READING BARCODES USING DIGITAL CAMERAS THROUGH IMAGE PROCESSING

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "READING BARCODES USING DIGITAL CAMERAS THROUGH IMAGE PROCESSING"

Transcription

1 Proceedings of 5th International Symposium on Intelligent Manufacturing Systems, May 29-31, 2006: Sakarya University, Department of Industrial Engineering READING BARCODES USING DIGITAL CAMERAS THROUGH IMAGE PROCESSING Authors: Author : Emre BAŞARAN Tel : Author : Özgür ULUÇAY Tel : Author : Assoc. Prof. Dr. Sarp ERTÜRK Tel : / 2204 About the Authors: Emre Başaran was born on 13th May 1983 in Balıkesir, Turkey. He is a last year student in the Electronic and Telecom Engineering department of Kocaeli University. Since the last year he has been working on a project based on barcode reading with image processing techniques. He enjoys computer programming with C, C# and is interested in new technologies like mobile communications. He wants to continue his studies in signal processing over mobile communications. Özgür Ulucay was born in 5 May 1980 in Eskişehir, Turkey. He graduated from the Electronic and Tel. Engineering department of Kocaeli University. He gained the Master degree in 2004 and continues towards the PhD. degree in the same department. He enjoys computer programming and studies 3-D modeling and signal processing. He has some papers in the computer graphics field. He continues to study in signal processing over internet and mobile programming technologies. Sarp Ertürk received his B.Sc. in Electrical and Electronics Engineering from Middle East Technical University, Ankara in He received his M.Sc. in Telecommunication and Information Systems and Ph.D. in Electronic Systems Engineering in 1996 and 1999 respectively from the University of Essex, U.K. From 1999 to 2001 he carried out his compulsory service at the Army Academy, Ankara as Lecturer in the Electrical Engineering department. Since 2001 he has been with the University of Kocaeli, Turkey, where he is currently appointed as Associate Professor. He has established and is still directing KULIS (Kocaeli University Laboratory of Image and Signal processing). He has been offered a Distinguished Visiting Professor position by Chung-Ang University, South Korea, between March-September His research interests are in the area of digital signal and image processing.

2 Abstract: All people go to the supermarket to buy something that needs lots of time. Each product has a label made up of lines with different thicknesses. We go to cashbox when buying these products. Cashiers pass the products through a hand-scanner to see the corresponding price. In this paper, a method is presented for barcode reading with camera which is based on image processing. Normally, the barcode reading process is performed by laser readers, and laser readers are necessary tools for barcode readings. In this paper we present an alternative method for barcode reading directly from camera images. The advantage of the proposed method is that barcodes can be read directly from the line, having product barcodes look upwards facing a camera located above the line, so that it will not be necessary to pick up all individual products to pass them through a laser scanner manually. Thus the proposed approach will provide speed and simplicity. Introduction: Generally, barcodes are symbols shaped in the form of rectangles which consist of thin or thick parallel lines parallel to each other. Barcodes provide means for automatic rapid data input into the computer. Since the last decade, barcodes are being used in many areas such as market products and electronic devices. The lines on barcodes contain the reference number of the product. This information should be recorded in computers to store each product separately for counting company sales and purchase quantities. When reading barcodes on products using some laser scanning device, a signal is generated by the system and is processed in the computer by some software. Then this information is used to determine which product is selected. This process provides rapid and reliable sales opportunities to companies for selling their products. There are very different types of barcodes: EAN, EAN-13, EAN-8, Code 39, Code 93, Code 128, and UPC are well known. UPC and EAN types are most commonly used cases. The UPC numbering system is used in Canada and America, while the EAN 13 numbering system is used in Europe and Turkey [3]. Figure 1: A sample barcode in EAN 13 numbering system 836

3 Figure 1 shows a sample barcode in the EAN 13 numbering system structure. The black lines on the top of the barcode represent the logic true or 1, and the blanks represent the logic false or 0. The width of the thinnest black line is the reference width. The reference width is presented by a single bit and the largest line width of the barcodes can be four times the reference width at the maximum. In the same way, the width of the thinnest white line has a reference value, and the thickest white line can have a width of four times the reference width at maximum. There are starting and finishing codes equal to 101 values at the beginning and end of the barcode which show the reference widths. There is a longer barcode giving the value in the center of the barcode and this code lines have reference widths, too. Barcodes are formed in the form of a bit string according to line thickness to analyze a barcode. There are 12 numbers on the barcode and each number is defined by 7 bits. Barcodes also have 3 start, 3 stop and 5 bits in the middle for referencing. As a result the barcodes are defined by 95 bits in total. Barcode values are not obtained directly from these 95 bits long codes. These values will be obtained using barcode numbering schemes. In this paper, the EAN 13 numbering scheme is used to determine the barcode reading process. The methodology to achieve the value of the barcodes using this scheme includes the following structure: 1. Find reference width using start (3); stop (3) and center (5) guard bits. 2. Remove the reference bits (3 start, 3 stop, and 5 center bits) from the barcode code bit string. As a result obtain the 84 bits long barcode information code bit string. 3. Divide the barcode bits into two areas. The first area consists of the first 42 bits, and the second area consists of the last 42 bits. For the first area: 4. Find first the decimal value parity of the barcode. This parity value will be used to determine the order of barcode code readings. The first 42 bits will be read using the encoding table shown in Figure 2b according to the order obtained from the odd-even table shown in Figure 2a. 5. Select seven (7) bit to encode/decode a digit: a. Find the resulting value by searching the respective odd/even column of the left-hand encoding table shown in Figure 2b. b. Go to step 5 if remaining bit count is bigger than zero. For the second area: 6. Select seven (7) bits to encode/decode a digit: a. Find the resulting value by searching the right-hand encoding table shown in figure 2b. b. Go to step 6 if remaining bit count is bigger than zero. 7. Construct the barcode code using the obtained values. Algorithm 1: Barcode encoding algorithm for EAN 13 numbering system. 837

4 a) Odd/Even Parity Table b) Encoding Table Figure 2: Encoding tables for the EAN 13 numbering system. As the first decimal number (for instance the first 9 in the barcode shown in Figure 1) shows the corresponding row of the odd/even parity table and is not encoded in the barcode, it cannot be extracted by identifying the thickness of vertical lines. In laser scanner systems, this number is obtained using parity calculation. For simplification, the parity calculation step is omitted in the proposed system, and both sides of the left-hand encoding approach shown in Figure 2b is entered into the database and the correct value is determined by comparing against both (odd and even parity) cases. Approach: Our approach contains an edge detection algorithm to obtain barcode borders from images acquired using a camera, and some threshold mechanism to process images as binary patterns. At first, an image that contains the barcode information is acquired using a camera. The color image contains in fact the full usable information. For faster and simple processing, the image is converted to grayscale format. Because the acquired image will typically contain an area larger than the barcode, it is initially required to crop the barcode area as the rest of the image is unnecessary. Because only the barcode region is required, the borders of the barcode line coordinates must be determined. An edge detection algorithm can be used for determining the borders. Edge detection algorithms can use different kernels. The Canny edge detector provides a reasonable and successful approach for edge detection [1]. The Canny detector uses two kernels, one for the vertical edges and one for the horizontal edges. The utilized kernels are shown in Figure 3. The Canny algorithm provides an optimal edge detector based on a set of 838

5 criteria which includes finding the most edges by minimizing the error rate, marking edges as closely as possible to the actual edges to maximize localization, and marking edges only once when a single edge exists for minimal response [2]. Figure 3: Canny edge detection kernels. The left kernel is used for the detection of vertical edges and the right kernel is used for the detection of the horizontal edges [2]. In this paper Canny edge detection kernels are used to determine the barcode area. As a barcode is made up of vertical lines, it has been found that utilization of the vertical edge detection kernel is sufficient. Hence the computational load is reduced by using only the vertical edge detection kernel instead of both kernels. Hence, a single pass with the vertical edge detection kernel is used to determine the barcode area. After applying the edge detection kernel, a threshold algorithm is applied on the resulting image. At this stage, the image indicates all vertical lines including barcode lines. The algorithm that is used in this work is described below in algorithm 2. Note that For all pixels: 1. Select the pixel to be processed and determine the kernel area. 2. Apply the vertical edge kernel shown in figure 3 (the left picture) to the selected kernel area. 3. If the result is positive, the new pixel value will be the resulting value. 4. If the result is negative, go to step The average value of the output is calculated. 6. Threshold the output using this average value. This algorithm gives all vertical lines in an image. This process is shown for example barcode images in Figures 4-6 below. Figure 4: An image with purple background color. 839

6 Figure 5: An image with white background color. Figure 6: An image with red background color. The obtained white lines on the resulted image after Canny edge detection and thresholding will then be separated into several parts. This partitioning is accomplished by finding neighbor pixels in the image. All neighbor white pixels must stay together, and all pixels that are not-neighbors must be separated. All of the resulted neighbor pixels will be stored in the form of neighbor tables as an array. This operation is summarized in algorithm 3 given below. For all pixels: 1. Select the pixel to be processed. 2. If the pixel is black go to 1. Else go to the next step. 3. If any neighbor table exists, go to step 4. Else construct a new neighbor table entry. 4. For all neighbor tables a) Search all pixel coordinates in the table to detect any neighbor relationship with a selected pixel. b) If any relationship is found, put this pixel in this table and return to step 1. c) If any relationship is not found, construct a new table and put this pixel in it. Go to step 1. Algorithm 3: Constructing neighbor tables. These tables include all the barcode line places and unnecessary noise. For eliminating unnecessary noise from the barcode information we have to determine the differences between them. To do that, at first we must determine the table entries that only includes barcode line places. The size of tables which include barcode lines must be equal or very close to each other; because all barcode line heights are equal in an image. 840

7 There are 30 black stripes and 29 white stripes in every barcode. Our edge detection algorithm just detects the passing from black to white, so there will be 30 tables which have same or very close sizes. In this approach, the 30 tables which contain the barcode line pixels must be consecutive. At first we must determine the barcode upper-left pixel coordinate which is mainly any one of the available table entries. Then we have to search all tables to determine the barcode area. To do that, we use a standard deviation value to find all 30 barcode lines which follow each other, and have the same or close size. This approach is described in algorithm 4 below. For all tables: 1. Determine the standard deviation of region areas 2. Construct one counter that counts neighbor tables which have suitable standard deviation and set counter value to zero. 3. Select one table for comparison of table sizes if there is no selected table. a) Get the table size. 4. Select next table for comparison. a) Get the second table size. 5. Compare the values using computed standard deviation. a) If division of table size value difference is below the standard deviation. Increase counter value and assign the first table as the one with this counter value. I. If the counter value is equal to 30 Go to step 6. II. Else go to step 4. b) Else go to step First and second selected tables together indicate the barcode start and stop positions, respectively. The area covering the pixel values of the first and second table is the barcode field. Algorithm 4: Determining the Barcode Area The approach given in Algorithm 4 is used to obtain the barcode fields in the sample images shown in Figure 4-6. In fact, these fields indicate the coordinates of the barcode fields. These coordinates will be used to obtain the real barcode lines. The lines that are included in the tables are not the actual barcode lines yet, they only indicate the barcode line positions. The barcode images only contain barcode lines and their threshold equivalents are obtained using these coordinates as shown in Figure (7-9) below. Figure 7: The barcode field of the full image shown in Figure

8 Figure 8: The barcode field of the full image shown in Figure 5. Figure 9: The barcode field of the full image in Figure 6. After barcode fields are cut out from the image, a threshold is applied on these fields using the mean values of these fields. Then, the resulting image only contains the barcode lines which indicate the barcode codes. The barcode codes are obtained using these images using an algorithm that stores white and black lines together in arrays by determining positions of these lines described above. Then, every barcode code is obtained using these array sizes. Note that the reference sizes have to be considered to make codes meaningful. This operation is given in Algorithm 5 below. 1. Construct a variable refsize to indicate a reference size, and a variable result for the result code. 2. Select first array to be processed. 3. This array should contain black pixels. The refsize variable is set to this array size. For all lines: 4. Select next array if any unselected array exists else go to step Divide this array size to refsize. The calculated floating value is stored as a variable codevalbyref. Round this value to the closest integer. 6. If this array includes black pixels write logic 1 for the corresponding amount of the codevalbyref value to the result value. Go to step If this array includes white pixels write logic 0 for the corresponding amount of the codevalbyref value to the result value. Go to step The result value must be the 95 bits long barcode code. Algorithm 5: Determining the Barcode Code 842

9 After obtaining the 95 bits long barcode code, it must be converted to meaningful decimal numbers. This can be accomplished using Algorithm 1, as described earlier. The resulting decimal numbers indicates the barcode code and is meaningfully constructed as shown in Figure 10 using algorithm 5 and 1 for sample images shown in Figures (7-9) Figure 10: The barcode results obtained for the sample images A total of 15 barcode images with differently sized barcodes have been processed with the presented method. A 100 % correct detection performance has been obtained for the method. In the future it is planned to evaluate the methods on a larger database, with images taken under different lighting conditions, with various orientations and multiple barcodes within an image. Conclusions: In this paper; a method for barcode reading with a camera based on image processing has been presented. First of all; edges are detected in the image using an edge detection method and then white areas are assigned into arrays. Then we subtract the barcode area from the original image using these array sizes. A threshold is applied using images mean values of the barcode field after this process. Then we get the barcode number from black and white areas obtained after threshold process. The barcode reading process has been carried out fast, effectively and successfully with the presented method. References: [1] Canny, J., A Computational Approach to Edge Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, 8: , November 1986 [2] Hong Shan Neoh, Asher Hazanchuk, Adaptive Edge Detection for Real-Time Video Processing using FPGAs, GSPx 2004 conference paper: [3] Barcode toolkits from Softek Software: [4] Vault Information Services LLC: 843

Locating and Decoding EAN-13 Barcodes from Images Captured by Digital Cameras

Locating and Decoding EAN-13 Barcodes from Images Captured by Digital Cameras Locating and Decoding EAN-13 Barcodes from Images Captured by Digital Cameras W3A.5 Douglas Chai and Florian Hock Visual Information Processing Research Group School of Engineering and Mathematics Edith

More information

Implementation of Canny Edge Detector of color images on CELL/B.E. Architecture.

Implementation of Canny Edge Detector of color images on CELL/B.E. Architecture. Implementation of Canny Edge Detector of color images on CELL/B.E. Architecture. Chirag Gupta,Sumod Mohan K cgupta@clemson.edu, sumodm@clemson.edu Abstract In this project we propose a method to improve

More information

Canny Edge Detection

Canny Edge Detection Canny Edge Detection 09gr820 March 23, 2009 1 Introduction The purpose of edge detection in general is to significantly reduce the amount of data in an image, while preserving the structural properties

More information

Signature Region of Interest using Auto cropping

Signature Region of Interest using Auto cropping ISSN (Online): 1694-0784 ISSN (Print): 1694-0814 1 Signature Region of Interest using Auto cropping Bassam Al-Mahadeen 1, Mokhled S. AlTarawneh 2 and Islam H. AlTarawneh 2 1 Math. And Computer Department,

More information

Image Estimation Algorithm for Out of Focus and Blur Images to Retrieve the Barcode Value

Image Estimation Algorithm for Out of Focus and Blur Images to Retrieve the Barcode Value IJSTE - International Journal of Science Technology & Engineering Volume 1 Issue 10 April 2015 ISSN (online): 2349-784X Image Estimation Algorithm for Out of Focus and Blur Images to Retrieve the Barcode

More information

BRESHENHAM S ALGORITHM

BRESHENHAM S ALGORITHM On-Line Computer Graphics Notes BRESHENHAM S ALGORITHM Kenneth I. Joy Visualization and Graphics Research Group Department of Computer Science University of California, Davis Overview The basic line drawing

More information

Barcode Based Automated Parking Management System

Barcode Based Automated Parking Management System IJSRD - International Journal for Scientific Research & Development Vol. 2, Issue 03, 2014 ISSN (online): 2321-0613 Barcode Based Automated Parking Management System Parth Rajeshbhai Zalawadia 1 Jasmin

More information

A Guide to Creating Machine-Readable Forms

A Guide to Creating Machine-Readable Forms A Guide to Creating Machine-Readable Forms 2009 ABBYY. All rights reserved. Table of Contents What is a Form?... 3 Machine Readable Forms... 4 Form Completion Methods...4 Elements of Machine Readable Forms...4

More information

Line Separation for Complex Document Images Using Fuzzy Runlength

Line Separation for Complex Document Images Using Fuzzy Runlength Line Separation for Complex Document Images Using Fuzzy Runlength Zhixin Shi and Venu Govindaraju Center of Excellence for Document Analysis and Recognition(CEDAR) State University of New York at Buffalo,

More information

Visual Product Identification for Blind

Visual Product Identification for Blind RESEARCH ARTICLE OPEN ACCESS Visual Product Identification for Blind Krutarth Majithia*, Darshan Sanghavi**, Bhavesh Pandya***, Sonali Vaidya**** *(Student, Department of Information Technology, St, Francis

More information

Arithmetic Operations

Arithmetic Operations Arithmetic Operations Dongbing Gu School of Computer Science and Electronic Engineering University of Essex UK Spring 2013 D. Gu (Univ. of Essex) Arithmetic Operations Spring 2013 1 / 34 Outline 1 Introduction

More information

Sheet Music Reader 1 INTRODUCTION 2 IMPLEMENTATION

Sheet Music Reader 1 INTRODUCTION 2 IMPLEMENTATION 1 Sheet Music Reader Sevy Harris, Prateek Verma Department of Electrical Engineering Stanford University Stanford, CA sharris5@stanford.edu, prateekv@stanford.edu Abstract The goal of this project was

More information

222 The International Arab Journal of Information Technology, Vol. 1, No. 2, July 2004 particular pixels. High pass filter and low pass filter are gen

222 The International Arab Journal of Information Technology, Vol. 1, No. 2, July 2004 particular pixels. High pass filter and low pass filter are gen The International Arab Journal of Information Technology, Vol. 1, No. 2, July 2004 221 A New Approach for Contrast Enhancement Using Sigmoid Function Naglaa Hassan 1&2 and Norio Akamatsu 1 1 Department

More information

Word Spotting in Cursive Handwritten Documents using Modified Character Shape Codes

Word Spotting in Cursive Handwritten Documents using Modified Character Shape Codes Word Spotting in Cursive Handwritten Documents using Modified Character Shape Codes Sayantan Sarkar Department of Electrical Engineering, NIT Rourkela sayantansarkar24@gmail.com Abstract.There is a large

More information

Hybrid Lossless Compression Method For Binary Images

Hybrid Lossless Compression Method For Binary Images M.F. TALU AND İ. TÜRKOĞLU/ IU-JEEE Vol. 11(2), (2011), 1399-1405 Hybrid Lossless Compression Method For Binary Images M. Fatih TALU, İbrahim TÜRKOĞLU Inonu University, Dept. of Computer Engineering, Engineering

More information

Unit 2: Number Systems, Codes and Logic Functions

Unit 2: Number Systems, Codes and Logic Functions Unit 2: Number Systems, Codes and Logic Functions Introduction A digital computer manipulates discrete elements of data and that these elements are represented in the binary forms. Operands used for calculations

More information

LIST OF CONTENTS CHAPTER CONTENT PAGE DECLARATION DEDICATION ACKNOWLEDGEMENTS ABSTRACT ABSTRAK

LIST OF CONTENTS CHAPTER CONTENT PAGE DECLARATION DEDICATION ACKNOWLEDGEMENTS ABSTRACT ABSTRAK vii LIST OF CONTENTS CHAPTER CONTENT PAGE DECLARATION DEDICATION ACKNOWLEDGEMENTS ABSTRACT ABSTRAK LIST OF CONTENTS LIST OF TABLES LIST OF FIGURES LIST OF NOTATIONS LIST OF ABBREVIATIONS LIST OF APPENDICES

More information

Fast Detection and Tracking of Faces in Uncontrolled Environments for Autonomous Robots Using the CNN-UM

Fast Detection and Tracking of Faces in Uncontrolled Environments for Autonomous Robots Using the CNN-UM Fast Detection and Tracking of Faces in Uncontrolled Environments for Autonomous Robots Using the CNN-UM J. McRaven, M. Scheutz, Gy. Cserey, V. Andronache and W. Porod Department of Electrical Engineering

More information

CSc 28 Data representation. CSc 28 Fall

CSc 28 Data representation. CSc 28 Fall CSc 28 Data representation 1 Binary numbers Binary number is simply a number comprised of only 0's and 1's. Computers use binary numbers because it's easy for them to communicate using electrical current

More information

All V7 registers support barcode printing, except the Sharp 410/420 1A ROM and that limitation is based upon the register.

All V7 registers support barcode printing, except the Sharp 410/420 1A ROM and that limitation is based upon the register. Tools Section Barcode Printing These are basic instructions for Version 7 Polling barcode printing. Users will need to have a PLU/UPC file containing either UPC-A, UPC-E, EAN 13 or EAN 8 numbers, label

More information

Digital Image Processing Using Matlab. Haris Papasaika-Hanusch Institute of Geodesy and Photogrammetry, ETH Zurich

Digital Image Processing Using Matlab. Haris Papasaika-Hanusch Institute of Geodesy and Photogrammetry, ETH Zurich Haris Papasaika-Hanusch Institute of Geodesy and Photogrammetry, ETH Zurich haris@geod.baug.ethz.ch Images and Digital Images A digital image differs from a photo in that the values are all discrete. Usually

More information

Barcodes principle. Identification systems (IDFS) Department of Control and Telematics Faculty of Transportation Sciences, CTU in Prague

Barcodes principle. Identification systems (IDFS) Department of Control and Telematics Faculty of Transportation Sciences, CTU in Prague Barcodes principle Identification systems (IDFS) Department of Control and Telematics Faculty of Transportation Sciences, CTU in Prague Contents How does it work? Bulls eye code PostNet 1D Bar code 2D

More information

Handwritten Character Recognition from Bank Cheque

Handwritten Character Recognition from Bank Cheque International Journal of Computer Sciences and Engineering Open Access Research Paper Volume-4, Special Issue-1 E-ISSN: 2347-2693 Handwritten Character Recognition from Bank Cheque Siddhartha Banerjee*

More information

Barcode Reference Guide

Barcode Reference Guide Version 1.0 Barcode Reference Guide Revision 3 3/24/2003 Barcode Module Reference Guide Barcode Reference Guide Revision 3 3/24/2003 Table of Contents Introduction...7 Barcode...7 Requirements...7 Barcode

More information

Directions to Candidates

Directions to Candidates i MATRICULATION AND SECONDARY EDUCATION CERTIFICATE EXAMINATIONS BOARD UNIVERSITY OF MALTA, MSIDA SECONDARY EDUCATION CERTIFICATE LEVEL MAY 2010 SESSION SUBJECT: Computer Studies PAPER NUMBER: IIA DATE:

More information

Automatic Extraction of Signatures from Bank Cheques and other Documents

Automatic Extraction of Signatures from Bank Cheques and other Documents Automatic Extraction of Signatures from Bank Cheques and other Documents Vamsi Krishna Madasu *, Mohd. Hafizuddin Mohd. Yusof, M. Hanmandlu ß, Kurt Kubik * *Intelligent Real-Time Imaging and Sensing group,

More information

The ID Technology. Introduction to GS1 Barcodes

The ID Technology. Introduction to GS1 Barcodes The ID Technology Introduction to GS1 Barcodes Contents GS1 - The Basics 2 Starting Point - GTIN 3 GTIN Labels for Cases - ITF-14 5 Adding More Data - GS1 128 6 GS1 Application Identifiers 7 Logistics

More information

3. BINARY NUMBERS AND ARITHMETIC

3. BINARY NUMBERS AND ARITHMETIC 3. BINARY NUMBERS AND ARITHMETIC 3.1. Binary Numbers The only place in a computer where you will find the number 9 is on the keyboard. Inside the computer you will only find 0 s and 1 s. All computer memory

More information

ENGR 1000, Introduction to Engineering Design. Counting in Binary

ENGR 1000, Introduction to Engineering Design. Counting in Binary ENGR 1000, Introduction to Engineering Design Unit 1: Prerequisite Knowledge for Mechatronics Systems Lesson 1.1: Converting binary numbers to decimal numbers and back Objectives: Convert decimal numbers

More information

A guide to barcode symbology for the logistics industry

A guide to barcode symbology for the logistics industry A guide to barcode symbology for the logistics industry Symbology in barcodes Barcode technologies provide fast reliable data collection to ensure item or package traceability, and enhance customer service.

More information

JEDMICS C4 COMPRESSED IMAGE FILE FORMAT TECHNICAL SPECIFICATION FOR THE JOINT ENGINEERING DATA MANAGEMENT INFORMATION AND CONTROL SYSTEM (JEDMICS)

JEDMICS C4 COMPRESSED IMAGE FILE FORMAT TECHNICAL SPECIFICATION FOR THE JOINT ENGINEERING DATA MANAGEMENT INFORMATION AND CONTROL SYSTEM (JEDMICS) JEDMICS C4 COMPRESSED IMAGE FILE FORMAT TECHNICAL SPECIFICATION FOR THE JOINT ENGINEERING DATA MANAGEMENT INFORMATION AND CONTROL SYSTEM (JEDMICS) APRIL 2002 PREPARED BY: NORTHROP GRUMMAN INFORMATION TECHNOLOGY

More information

Form Design Guidelines Part of the Best-Practices Handbook. Form Design Guidelines

Form Design Guidelines Part of the Best-Practices Handbook. Form Design Guidelines Part of the Best-Practices Handbook Form Design Guidelines I T C O N S U L T I N G Managing projects, supporting implementations and roll-outs onsite combined with expert knowledge. C U S T O M D E V E

More information

As we have discussed, digital circuits use binary signals but are required to handle

As we have discussed, digital circuits use binary signals but are required to handle Chapter 2 CODES AND THEIR CONVERSIONS 2.1 INTRODUCTION As we have discussed, digital circuits use binary signals but are required to handle data which may be alphabetic, numeric, or special characters.

More information

Examples of Solved Problems for Chapter3,5,6,7,and8

Examples of Solved Problems for Chapter3,5,6,7,and8 Chapter 3 Examples of Solved Problems for Chapter3,5,6,7,and8 This document presents some typical problems that the student may encounter, and shows how such problems can be solved. Note that the numbering

More information

To effectively manage and control a factory, we need information. How do we collect it?

To effectively manage and control a factory, we need information. How do we collect it? Auto-ID 321 Auto-ID Data-collection needs: What is our WIP? What is productivity or assignment of employees? What is utilization of machines? What is progress of orders? What is our inventory? What must

More information

ADVANCED APPLICATIONS OF ELECTRICAL ENGINEERING

ADVANCED APPLICATIONS OF ELECTRICAL ENGINEERING Development of a Software Tool for Performance Evaluation of MIMO OFDM Alamouti using a didactical Approach as a Educational and Research support in Wireless Communications JOSE CORDOVA, REBECA ESTRADA

More information

Face detection is a process of localizing and extracting the face region from the

Face detection is a process of localizing and extracting the face region from the Chapter 4 FACE NORMALIZATION 4.1 INTRODUCTION Face detection is a process of localizing and extracting the face region from the background. The detected face varies in rotation, brightness, size, etc.

More information

Chess Vision. Chua Huiyan Le Vinh Wong Lai Kuan

Chess Vision. Chua Huiyan Le Vinh Wong Lai Kuan Chess Vision Chua Huiyan Le Vinh Wong Lai Kuan Outline Introduction Background Studies 2D Chess Vision Real-time Board Detection Extraction and Undistortion of Board Board Configuration Recognition 3D

More information

Enhanced Bar Code Engine

Enhanced Bar Code Engine Enhanced Bar Code Engine Introduction Access to the Kofax Standard bar code recognition engine is provided through ImageControls-based applications and ISIS-based applications. In addition to the standard

More information

If you are already comfortable with the use of graphs, you can omit the following and do the self-test.

If you are already comfortable with the use of graphs, you can omit the following and do the self-test. be made to keep the time free. How? One of the most frustrating problems for a professor is to have a student arrive on the day before the exam and say "I don't understand anything you are doing in class."

More information

Blind Deconvolution of Barcodes via Dictionary Analysis and Wiener Filter of Barcode Subsections

Blind Deconvolution of Barcodes via Dictionary Analysis and Wiener Filter of Barcode Subsections Blind Deconvolution of Barcodes via Dictionary Analysis and Wiener Filter of Barcode Subsections Maximilian Hung, Bohyun B. Kim, Xiling Zhang August 17, 2013 Abstract While current systems already provide

More information

The string of digits 101101 in the binary number system represents the quantity

The string of digits 101101 in the binary number system represents the quantity Data Representation Section 3.1 Data Types Registers contain either data or control information Control information is a bit or group of bits used to specify the sequence of command signals needed for

More information

Data Storage 3.1. Foundations of Computer Science Cengage Learning

Data Storage 3.1. Foundations of Computer Science Cengage Learning 3 Data Storage 3.1 Foundations of Computer Science Cengage Learning Objectives After studying this chapter, the student should be able to: List five different data types used in a computer. Describe how

More information

Pick the Right Size Book for Your Unique Project!

Pick the Right Size Book for Your Unique Project! For each project we ultimately need two PDF files; one for the Cover and one for the interior text (The Block). The Cover includes: back cover, front cover, and spine. The Block is all the rest. Pick the

More information

How to document Working with Barcodes ( 1D and 2D )

How to document Working with Barcodes ( 1D and 2D ) Barcodes and what can do with ScannerVision Barcodes in ScannerVision can be used as the source of metadata or as document splitters or both. As the source of metadata the data contained in the barcode

More information

VISUAL ALGEBRA FOR COLLEGE STUDENTS. Laurie J. Burton Western Oregon University

VISUAL ALGEBRA FOR COLLEGE STUDENTS. Laurie J. Burton Western Oregon University VISUAL ALGEBRA FOR COLLEGE STUDENTS Laurie J. Burton Western Oregon University VISUAL ALGEBRA FOR COLLEGE STUDENTS TABLE OF CONTENTS Welcome and Introduction 1 Chapter 1: INTEGERS AND INTEGER OPERATIONS

More information

User Manual Microsoft Dynamics AX Add-on LabAX Label Printing

User Manual Microsoft Dynamics AX Add-on LabAX Label Printing User Manual Microsoft Dynamics AX Add-on LabAX Label Printing Version 1.7 Last Update: 17.04.2011 User Manual Microsoft Dynamics AX Add-on LabAX Label Printing Page 2 / 23 Contents 1 Introduction... 3

More information

Matt Cabot Rory Taca QR CODES

Matt Cabot Rory Taca QR CODES Matt Cabot Rory Taca QR CODES QR codes were designed to assist in the manufacturing plants of the automotive industry. These easy to scan codes allowed for a rapid way to identify parts and made the entire

More information

Morphological segmentation of histology cell images

Morphological segmentation of histology cell images Morphological segmentation of histology cell images A.Nedzved, S.Ablameyko, I.Pitas Institute of Engineering Cybernetics of the National Academy of Sciences Surganova, 6, 00 Minsk, Belarus E-mail abl@newman.bas-net.by

More information

PK Font Compression. Figure 1.1: Old Macintosh OS 6 Fonts.

PK Font Compression. Figure 1.1: Old Macintosh OS 6 Fonts. PK Font Compression Obviously, these words are hard to read because the individual characters feature different styles and sizes. In a beautifully-typeset document, all the letters of a word and all the

More information

Softek Software Ltd. Softek Barcode Reader Toolkit for Android. Product Documentation V7.5.1

Softek Software Ltd. Softek Barcode Reader Toolkit for Android. Product Documentation V7.5.1 Softek Software Ltd Softek Barcode Reader Toolkit for Android Product Documentation V7.5.1 1 Contents 2... 1 3 Installation... 1 4 Calling Bardecoder from another App... 1 5 Settings for the Bardecoder

More information

Chapter 1 Digital Systems and Information

Chapter 1 Digital Systems and Information Logic and Computer Design Fundamentals Chapter 1 Digital Systems and Information Charles Kime & Thomas Kaminski 2008 Pearson Education, Inc. Overview Digital Systems, Computers, and Beyond Information

More information

The Role of Size Normalization on the Recognition Rate of Handwritten Numerals

The Role of Size Normalization on the Recognition Rate of Handwritten Numerals The Role of Size Normalization on the Recognition Rate of Handwritten Numerals Chun Lei He, Ping Zhang, Jianxiong Dong, Ching Y. Suen, Tien D. Bui Centre for Pattern Recognition and Machine Intelligence,

More information

Avery Dennison UK Consumer Helpline: 0800 80 50 20 Consumer email: consumerservice-uk@eu.averydennison.com

Avery Dennison UK Consumer Helpline: 0800 80 50 20 Consumer email: consumerservice-uk@eu.averydennison.com Avery DesignPro for PC Frequently Asked Questions General Information Questions Q: What are the system requirements for DesignPro? A: The following is required to run DesignPro: Microsoft Windows VistaTM,

More information

13. NUMBERS AND DATA 13.1 INTRODUCTION

13. NUMBERS AND DATA 13.1 INTRODUCTION 13. NUMBERS AND DATA 13.1 INTRODUCTION Base 10 (decimal) numbers developed naturally because the original developers (probably) had ten fingers, or 10 digits. Now consider logical systems that only have

More information

ELFRING FONTS INC. MICR FONTS FOR WINDOWS

ELFRING FONTS INC. MICR FONTS FOR WINDOWS ELFRING FONTS INC. MICR FONTS FOR WINDOWS This package contains ten MICR fonts (also known as E-13B) used to print the magnetic encoding lines on checks, and eight Secure Fonts for use in printing check

More information

Automatic Traffic Estimation Using Image Processing

Automatic Traffic Estimation Using Image Processing Automatic Traffic Estimation Using Image Processing Pejman Niksaz Science &Research Branch, Azad University of Yazd, Iran Pezhman_1366@yahoo.com Abstract As we know the population of city and number of

More information

Application of Data Matrix Verification Standards

Application of Data Matrix Verification Standards Data Matrix symbol verification at its most basic level eliminates the subjective quality determination that causes discord between marking and reading suppliers, and replaces those subjective opinions

More information

Integer multiplication

Integer multiplication Integer multiplication Suppose we have two unsigned integers, A and B, and we wish to compute their product. Let A be the multiplicand and B the multiplier: A n 1... A 1 A 0 multiplicand B n 1... B 1 B

More information

(888) 511-0264 CALL TOLL FREE. We offer Printed Barcode Labels for your Barcode Numbers. New Orders. Repeat orders HOW TO ORDER BARCODE LABELS

(888) 511-0264 CALL TOLL FREE. We offer Printed Barcode Labels for your Barcode Numbers. New Orders. Repeat orders HOW TO ORDER BARCODE LABELS Barcode Label Printing Prices Need a Special Size Barcode Label? We have many other sizes to fit your needs for Barcode Labels. CALL For Information To Place a Barcode Label Order or For any Questions...

More information

Skeletonization (part 1) Jason Rupard CAP6400

Skeletonization (part 1) Jason Rupard CAP6400 Skeletonization (part 1) Jason Rupard CAP6400 Skeletonization Topic Introduction Medial-Axis Transform (MAT) Thinning Other Concepts Introduction What is a skeleton? Opinion Do we know it? Skeleton Represents

More information

APPLICATION OF HOUGH TRANSFORM AND SUB-PIXEL EDGE DETECTION IN 1-D BARCODE SCANNING

APPLICATION OF HOUGH TRANSFORM AND SUB-PIXEL EDGE DETECTION IN 1-D BARCODE SCANNING APPLICATION OF HOUGH TRANSFORM AND SUB-PIXEL EDGE DETECTION IN 1-D BARCODE SCANNING Harsh Kapadia 1, Alpesh Patel 2 Assistant Professor, Dept. of Electrical Engg., Institute of Technology, Nirma University,

More information

A Motion-Tracking DMX512 Controller Miren Bamforth Project Proposal Fall 2014

A Motion-Tracking DMX512 Controller Miren Bamforth Project Proposal Fall 2014 A Motion-Tracking DMX512 Controller Miren Bamforth 6.111 Project Proposal Fall 2014 1 Overview Some modern theatrical lighting instruments are able to rotate in two dimensions; they are referred to as

More information

Binary Codes. Objectives. Binary Codes for Decimal Digits

Binary Codes. Objectives. Binary Codes for Decimal Digits Binary Codes Objectives In this lesson, you will study: 1. Several binary codes including Binary Coded Decimal (BCD), Error detection codes, Character codes 2. Coding versus binary conversion. Binary Codes

More information

Two s Complement Arithmetic

Two s Complement Arithmetic Two s Complement Arithmetic We now address the issue of representing integers as binary strings in a computer. There are four formats that have been used in the past; only one is of interest to us. The

More information

Number Systems and. Data Representation

Number Systems and. Data Representation Number Systems and Data Representation 1 Lecture Outline Number Systems Binary, Octal, Hexadecimal Representation of characters using codes Representation of Numbers Integer, Floating Point, Binary Coded

More information

Solution for Homework 2

Solution for Homework 2 Solution for Homework 2 Problem 1 a. What is the minimum number of bits that are required to uniquely represent the characters of English alphabet? (Consider upper case characters alone) The number of

More information

Computer Vision: Filtering

Computer Vision: Filtering Computer Vision: Filtering Raquel Urtasun TTI Chicago Jan 10, 2013 Raquel Urtasun (TTI-C) Computer Vision Jan 10, 2013 1 / 82 Today s lecture... Image formation Image Filtering Raquel Urtasun (TTI-C) Computer

More information

1. True or False? A natural number is the number 0 or any number obtained by adding 1 to a natural number.

1. True or False? A natural number is the number 0 or any number obtained by adding 1 to a natural number. CS Illuminated, 5 th ed. Chapter 2 Review Quiz 1. True or False? A natural number is the number 0 or any number obtained by adding 1 to a natural number. 2. True or False? The category of numbers called

More information

Building an Advanced Invariant Real-Time Human Tracking System

Building an Advanced Invariant Real-Time Human Tracking System UDC 004.41 Building an Advanced Invariant Real-Time Human Tracking System Fayez Idris 1, Mazen Abu_Zaher 2, Rashad J. Rasras 3, and Ibrahiem M. M. El Emary 4 1 School of Informatics and Computing, German-Jordanian

More information

2011, The McGraw-Hill Companies, Inc. Chapter 3

2011, The McGraw-Hill Companies, Inc. Chapter 3 Chapter 3 3.1 Decimal System The radix or base of a number system determines the total number of different symbols or digits used by that system. The decimal system has a base of 10 with the digits 0 through

More information

Adding and Subtracting Positive and Negative Numbers

Adding and Subtracting Positive and Negative Numbers Adding and Subtracting Positive and Negative Numbers Absolute Value For any real number, the distance from zero on the number line is the absolute value of the number. The absolute value of any real number

More information

Computer Organization and Architecture

Computer Organization and Architecture Computer Organization and Architecture Chapter 9 Computer Arithmetic Arithmetic & Logic Unit Performs arithmetic and logic operations on data everything that we think of as computing. Everything else in

More information

Detection of Bare PCB Defects by Image Subtraction Method using Machine Vision

Detection of Bare PCB Defects by Image Subtraction Method using Machine Vision , July 6-8, 2011, London, U.K. Detection of Bare PCB Defects by Image Subtraction Method using Machine Vision Ajay Pal Singh Chauhan, Sharat Chandra Bhardwaj Abstract A Printed Circuit Board (PCB) consists

More information

HOMEWORK # 2 SOLUTIO

HOMEWORK # 2 SOLUTIO HOMEWORK # 2 SOLUTIO Problem 1 (2 points) a. There are 313 characters in the Tamil language. If every character is to be encoded into a unique bit pattern, what is the minimum number of bits required to

More information

Design of the ALU Adder, Logic, and the Control Unit

Design of the ALU Adder, Logic, and the Control Unit Design of the ALU Adder, Logic, and the Control Unit This lecture will finish our look at the CPU and ALU of the computer. Remember: 1. The ALU performs the arithmetic and logic operations. 2. The control

More information

REAL TIME TRAFFIC LIGHT CONTROL USING IMAGE PROCESSING

REAL TIME TRAFFIC LIGHT CONTROL USING IMAGE PROCESSING REAL TIME TRAFFIC LIGHT CONTROL USING IMAGE PROCESSING Ms.PALLAVI CHOUDEKAR Ajay Kumar Garg Engineering College, Department of electrical and electronics Ms.SAYANTI BANERJEE Ajay Kumar Garg Engineering

More information

Number Representation

Number Representation Number Representation COMP375 Computer Organization and darchitecture t How do we represent data in a computer? At the lowest level, a computer is an electronic machine. works by controlling the flow of

More information

Two Ways to Count Cells with ImageJ

Two Ways to Count Cells with ImageJ Two Ways to Count Cells with ImageJ Figuring out how many cells are in an image is a common need in image analysis. There are several ways to go about this, some more involved than others. These instructions

More information

w w w. g e o s o f t. c o m

w w w. g e o s o f t. c o m SEG-Y Reader Convert SEG-Y files to: Windows BMP, Geosoft Grid, Geosoft Database, Geosoft Voxel USER GUIDE w w w. g e o s o f t. c o m SEG-Y Reader SEG-Y Reader reads 2D and 3D SEG-Y format data files

More information

3D Scanner using Line Laser. 1. Introduction. 2. Theory

3D Scanner using Line Laser. 1. Introduction. 2. Theory . Introduction 3D Scanner using Line Laser Di Lu Electrical, Computer, and Systems Engineering Rensselaer Polytechnic Institute The goal of 3D reconstruction is to recover the 3D properties of a geometric

More information

International Journal of Advancements in Technology ISSN Virtual Makeover Using MATLAB

International Journal of Advancements in Technology  ISSN Virtual Makeover Using MATLAB Virtual Makeover Using MATLAB Akash I. Mecwan akash.mecwan@nirmauni.ac.in Vijay G. Savani vijay.savani@nirmauni.ac.in Electronics and Communication Department, Institute of Technology, Nirma University

More information

Chapter II Binary Data Representation

Chapter II Binary Data Representation Chapter II Binary Data Representation The atomic unit of data in computer systems is the bit, which is actually an acronym that stands for BInary digit. It can hold only 2 values or states: 0 or 1, true

More information

Data Storage. Chapter 3. Objectives. 3-1 Data Types. Data Inside the Computer. After studying this chapter, students should be able to:

Data Storage. Chapter 3. Objectives. 3-1 Data Types. Data Inside the Computer. After studying this chapter, students should be able to: Chapter 3 Data Storage Objectives After studying this chapter, students should be able to: List five different data types used in a computer. Describe how integers are stored in a computer. Describe how

More information

Lesson 10: Video-Out Interface

Lesson 10: Video-Out Interface Lesson 10: Video-Out Interface 1. Introduction The Altera University Program provides a number of hardware controllers, called cores, to control the Video Graphics Array (VGA) Digital-to-Analog Converter

More information

IMAGE MODIFICATION DEVELOPMENT AND IMPLEMENTATION: A SOFTWARE MODELING USING MATLAB

IMAGE MODIFICATION DEVELOPMENT AND IMPLEMENTATION: A SOFTWARE MODELING USING MATLAB Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 4, Issue. 8, August 2015,

More information

Understanding Rasters By Joseph Collins-Unruh

Understanding Rasters By Joseph Collins-Unruh Understanding Rasters By Joseph Collins-Unruh - 1 - Table of Contents Introduction and Statement of Purpose...3 General Properties of Rasters...4 Height and Width...4 Data Type...4 Vertical Order...4 Endianness

More information

The Journey Inside SM : Digital Information Background Information, Part 1

The Journey Inside SM : Digital Information Background Information, Part 1 SM : Digital Information Background Information, Part 1 Language of Computers This unit examines the way in which information is stored in a computer. The data and information that is part of our day-to-day

More information

Chapter 1: Machine Vision Systems & Image Processing

Chapter 1: Machine Vision Systems & Image Processing Chapter 1: Machine Vision Systems & Image Processing 1.0 Introduction While other sensors, such as proximity, touch, and force sensing play a significant role in the improvement of intelligent systems,

More information

A4-R3: COMPUTER ORGANISATION

A4-R3: COMPUTER ORGANISATION A4-R3: COMPUTER ORGANISATION NOTE: 1. There are TWO PARTS in this Module/Paper. PART ONE contains FOUR questions and PART TWO contains FIVE questions. 2. PART ONE is to be answered in the TEAR-OFF ANSWER

More information

A Simple Feature Extraction Technique of a Pattern By Hopfield Network

A Simple Feature Extraction Technique of a Pattern By Hopfield Network A Simple Feature Extraction Technique of a Pattern By Hopfield Network A.Nag!, S. Biswas *, D. Sarkar *, P.P. Sarkar *, B. Gupta **! Academy of Technology, Hoogly - 722 *USIC, University of Kalyani, Kalyani

More information

MODULE 10- SHIFT REGISTERS,UARTS, USB & SERIAL DATA TRANSMISSION OVERVIEW:

MODULE 10- SHIFT REGISTERS,UARTS, USB & SERIAL DATA TRANSMISSION OVERVIEW: Introduction to Digital Electronics Module 10: Serail Data Transmission 1 MODULE 10- SHIFT REGISTERS,UARTS, USB & SERIAL DATA TRANSMISSION OVERVIEW: A shift register is a series of "D" Flip Flops with

More information

Printing with the Kiaro!

Printing with the Kiaro! June 14 Printing with the Kiaro! Third-Party Software An Astro-Med, Inc. Product Group 600 East Greenwich Ave. West Warwick, RI 02893 USA Toll-Free: 877-757-7978 (USA and Canada) Tel: +401-828-4000 www.quicklabel.com

More information

Implementation of OCR Based on Template Matching and Integrating it in Android Application

Implementation of OCR Based on Template Matching and Integrating it in Android Application International Journal of Computer Sciences and EngineeringOpen Access Technical Paper Volume-04, Issue-02 E-ISSN: 2347-2693 Implementation of OCR Based on Template Matching and Integrating it in Android

More information

Correcting the Lateral Response Artifact in Radiochromic Film Images from Flatbed Scanners

Correcting the Lateral Response Artifact in Radiochromic Film Images from Flatbed Scanners Correcting the Lateral Response Artifact in Radiochromic Film Images from Flatbed Scanners Background The lateral response artifact (LRA) in radiochromic film images from flatbed scanners was first pointed

More information

Binary Representation and Computer Arithmetic

Binary Representation and Computer Arithmetic Binary Representation and Computer Arithmetic The decimal system of counting and keeping track of items was first created by Hindu mathematicians in India in A.D. 4. Since it involved the use of fingers

More information

Graphics Specifications for Aluminum Cans

Graphics Specifications for Aluminum Cans Graphics Specifications for Aluminum Cans Artwork for aluminum can printing has a number of special requirements that must be addressed before it is ready for production. The Printing Surface The printing

More information

Bits and Bytes. Analog versus digital

Bits and Bytes. Analog versus digital Bits and Bytes Computers are used to store and process data. Processed data is called information. You are used to see or hear information processed with the help of a computer: a paper you ve just typed

More information

Model-based Chart Image Recognition

Model-based Chart Image Recognition Model-based Chart Image Recognition Weihua Huang, Chew Lim Tan and Wee Kheng Leow SOC, National University of Singapore, 3 Science Drive 2, Singapore 117543 E-mail: {huangwh,tancl, leowwk@comp.nus.edu.sg}

More information

QR Codes and Modern Marketing

QR Codes and Modern Marketing www.mediascopeinc.com pg. 1 MEDIASCOPE Right on Target! QR Codes and Modern Marketing An examination of the marketing potential, U.S. market adoption, and functionality of QR codes. Copyright Mediascope,

More information