Approach to reduce the computational image processing requirements for a computer vision system using sensor preprocessing and the Hotelling transform

2005 ◽  
Author(s):  
Thomas R. Schei ◽  
Cameron H. G. Wright ◽  
Daniel J. Pack
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Vision System ◽  
Computer Vision System

scholarly journals Rancang Bangun Computer Vision System (CVS) Sebagai Instrumen Pengukuran Warna Buah-Buahan

2020 ◽  
Vol 40 (1) ◽  
pp. 21
Author(s):  
Ferlando Jubelito Simanungkalit ◽  
Rosnawyta Simanjuntak
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Vision System ◽  
Agricultural Products ◽  
Color Measurement ◽  
Percentage Error ◽  
Computer Vision System ◽  
Accuracy Rate ◽  
Processing Software ◽  
Image Processing Software

Color had a correlation with physical appearance, nutritional and chemical content as well as sensory properties which determine the quality of agricultural products and foods. Conventional color measurements were performed destructively using laboratory equipment. Therefore, color measurement methods of agricultural products were needed more quickly, accurately and non-destructively. This study aimed to develop a Computer Vision System (CVS) that can be used as a tool to measure the color of fruits. The designed CVS consists of a 60x60x60 cm black mini photo studio; a pair 15 watt LED lighting, sony α6000 digital camera, a set of laptop and an image processing software applications. Image processing software was programmed using VB.Net 2008 programming language. The developed CVS was calibrated using 24 color charts Macbeth Colorchecker (Gretag-Macbeth, USA). The calibration results of 24 color chart of Macbeth Colorchecker was resulted in a MAPE (Mean Absolute Percentage Error) value of component R / Red = 0%; G / Green = 0% and B / Blue = 0,5%; with 99% accuracy rate. In color measurement, the developed CVS had a 95% accuracy rate.

scholarly journals Sistem Computer Vision Pengenalan Pola Angka dan Operator Matematika Pada Permainan Kartu Angka Berbasis Jaringan Syaraf Tiruan Perceptron

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Rian Rahmanda Putra ◽  
Fery Antony
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Vision System ◽  
Computer Vision System ◽  
Image Processing Techniques ◽  
Perceptron Algorithm ◽  
Card Number ◽  
Training Stage ◽  
Processing Techniques ◽  
Real Time Application

<p align="center"><strong><em>Abstract <br /></em></strong></p><p><em>Computer vision is an image processing by a computer to obtain information from image captured through the camera generally used in real-time application. This paper reports on the results of research conducted on computer vision system designed to be able to recognize the image number (0-9) and mathematical operators (addition (+) and subtraction (-)) in a card number figures. Computer vision system designed in this study consists of a camera on the android phone that used to captured images on the card number and the computer that has artificial neural network perceptron algorithm in identifiying images. Both components of the computer vision system are connected wirelessly through the TCP/IP Protocol. At the training stage of Perceptron ANN, 10 samples for each number and mathematical operators are used. Computer vision system built in this study also have several image processing techniques such as greyscalling, thresholding, cropping and resizing. This techniques is used to filter the information from the images captured by camera in order to get the adequate and smaller image to be processed by ANN Perceptron. Stages of testing performed three times. First testing is given picture numbers 0-3, second testing is given picture number 4-7 and third testing is given number 8-9, addition symbol and subtraction symbol. Based on testing result, system built are able to recognize 10 from 12 image rendered with a success rate of 83.33%.</em></p><p><strong><em>Keywords</em></strong><em> : Computer vision, perceptron, card number</em></p><p><em> </em></p><p align="center"><strong><em>Abstrak <br /></em></strong></p><p><em>Computer vision merupakan proses pengolahan citra oleh computer untuk mendapatkan informasi dari citra yang ditangkap melalui kamera yang umumnya digunakan pada aplikasi waktu nyata. Tulisan ini melaporkan tentang hasil penelitian yang dilakukan tentang sistem computer vision yang dirancang untuk dapat mengenali gambar angka (0-9) dan operator matematika(penjumlahan (+) dan pengurangan (-)) pada permainan kartu angka. Sistem computer vision yang dirancang pada penelitian ini terdiri dari kamera pada ponsel android yang digunakan untuk menangkap gambar pada kartu angka dan komputer yang memiliki algoritama Jaringan Syaraf Tiruan Perceptron dalam melakukan identifikasi gambar. Kedua komponen sistem computer vision tersebut dihubungkan memlaui jaringan wireless melalui protocol TCP/IP. Pada tahapan pelatihan JST perceptron, digunakan 10 sample citra untuk masing – masing angka dan operator matematika yang akan dikenali oleh sistem. Pada penelitian ini juga dilakukan tahapan pemrosesan citra sebelum diolah oleh JST Perceptron baik dalam tahapan pelatihan maupun pada saat sistem dijalankan. Tahapan pengolahan citra yang digunakan pada penelitian ini adalah greyscalling, thresholding, cropping dan resizing. Hal ini dilakukan untuk menyaring informasi pada citra yang ditangkap oleh kamera agar didapatkan citra yang berukuran kecil dengan  informasi yang lengkap untuk diproses oleh JST Perceptron. Pada saat sistem diuji coba, diberikan 4 deret kartu angka di depan kamera. Pada pengujian pertama diberikan gambar angka 0-3, pengujian kedua diberikan gambar angka 4-7 dan pada pengujian ketiga diberikan angka 8-9 serta gambar operator penjumlahan dan pengurangan. Berdasarkan pengujian yang dilakukan, sistem computer vision yang dirancang mampu mengenali 10dari 12 gambar yang diberikan dengan tingkat keberhasilan sebesar 83.33%.</em></p><p><strong><em>Kata Kunci </em></strong><em>: computer vision, perceptron, kartu angka</em></p>

Applying Image Processing Methods to Positional Error Evaluation for a Microhole Array

2012 ◽  
Vol 538-541 ◽  
pp. 2131-2134 ◽  
Author(s):  
Dar Yuan Chang ◽  
Yu Xiao Lai ◽  
Ren Bin Fu
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Vision System ◽  
Positional Error ◽  
Error Evaluation ◽  
Computer Vision System ◽  
Critical Feature ◽  
Processing Methods ◽  
Fluid Delivery ◽  
Microhole Array

A microhole array is a critical feature in high-precision products, and acts as a microchannel for fluid delivery or a guiding hole for needle positioning. The precision of microhole fabrication affects the functions of product directly. This study presents a computer vision system that uses image processing methods to evaluate the positional error of a microhole array. A ceramic microhole array drilled by microdrilling process was made to demonstrate the proposed method. Analytical results show that the positional error is related to the precision of machining device noticeably and decreases with the number of holes drilled when adopts the mechanical microdrilling process.

scholarly journals Computer Vision System for Welding Inspection of Liquefied Petroleum Gas Pressure Vessels Based on Combined Digital Image Processing and Deep Learning Techniques

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4505
Author(s):  
Yarens J. Cruz ◽  
Marcelino Rivas ◽  
Ramón Quiza ◽  
Gerardo Beruvides ◽  
Rodolfo E. Haber
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Digital Image Processing ◽  
Digital Image ◽  
Vision System ◽  
Pressure Vessels ◽  
Liquefied Petroleum Gas ◽  
Computer Vision System ◽  
Learning Techniques

One of the most important operations during the manufacturing process of a pressure vessel is welding. The result of this operation has a great impact on the vessel integrity; thus, welding inspection procedures must detect defects that could lead to an accident. This paper introduces a computer vision system based on structured light for welding inspection of liquefied petroleum gas (LPG) pressure vessels by using combined digital image processing and deep learning techniques. The inspection procedure applied prior to the welding operation was based on a convolutional neural network (CNN), and it correctly detected the misalignment of the parts to be welded in 97.7% of the cases during the method testing. The post-welding inspection procedure was based on a laser triangulation method, and it estimated the weld bead height and width, with average relative errors of 2.7% and 3.4%, respectively, during the method testing. This post-welding inspection procedure allows us to detect geometrical nonconformities that compromise the weld bead integrity. By using this system, the quality index of the process was improved from 95.0% to 99.5% during practical validation in an industrial environment, demonstrating its robustness.

Computational image processing for a computer vision system using biomimetic sensors and eigenspace object models

2004 ◽  
Author(s):  
Cameron H. G. Wright ◽  
Steven F. Barrett ◽  
Daniel J. Pack ◽  
Thomas R. Schei ◽  
Jeffrey R. Anderson ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Vision System ◽  
Computer Vision System ◽  
Object Models

Modern Technology for Image processing and Computer vision -A Review

2018 ◽  
Vol 1 (2) ◽  
pp. 17-23
Author(s):  
Takialddin Al Smadi
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Augmented Reality ◽  
Video Compression ◽  
Video Processing ◽  
Vision System ◽  
Citrus Fruit ◽  
Modern Technology ◽  
Image And Video Processing ◽  
Depth Video

This survey outlines the use of computer vision in Image and video processing in multidisciplinary applications; either in academia or industry, which are active in this field.The scope of this paper covers the theoretical and practical aspects in image and video processing in addition of computer vision, from essential research to evolution of application.In this paper a various subjects of image processing and computer vision will be demonstrated ,these subjects are spanned from the evolution of mobile augmented reality (MAR) applications, to augmented reality under 3D modeling and real time depth imaging, video processing algorithms will be discussed to get higher depth video compression, beside that in the field of mobile platform an automatic computer vision system for citrus fruit has been implemented ,where the Bayesian classification with Boundary Growing to detect the text in the video scene. Also the paper illustrates the usability of the handed interactive method to the portable projector based on augmented reality.   © 2018 JASET, International Scholars and Researchers Association

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