◽  
Vol 433-440 ◽  
pp. 727-732
Author(s):  
Anton Satria Prabuwono ◽  
Siti Rahayu Zulkipli ◽  
Doli Anggia Harahap ◽  
Wendi Usino ◽  
A. Hasniaty
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Visual Inspection ◽  
Processing Technique ◽  
Compact Disc ◽  
Image Processing Technique ◽  
Accuracy Rate ◽  
Network Training ◽  
Fields Of Study

Image processing is widely used in various fields of study including manufacturing as product inspection. In compact disc manufacturing, image processing has been implemented to recognize defect products. In this research, we implemented image processing technique as pre-processing processes. The aim is to acquire simple image to be processed and analyzed. In order to express the object from the image, the features were extracted using Invariant Moment (IM). Afterward, neural network was used to train the input from IM’s results. Thus, decision can be made whether the compact disc is accepted or rejected based on the training. Two experiments have been done in this research to evaluate 40 datasets of good and defective images of compact discs. The result shows that accuracy rate increased and can identify the quality of compact discs based on neural network training.

scholarly journals AUTOMATIC NUMBER PLATE RECOGNITION FOR DIFFERENT FONTS AND NON-ROMAN SCRIPT

2021 ◽  
Vol 5 (11) ◽  
Author(s):  
Kirad Varad Vinay ◽  
Indla Omkar Balaobaiah ◽  
Mujawar Sohail Mahiboob ◽  
Shinde Dinesh Nagnath ◽  
Prof. Darshana Patil
Keyword(s):  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Red Light ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Learning Technology ◽  
Size Number ◽  
Roman Script

According to survey taken the total number of vehicles in [1] India were 260 million. Therefore, there is a need to develop Automatic Number Plate Recognition (ANPR) systems [1] in India because of the large number of vehicles travelling on the roads. [1] It would also help in proper tracking of the vehicles, traffic examining, finding stolen vehicles, supervising parking toll and imposing strict actions against red light breaching. Automatic number plate recognition is image processing technique for finding number plate from image and extracting characters from detected number plate. ANPR in India has always been challenging due to different lighting conditions, changes in fonts, shapes, angles, letters size, number of lines and padding between lines, different languages used. In our project we proposed a model that can detects number plate with considering all irregularities. this system uses Computer vision and machine learning technology in order to detect number plate from image. In our proposed system number plate can be of different fonts and non-roman script. For identification of characters from number plate we use OCR (Optical character recognition) technique. OCR involves two parts: Character segmentation and Character Recognition. This OCR system can be used to extract characters of different fonts and non-roman script. The Quality of OCR depends on the quality of image, image contrast, text font style and size. To improve quality of OCR we can use image processing technique to enhance quality of image.

scholarly journals Improved Image Processing Techniques for User Immersion Problem Alleviation in Virtual Reality Environments

2020 ◽  
Vol 2 (2) ◽  
pp. 77-84
Author(s):  
Dr. Dhaya R.
Keyword(s):  
Image Processing ◽  
Virtual Reality ◽  
Processing Speed ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Image Processing Techniques ◽  
Processing Techniques

The latest advertisements on the advancements of the virtual reality has paved way for diverse studies, in manifold fields that can benefit by utilizing the technologies of the virtual reality, not excluding the design, gaming and the simulated understanding. Yet whenever a virtual reality device conveys information in form of images with the assistance of the display that is positioned closer to the user’s eyes it faces problems like minimizing the speed of the process and degradation in the quality of images ending up in huge variations across the virtual realism and the realism causing user immersion problems. So to mitigate the immersion problems of the user because of the low quality of image and the minimization of processing speed in the virtual reality environments the paper puts forth an improved image processing technique to improvise the sharpness of the images in order to enhance quality of the images and heighten the processing speed.

scholarly journals Can the Image Processing Technique Be Potentially Used to Evaluate Quality of Frying Oil?

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Patchimaporn Udomkun ◽  
Bhundit Innawong ◽  
Wantakan Sopa
Keyword(s):  
Image Processing ◽  
Contact Angle ◽  
Vision System ◽  
Processing Technique ◽  
Frying Oil ◽  
Image Processing Technique ◽  
Frying Time ◽  
Polar Materials ◽  
Highly Correlated

The objective of this study was to investigate the feasibility of a computer vision system (CVS) for assessing the contact angle of frying oil. The oil was used to fry carbohydrate- and protein-based foods for 40 h, and the oil was collected for measuring free fatty acids (FFA), peroxide value (PV), total polar materials (TPMs), and FOS reading (dielectric constant). The results showed that FFA linearly increased with frying time (R2 > 0.95) while the polynomial correlation between TPMs and FOS reading as a result of time was observed (R2 > 0.97). The contact angle obtained from CVS was highly correlated with all chemical qualities (R2 > 0.94), except PV. In addition, the contact angle models could be used to adequately predict FFA, TPMs, and FOS reading of frying oil (R2 > 0.91). This result suggested that the image processing technique through CVS could be an appropriate alternative to chemical analysis, especially for small- and medium-scale industrial frying.

Nondestructive Evaluation System for White Ginseng Quality Using Image Processing Technique

2006 ◽  
Vol 321-323 ◽  
pp. 1225-1228
Author(s):  
Seong Min Kim ◽  
Chul Soo Kim ◽  
Chong Ho Lee ◽  
Myung Ho Kim ◽  
Seung Jae Park
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Networks ◽  
Evaluation System ◽  
Image Acquisition ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Gray Level ◽  
White Ginseng ◽  
Artificial Neural

A real-time white ginseng quality evaluation system based on a machine vision technique and artificial neural networks was developed to replace the current manual grading and its efficiency was tested. The system consisted of conveyor, image acquisition system synchronized with a sample-detecting sensor, and image processing and decision-making system. Software running under Windows system was developed. The algorithm included three consecutive stages of (a) image acquisition and preprocessing, (b) mathematical feature extraction, and (c) grade decision using artificial neural networks. Mathematical features such as area ratio, mean and standard deviation of gray level, skewness of gray level histogram, and the number of run segment, were extracted from five equally divided parts of a specimen. An artificial neural network model was used to classify samples into three grading categories. The grading error of the system was about 26%, which is comparable to the 30% in case of manual grading. The grading rate was one sample per a second.

scholarly journals Detection of Cataract Through Feature Extraction by the Novel Angular Binary Pattern (NABP) and Classification by Kernel Based Convolutional Neural Networks

2021 ◽  
Author(s):  
A Sirajudeen ◽  
Anuradha balasubramaniam ◽  
S Karthikeyan
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Convolutional Neural Networks ◽  
Texture Features ◽  
Processing Technique ◽  
Image Processing Technique ◽  
The Novel ◽  
Shape Features ◽  
Visual Interpretation ◽  
Color Features

Abstract Cataract is a condition of the opacity in the lenticular regions, which usually results in bad visual interpretation of the viewed object or any entity. Hence the timely detection of cataract is considered to be significant and can even contribute in the prevention from loss of fight that might occur if the cataract is left untreated. In this paper, detection of cataract disease is carried out based on the image processing technique. Color features, texture features and shape features are extracted separately. This study proposed a Novel Angular Binary Pattern (NABP) for the extraction of texture features. And after the extraction of features, the images are subjected to classification through the implementation of the proposed novel Kernel Based Convolutional Neural Networks. Results are obtained separately for all the three types of features. A comparison is carried out for the proposed work with existing works and based on the results obtained it can be seen that the proposed work comes up with the enhanced results than the traditional methods.

scholarly journals Harumanis mango leaf disease recognition system using image processing technique

2021 ◽  
Vol 23 (1) ◽  
pp. 378
Author(s):  
R. A. JM. Gining ◽  
S. S. M. Fauzi ◽  
N. M . Yusoff ◽  
T. R. Razak ◽  
M. H. Ismail ◽  
...  
Keyword(s):  
Image Processing ◽  
Recognition System ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Human Errors ◽  
Leaf Disease ◽  
Rgb Image

Current Harumanismango farming technique in Malaysia still mostlydepends on the farmers' own expertise to monitor the crops from the attack ofpests and insects. This approach is susceptible to human errors, and thosewho do not possess this skill may not be able to detect the disease at the righttime. As leaf diseases seriously affect the crop's growth and the quality of theyield, this study aims to develop a recognition system that detects thepresence of disease in the mango leaf using image processing technique.First, the image is acquired through a smartphone camera; once it has beenpre-processed, it is then segmented in which the RGB image is converted toan HSI image, then the features are extracted. Lastly, the classification ofdisease is done to determine thetype of leaf disease. The proposed systemeffectively detects and classify the disease with an accuracy of 68.89%. Thefindings of this project will contribute to farmers and society's benefit, andresearchers can use the approach to address similar issues in future works.

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