Estimating Quality of Canola Seed Using a Flatbed Scanner.

Slides:



Advertisements
Similar presentations
Digital Image Processing
Advertisements

Photography and CS Philip Chan. Film vs Digital Camera What is the difference?
What makes an image memorable?
Introduction Radiostereometry (RSA) is well established research method for assessing movement of orthopaedic implants, bone fractures, and.
ICRA 2002 Topological Mobile Robot Localization Using Fast Vision Techniques Paul Blaer and Peter Allen Dept. of Computer Science, Columbia University.
UNIVERSITY OF FLORIDA DIGITAL COLLECTIONS PART I Selection, Scanning and Submittal of Government Documents.
CHAPTER 23: Two Categorical Variables: The Chi-Square Test
Image Processing IB Paper 8 – Part A Ognjen Arandjelović Ognjen Arandjelović
DIGITAL IMAGE PROCESSING CMSC 150: Lecture 14. Conventional Cameras  Entirely chemical and mechanical processes  Film: records a chemical record of.
P449. p450 Figure 15-1 p451 Figure 15-2 p453 Figure 15-2a p453.
Fundamentals of Digital Imaging
Digital Conversion of Single-Use Camera Ranging Lab Michael Spaeth SUC Tech Center Eastman Kodak Company.
Machine Vision Basics 1.What is machine vision? 2.Some examples of machine vision applications 3.What can be learned from biological vision? 4.The curse.
Digital Cameras CCD (Monochrome) RGB Color Filter Array.
Noise Reduction in Digital Images Lana Jobes Research Advisor: Dr. Jeff Pelz.
Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. Chapter 4 Image Slides.
Digital Images The nature and acquisition of a digital image.
Selecting the Right Color Palette: Understanding RGB and CMYK Color Presented by Pat McClure and Tony Kugler.
Analog and Digital Cameras  History of Digital cameras  Advantages and Disadvantages / Similarities and Differences of both types of cameras  Types.
SCCS 4761 Introduction What is Image Processing? Fundamental of Image Processing.
Identifying Computer Graphics Using HSV Model And Statistical Moments Of Characteristic Functions Xiao Cai, Yuewen Wang.
4/14: Scanners Roll Call Response to request Lecture: scanners –general –flatbed –sheet-fed –hand-held –OCR Image courtesy of Microtek
Fundamentals of Photoshop
Capture your favorite image Done by: ms.Hanan Albarigi.
Copyright © Texas Education Agency, All rights reserved.1 Introduction to Scanners Principles of Information Technology.
Computer Graphics Using “ Adobe Photoshop ” Introduction to E-Learning Center, DAD presents Workshop on Instructor: Mazhar.
Color. -Visual light -An integral part of the sculpture -Creates desired effect -Distinguish items -Strengthen interest.
Image Processing & Perception Sec 9-11 Web Design.
How to Choose Frame Grabber …that’s right for your application Coreco Imaging.
Computational and Biological Vision “Colors Out Of Space” Digital color representation, color spaces and more! Amir Eluk Software Engineering.
Digitization: MSU Project Example and Funding Information Paul Martinez Cataloging Librarian/Archivist Montclair State University.
1 After completing this lesson, you will be able to: Identify the key differences between analog and digital technologies. Define digital camera terms,
Digital Image Fundamentals. What Makes a good image? Cameras (resolution, focus, aperture), Distance from object (field of view), Illumination (intensity.
1 Chapter 1: Introduction 1.1 Images and Pictures Human have evolved very precise visual skills: We can identify a face in an instant We can differentiate.
EDGE DETECTION USING MINMAX MEASURES SOUNDARARAJAN EZEKIEL Matthew Lang Department of Computer Science Indiana University of Pennsylvania Indiana, PA.
BARCODE IDENTIFICATION BY USING WAVELET BASED ENERGY Soundararajan Ezekiel, Gary Greenwood, David Pazzaglia Computer Science Department Indiana University.
Image Representation. Digital Cameras Scanned Film & Photographs Digitized TV Signals Computer Graphics Radar & Sonar Medical Imaging Devices (X-Ray,
In Defense of Nearest-Neighbor Based Image Classification Oren Boiman The Weizmann Institute of Science Rehovot, ISRAEL Eli Shechtman Adobe Systems Inc.
Chapter 2: Digital Image Fundamentals Spring 2006, 劉震昌.
Digital Image Processing Part 1 Introduction. The eye.
Presented By: ROLL No IMTIAZ HUSSAIN048 M.EHSAN ULLAH012 MUHAMMAD IDREES027 HAFIZ ABU BAKKAR096(06)
Digital Image Processing NET 404) ) Introduction and Overview
Computer Vision Introduction to Digital Images.
United States Department of Agriculture Grain Inspection Advisory Committee Meeting, June 2013 Review of Rice Initiatives 1 Investigate use of NIRT to.
CS654: Digital Image Analysis Lecture 30: Color Model Conversion.
Development of Vegetation Indices as Economic Thresholds for Control of Defoliating Insects of Soybean James BoardVijay MakaRandy PriceDina KnightMatthew.
Digital Imaging, Photography, Videography. Photography Writing with light.
Intelligent Vision Systems Image Geometry and Acquisition ENT 496 Ms. HEMA C.R. Lecture 2.
ISAN-DSP GROUP Digital Image Fundamentals ISAN-DSP GROUP What is Digital Image Processing ? Processing of a multidimensional pictures by a digital computer.
TOPIC 4 INTRODUCTION TO MEDIA COMPUTATION: DIGITAL PICTURES Notes adapted from Introduction to Computing and Programming with Java: A Multimedia Approach.
HOW SCANNERS WORK A scanner is a device that uses a light source to electronically convert an image into binary data (0s and 1s). This binary data can.
© UNT in partnership with TEA1 Introduction to Scanners Principles of Information Technology.
Scanner Scanner Introduction: Scanner is an input device. It reads the graphical images or line art or text from the source and converts.
An Introduction to Digital Image Processing Dr.Amnach Khawne Department of Computer Engineering, KMITL.
Date of download: 6/23/2016 Copyright © 2016 SPIE. All rights reserved. Five sampling types with P=8, R=1: (a) X=0, (b) X=1, (c) X=2, (d) X=3, (e) X=4.
Scanner.
Red Green Blue (RGB) Colour conversions Y and RGB Link In the images, the lighter the colour intensity (Red, Green, Blue), the more the contribution.
1 2 Mau forest Estate Tea Cloud & shadow South Nandi Forest Reserve
Introduction to Scanners
APPLYING THE FUNCTION PERIPHERAL AND INSTALLATION PC
Chapter I, Digital Imaging Fundamentals: Lesson II Capture
Scanners.
Computer Graphics Using “Adobe Photoshop”
How to Digitize the Natural Color
Day 56 Identifying Outliers
Volume 107, Issue 6, Pages (September 2014)
Basic Concepts of Digital Imaging
© 2010 Cengage Learning Engineering. All Rights Reserved.
Cholesterol-Rich Plasma Membrane Domains (Lipid Rafts) in Keratinocytes: Importance in the Baseline and UVA-Induced Generation of Reactive Oxygen Species 
Introduction to Colour Management
Presentation transcript:

Estimating Quality of Canola Seed Using a Flatbed Scanner

Introduction Grading of Canola: – Visual inspection – Follows US standard guidelines Machine Vision techniques using CCD cameras and flat bed scanner have been used to grade, size and classify rice, wheat, pulses, soybeans and lentils. These techniques have not been so far applied to grade canola

Objective Grading canola into samples with less than 2% foreign material (pure sample) and samples with more than 2% foreign materials (impure sample) using flat bed scanners

Material and Methods Canola Samples: 0%,2%,5%,10%,20%,40% and 60% foreign material. Five sub samples of 45gm from each sample were used for further testing Image Acquisition: Color image flat bed scanner (CanoScan 8400, Canon USA Inc., Lake Success, NY).Each sample was scanned at 150 dpi Color Calibration: Kodak gray cards (Catalog No. E , Eastman Kodak Company, 1999) Data Acquisition: Mean values, that is the average intensity values, of the red (R), green (G) and blue (B) domains were recorded using Adobe Photoshop Elements 2.0 image editing software

Figure 1 Red Histogram data Figure 2 Green Histogram data Figure 3 Blue Histogram data

Figure 4 Canonical plot obtained from discriminant analysis using RGB domain Table 1 Classification Table for different canola samples* classified using discriminant analysis Percent Impurities 0%2%5%10%20%40%60% 0% % % % % % % * Number of samples for each type = 5

Figure 5 Canonical plot obtained from discriminant analysis using only R-G domain

(a) (c) (b) (d) Figure 6 Images 2% (a), 5% (b), 10 % (c), and 20% (d) samples

(a) (b) Figure 7 Images 40% (a) and 60% (b) samples

Conclusions Histogram Analysis: R and G domains were able to distinguish between pure and impure samples better than the B domain Discriminant analysis: Categorized the samples broadly speaking into three different groups – Samples with 0% foreign material were significantly different – Samples with 2%, 5%, 10%, 20% foreign material – Samples with 40% and 60% foreign material Visual analysis: Justified the results obtained