Gray-World assumption on perceptual color spaces. Universidad de Guanajuato División de Ingenierías Campus Irapuato-Salamanca
|
|
- Mary Nicholson
- 5 years ago
- Views:
Transcription
1 Gray-World assumption on perceptual color spaces Jonathan Cepeda-Negrete Raul E. Sanchez-Yanez Universidad de Guanajuato División de Ingenierías Campus Irapuato-Salamanca
2 Outline 1. Introduction Color constancy Related work Our proposal 2. Methodology Gray-World assumption Our approaches 3. Experimental Results Benchmark used Metric for the evaluation Results 4. Conclusions 2
3 Section INTRODUCTION
4 Introduction Color Constancy The ability of a system to recognize the correct colors, independently of the color source present in a scene is known as Color Constancy [1]. Figure 1. Result of a color constancy algorithm upon an image. 4 [1] S. Zeki, A vision of the brain, J. Wiley and sons, Eds. Wiley-Blackwell, January 15, (1993).
5 Introduction Related Work Most color constancy algorithms have been proposed and implemented in the RGB color space, and, in spite of the existence of a considerable number of methods, there is not a general solution for the color constancy problem. Gray-World assumption. White-Patch Shades of Gray Gray-Edges...etc. Among the few research works addressing on the estimation of the illuminant on perceptual color spaces we can mention the study by Kloss [2], where the illuminant was estimated using WP and GW algorithms on CIELAB. 5 [2] Kloss GK. Colour Constancy using von Kries Transformations Colour Constancy goes to the Lab. Res Lett Inf Math Sci.; 13: pp (2009).
6 Introduction Our Proposal In this study, we propose the estimation of the illuminant directly on a perceptual color space. Thus, we focus on solving the color constancy problem. The GW assumption is analyzed in two perceptual color spaces. Specifically, the well-known CIE 1976 L a b (CIELAB) and the CIE 1976 L u v (CIELUV) in order to provide a simple and fast transformation. The standard GW approach on the RGB color space, and the GE algorithm [3] are included for reference purposes. 6 [3] van de Weijer J, Gevers T, Gijsenij A. Edge-Based Color Constancy. IEEE Trans Image Process; 16(9): pp (2007).
7 Section METHODOLOGY
8 Methodology Methodology The outcomes of GW approach in two perceptual color spaces are compared with those obtained using the standard GW, as depicted in the figure. Gray-World algorithm RGB CIELab Figure 2. Methodology for the experimental tests. 8 CIELuv
9 Methodology Image Transformation The algorithm considered throughout this work assumes that the illumination is uniform across the scene. Equation (1) gives the relationship for the color intensity, f ( x, y) = G( x, y) R ( x, y) I i i i (1) The outcome image is given by o i (x, y) = f i(x, y) I i = G(x, y)r i (x, y) (2) 9
10 Methodology Gray-World Algorithm The Gray World assumption (GW) is the most popular algorithm for color constancy. Proposed by Buchsbaum [4], it is used as reference for other algorithms. The GW is based on the assumption that, on average, the real world tends to gray, and estimates the illuminant using the average color of all pixels. a i = 1 MN M 1 N 1 x=0 y=0 f i (x, y) (3) o ( x, y) = i fi ( x, y) 2a i (4) 10 [4] G. Buchsbaum. A spatial processor model for object colour perception. Journal of The Franklin Institute-engineering and Applied Mathematics, 310:1 26, 1980.
11 Methodology Gray-World Assumption on Perceptual Color Spaces We propose to apply this assumption on two perceptual color spaces, and perform a comparison. Perceptual color spaces are conformed by two chromatic components, additionally to lightness. These two chromatic components are used in the estimation of the illuminant. CIELAB I a * = 1 MN M 1 N 1 x=0 y=0 a * (x, y) I b * = 1 MN M 1 N 1 x=0 y=0 b * (x, y) (5,6) I L * = max{ L * (x, y) } (7) 11
12 Section EXPERIMENTAL RESULTS
13 Experimental Results SFU Gray Ball F. Ciurea and B. Funt [5] 11,346 images 13 Bianco et al.[6] 1,135 images [5] F. Ciurea and B. Funt, A large image database for color constancy research, in Proceedings of the Imaging Science and Technology Eleventh Color Imaging Conference, Scottsdale, pp , Nov. (2003). [6] S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, Improving color constancy using indoor-outdoor image classification, IEEE Trans. Image Process., vol. 17, no. 12, pp , (2008).
14 Experimental Results Angular Error Metric Hordley and Finlayson [7] proposed a metric well suited for the evaluation of the color constancy, the angular error. # e ang = cos 1 % $ I r I e I r I e & ( ' (8) 14 [7] S. D. Hordley and G. D. Finlayson. Re-evaluating colour constancy algorithms. In Proceedings of the Pattern Recognition, 17th International Conference on (ICPR 04), pages 76 79, (2004).
15 Experimental Results Results 10 9 RGB CIELab CIELuv 8 Angular error Image Index 15 Figure 3. Curve fitting for each approach showing the trend of the angular errors given by the outcomes. The lower the angular error is, the better the estimation is.
16 Experimental Results Results Original Ideal 0.0º 0.0º 0.0º 16 RGB CIELab CIELuv 8.0º 11.2º 3.5º 3.9º 4.2º 4.7º a) b) c) 12.4º 7.6º 3.3º Figure 4. Three examples out of the 1135 images where the angular error is shown in the gray ball. The ideal image is included. a) ApacheTrial frame no , b) CIC2002 frame no , c) DeerLake frame no
17 Experimental Results Results (II) According to the results, the application of GW on any perceptual color space is significantly better than RGB. However, the GW algorithm applied on CIELUV is marginally better than the algorithm on CIELAB. The Gray-Edge (GE) algorithm is included in the experiments for comparison purposes. The difference in performance between GW using a perceptual space and GE is very small. However, the main difference is that the GW algorithm in any color space does not require any tuning process. TABLE I: Statistical angular errors (degrades) for the different approaches. 17 Algorithm Median Mean Max Gray-World (RGB) Gray-World (CIELAB) Gray-World (CIELUV) nd order Gray-Edge st order Gray-Edge
18 Experimental Results Results (II) According to the results, the application of GW on any perceptual color space is significantly better than RGB. However, the GW algorithm applied on CIELUV is marginally better than the algorithm on CIELAB. The Gray-Edge (GE) algorithm is included in the experiments for comparison purposes. The difference in performance between GW using a perceptual space and GE is very small. However, the main difference is that the GW algorithm in any color space does not require any tuning process. TABLE I: Statistical angular errors (degrades) for the different approaches. 18 Algorithm Median Mean Max Gray-World (RGB) Gray-World (CIELAB) Gray-World (CIELUV) nd order Gray-Edge st order Gray-Edge
19 Experimental Results Results (III) The processing time was measured for each algorithm and compared. We can appreciate that, the difference between GE and GW approaches is significant. The GW assumption, applied in any color space, takes a considerable smaller amount of time than the GE approach. TABLE II: Computing time for each approach. Algorithm Time (ms) Gray-World (RGB) 0.57 Gray-World (CIELUV) Gray-World (CIELAB) st order Gray-Edge nd order Gray-Edge
20 Section CONCLUSIONS
21 Conclusions Conclusions and Remarks A variation of the method using the GW assumption for color constancy has been analyzed in the CIELAB and CIELUV color spaces. According to the results, we conclude that outcomes from our approaches, a GW assumption in a perceptual color space, are better than those obtained using the standard procedure in RGB. Despite that the outcomes using the GE algorithm are slightly better than those using our approach, for practical applications we can choose the latter, because it is significantly faster and does not require a tuning process. 21 Also, we can appreciate that GW on CIELUV is marginally better than on CIELAB according to the accuracy of the estimated illuminant. Moreover, the processing time is considerably faster on CIELUV.
22 Gray-World assumption on perceptual color spaces Jonathan Cepeda-Negrete Raul E. Sanchez-Yanez Thanks for your attention!
A Curious Problem with Using the Colour Checker Dataset for Illuminant Estimation
A Curious Problem with Using the Colour Checker Dataset for Illuminant Estimation Graham D. Finlayson 1, Ghalia Hemrit 1, Arjan Gijsenij 2, Peter Gehler 3 1 School of Computing Sciences, University of
More informationVideo-Based Illumination Estimation
Video-Based Illumination Estimation Ning Wang 1,2, Brian Funt 2, Congyan Lang 1, and De Xu 1 1 School of Computer Science and Infromation Technology, Beijing Jiaotong University, Beijing, China 2 School
More informationLight, Color, and Surface Reflectance. Shida Beigpour
Light, Color, and Surface Reflectance Shida Beigpour Overview Introduction Multi-illuminant Intrinsic Image Estimation Multi-illuminant Scene Datasets Multi-illuminant Color Constancy Conclusions 2 Introduction
More informationCOLOR FIDELITY OF CHROMATIC DISTRIBUTIONS BY TRIAD ILLUMINANT COMPARISON. Marcel P. Lucassen, Theo Gevers, Arjan Gijsenij
COLOR FIDELITY OF CHROMATIC DISTRIBUTIONS BY TRIAD ILLUMINANT COMPARISON Marcel P. Lucassen, Theo Gevers, Arjan Gijsenij Intelligent Systems Lab Amsterdam, University of Amsterdam ABSTRACT Performance
More informationHOW USEFUL ARE COLOUR INVARIANTS FOR IMAGE RETRIEVAL?
HOW USEFUL ARE COLOUR INVARIANTS FOR IMAGE RETRIEVAL? Gerald Schaefer School of Computing and Technology Nottingham Trent University Nottingham, U.K. Gerald.Schaefer@ntu.ac.uk Abstract Keywords: The images
More informationIntroduction to color science
Introduction to color science Trichromacy Spectral matching functions CIE XYZ color system xy-chromaticity diagram Color gamut Color temperature Color balancing algorithms Digital Image Processing: Bernd
More informationGeneralized Gamut Mapping using Image Derivative Structures for Color Constancy
DOI 10.1007/s11263-008-0171-3 Generalized Gamut Mapping using Image Derivative Structures for Color Constancy Arjan Gijsenij Theo Gevers Joost van de Weijer Received: 11 February 2008 / Accepted: 6 August
More informationSpectral Images and the Retinex Model
Spectral Images and the Retine Model Anahit Pogosova 1, Tuija Jetsu 1, Ville Heikkinen 2, Markku Hauta-Kasari 1, Timo Jääskeläinen 2 and Jussi Parkkinen 1 1 Department of Computer Science and Statistics,
More informationAn Algorithm to Determine the Chromaticity Under Non-uniform Illuminant
An Algorithm to Determine the Chromaticity Under Non-uniform Illuminant Sivalogeswaran Ratnasingam and Steve Collins Department of Engineering Science, University of Oxford, OX1 3PJ, Oxford, United Kingdom
More informationColor Constancy by Derivative-based Gamut Mapping
Color Constancy by Derivative-based Gamut Mapping Arjan Gijsenij, Theo Gevers, Joost Van de Weijer To cite this version: Arjan Gijsenij, Theo Gevers, Joost Van de Weijer. Color Constancy by Derivative-based
More informationColour balancing using sclera colour
IET Image Processing Research Article Colour balancing using sclera colour ISSN 1751-9659 Received on 21st February 217 Revised 1st October 217 Accepted on 29th October 217 E-First on 2th December 217
More information[2006] IEEE. Reprinted, with permission, from [Wenjing Jia, Huaifeng Zhang, Xiangjian He, and Qiang Wu, A Comparison on Histogram Based Image
[6] IEEE. Reprinted, with permission, from [Wenjing Jia, Huaifeng Zhang, Xiangjian He, and Qiang Wu, A Comparison on Histogram Based Image Matching Methods, Video and Signal Based Surveillance, 6. AVSS
More informationColor Constancy for Multiple Light Sources Arjan Gijsenij, Member, IEEE, Rui Lu, and Theo Gevers, Member, IEEE
IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 2, FEBRUARY 2012 697 Color Constancy for Multiple Light Sources Arjan Gijsenij, Member, IEEE, Rui Lu, and Theo Gevers, Member, IEEE Abstract Color constancy
More informationCombining Strategies for White Balance
Combining Strategies for White Balance Simone Bianco, Francesca Gasparini and Raimondo Schettini DISCo, Università degli Studi di Milano-Bicocca, Via Bicocca degli Arcimboldi 8, 20126 Milano, Italy ABSTRACT
More informationDeep Structured-Output Regression Learning for Computational Color Constancy
Deep Structured-Output Regression Learning for Computational Color Constancy Yanlin Qian, Ke Chen, Joni-Kristian Kämäräinen Department of Signal Processing Tampere University of Technology http://vision.cs.tut.fi/
More informationColor Constancy from Illumination Changes
(MIRU2004) 2004 7 153-8505 4-6-1 E E-mail: {rei,robby,ki}@cvl.iis.u-tokyo.ac.jp Finlayson [10] Color Constancy from Illumination Changes Rei KAWAKAMI,RobbyT.TAN, and Katsushi IKEUCHI Institute of Industrial
More informationPublished at the IEEE Workshop on Color and Photometry in Computer Vision 2011, in conjunction with ICCV 2011 in Barcelona, Spain.
Copyright 2011 IEEE. Published at the IEEE Workshop on Color and Photometry in Computer Vision 2011, in conjunction with ICCV 2011 in Barcelona, Spain. Personal use of this material is permitted. However,
More informationarxiv: v1 [cs.cv] 7 Dec 2018
Color Constancy by GANs: An Experimental Survey Partha Das1,2 Anil S. Baslamisli1 Yang Liu1,2 Sezer Karaoglu1,2 Theo Gevers1,2 1 2 Computer Vision Lab, University of Amsterdam 3DUniversum arxiv:1812.03085v1
More informationUvA-DARE (Digital Academic Repository) Edge-driven color constancy Gijsenij, A. Link to publication
UvA-DARE (Digital Academic Repository) Edge-driven color constancy Gijsenij, A. Link to publication Citation for published version (APA): Gijsenij, A. (2010). Edge-driven color constancy General rights
More informationA Statistical Approach to Culture Colors Distribution in Video Sensors Angela D Angelo, Jean-Luc Dugelay
A Statistical Approach to Culture Colors Distribution in Video Sensors Angela D Angelo, Jean-Luc Dugelay VPQM 2010, Scottsdale, Arizona, U.S.A, January 13-15 Outline Introduction Proposed approach Colors
More informationTexture Image Segmentation using FCM
Proceedings of 2012 4th International Conference on Machine Learning and Computing IPCSIT vol. 25 (2012) (2012) IACSIT Press, Singapore Texture Image Segmentation using FCM Kanchan S. Deshmukh + M.G.M
More informationDiagonal versus affine transformations for color correction
2108 J. Opt. oc. Am. A/ Vol. 17, No. 11/ November 2000 JOA Communications Diagonal versus affine transformations for color correction Brian V. Funt and Benjamin C. ewis chool of Computing cience, imon
More informationRecurrent Color Constancy
Recurrent Color Constancy Yanlin Qian1, Ke Chen1, Jarno Nikkanen2, Joni-Kristian K am ar ainen1, Jiri Matas1,3 1 Laboratory of Signal Processing, Tampere University of Technology 2 Intel Finland 3 Center
More informationColor can be an important cue for computer vision or
IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 20, NO. 9, SEPTEMBER 2011 2475 Computational Color Constancy: Survey and Experiments Arjan Gijsenij, Member, IEEE, Theo Gevers, Member, IEEE, and Joost van de
More informationComputational Color Constancy: Survey and Experiments
IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. X, NO. X, MONTH 2010 1 Computational Color Constancy: Survey and Experiments Arjan Gijsenij, Member, IEEE, Theo Gevers, Member, IEEE, Joost van de Weijer, Member,
More informationReproduction Angular Error: An Improved Performance Metric for Illuminant Estimation
FINLAYSON, ZAKIZADEH: REPRODUCTION ANGULAR ERROR 1 Reproduction Angular Error: An Improved Performance Metric for Illuminant Estimation Graham D. Finlayson g.finlayson@uea.ac.uk Roshanak Zakizadeh r.zakizadeh@uea.ac.uk
More informationIllumination Estimation Using a Multilinear Constraint on Dichromatic Planes
Illumination Estimation Using a Multilinear Constraint on Dichromatic Planes Javier Toro 1 and Brian Funt 2 LBMU 1, Centre Hospitalier de l Université de Montréal Montréal, QC, Canada H2L 2W5 School of
More information[2006] IEEE. Reprinted, with permission, from [Wenjing Jia, Gaussian Weighted Histogram Intersection for License Plate Classification, Pattern
[6] IEEE. Reprinted, with permission, from [Wening Jia, Gaussian Weighted Histogram Intersection for License Plate Classification, Pattern Recognition, 6. ICPR 6. 8th International Conference on (Volume:3
More informationIlluminant Estimation from Projections on the Planckian Locus
Illuminant Estimation from Projections on the Planckian Locus Baptiste Mazin, Julie Delon, and Yann Gousseau LTCI, Télécom-ParisTech, CNRS, 46 rue Barault, Paris 75013, France {baptiste.mazin,julie.delon,yann.gousseau}@telecom-paristech.fr
More informationLight source separation from image sequences of oscillating lights
2014 IEEE 28-th Convention of Electrical and Electronics Engineers in Israel Light source separation from image sequences of oscillating lights Amir Kolaman, Rami Hagege and Hugo Guterman Electrical and
More informationSupplementary Material: Specular Highlight Removal in Facial Images
Supplementary Material: Specular Highlight Removal in Facial Images Chen Li 1 Stephen Lin 2 Kun Zhou 1 Katsushi Ikeuchi 2 1 State Key Lab of CAD&CG, Zhejiang University 2 Microsoft Research 1. Computation
More informationMANY computer vision applications as well as image
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL., NO., MONTH 2013 1 Exemplar-Based Colour Constancy and Multiple Illumination Hamid Reza Vaezi Joze and Mark S. Drew Abstract Exemplar-based
More informationColour computer vision: fundamentals, applications and challenges. Dr. Ignacio Molina-Conde Depto. Tecnología Electrónica Univ.
Colour computer vision: fundamentals, applications and challenges Dr. Ignacio Molina-Conde Depto. Tecnología Electrónica Univ. of Málaga (Spain) Outline Part 1: colorimetry and colour perception: What
More informationEstimating the wavelength composition of scene illumination from image data is an
Chapter 3 The Principle and Improvement for AWB in DSC 3.1 Introduction Estimating the wavelength composition of scene illumination from image data is an important topics in color engineering. Solutions
More informationColor Image Segmentation
Color Image Segmentation Yining Deng, B. S. Manjunath and Hyundoo Shin* Department of Electrical and Computer Engineering University of California, Santa Barbara, CA 93106-9560 *Samsung Electronics Inc.
More informationOn Color Image Quantization by the K-Means Algorithm
On Color Image Quantization by the K-Means Algorithm Henryk Palus Institute of Automatic Control, Silesian University of Technology, Akademicka 16, 44-100 GLIWICE Poland, hpalus@polsl.gliwice.pl Abstract.
More informationExtended Corrected-Moments Illumination Estimation
Extended Corrected-Moments Illumination Estimation Xiaochuan Chen 1, Mark S. Drew 1, Ze-Nian Li 1, and Graham D. Finlayson 2 ; 1 Simon Fraser University, {xca64,mark,li}@cs.sfu.ca, 2 University of East
More informationAn ICA based Approach for Complex Color Scene Text Binarization
An ICA based Approach for Complex Color Scene Text Binarization Siddharth Kherada IIIT-Hyderabad, India siddharth.kherada@research.iiit.ac.in Anoop M. Namboodiri IIIT-Hyderabad, India anoop@iiit.ac.in
More informationdependent intensity function - the spectral distribution function (SPD) E( ). The surface reflectance is the proportion of incident light which is ref
Object-Based Illumination Classification H. Z. Hel-Or B. A. Wandell Dept. of Computer Science Haifa University Haifa 395, Israel Dept. Of Psychology Stanford University Stanford, CA 9435, USA Abstract
More informationExtensions of One-Dimensional Gray-level Nonlinear Image Processing Filters to Three-Dimensional Color Space
Extensions of One-Dimensional Gray-level Nonlinear Image Processing Filters to Three-Dimensional Color Space Orlando HERNANDEZ and Richard KNOWLES Department Electrical and Computer Engineering, The College
More informationA Comparison of Computational Color Constancy Algorithms; Part Two: Experiments with Image Data
This work has been accepted for publication in IEEE Transactions in Image Processing. 1 (See http://www.ieee.org/about/documentation/copyright/policies.htm for copyright issue details). A Comparison of
More informationCHANGES in illumination cause the measurements
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. XX, NO. Y, DATE Improving Color Constancy by Photometric Edge Weighting Arjan Gijsenij, Member, IEEE, Theo Gevers, Member, IEEE, Joost
More informationColour rendering open questions and possible solutions
Colour rendering open questions and possible solutions J Schanda Virtual Environments and Imaging Technologies Laboratory University of Pannonia, Hungary Overview CIE Test sample method Possible expansions
More informationA Novel Approach for Shadow Removal Based on Intensity Surface Approximation
A Novel Approach for Shadow Removal Based on Intensity Surface Approximation Eli Arbel THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE MASTER DEGREE University of Haifa Faculty of Social
More informationColor space transformations for digital photography exploiting information about the illuminant estimation process
374 J. Opt. Soc. Am. A / Vol. 29, No. 3 / March 2012 Bianco et al. Color space transformations for digital photography exploiting information about the illuminant estimation process Simone Bianco, 1, *
More informationSimultaneous surface texture classification and illumination tilt angle prediction
Simultaneous surface texture classification and illumination tilt angle prediction X. Lladó, A. Oliver, M. Petrou, J. Freixenet, and J. Martí Computer Vision and Robotics Group - IIiA. University of Girona
More informationTHE human visual system perceives the color of an
3612 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 8, AUGUST 2012 Chromaticity Space for Illuminant Invariant Recognition Sivalogeswaran Ratnasingam, Member, IEEE, and T. Martin McGinnity, Member,
More informationDetecting Digital Image Forgeries By Multi-illuminant Estimators
Research Paper Volume 2 Issue 8 April 2015 International Journal of Informative & Futuristic Research ISSN (Online): 2347-1697 Detecting Digital Image Forgeries By Multi-illuminant Estimators Paper ID
More informationColor Correction between Gray World and White Patch
Color Correction between Gray World and White Patch Alessandro Rizzi, Carlo Gatta, Daniele Marini a Dept. of Information Technology - University of Milano Via Bramante, 65-26013 Crema (CR) - Italy - E-mail:
More informationAutomatic Multi-light White Balance Using Illumination Gradients and Color Space Projection
Automatic Multi-light White Balance Using Illumination Gradients and Color Space Projection Clifford Lindsay and Emmanuel Agu Worcester Polytechnic Institute, Worcester, MA Abstract. White balance algorithms
More informationIllumination and Reflectance
COMP 546 Lecture 12 Illumination and Reflectance Tues. Feb. 20, 2018 1 Illumination and Reflectance Shading Brightness versus Lightness Color constancy Shading on a sunny day N(x) L N L Lambert s (cosine)
More informationColor Vision. Spectral Distributions Various Light Sources
Color Vision Light enters the eye Absorbed by cones Transmitted to brain Interpreted to perceive color Foundations of Vision Brian Wandell Spectral Distributions Various Light Sources Cones and Rods Cones:
More informationA Comparison of Color Models for Color Face Segmentation
Available online at www.sciencedirect.com Procedia Technology 7 ( 2013 ) 134 141 A Comparison of Color Models for Color Face Segmentation Manuel C. Sanchez-Cuevas, Ruth M. Aguilar-Ponce, J. Luis Tecpanecatl-Xihuitl
More informationSpectral Adaptation. Chromatic Adaptation
Spectral Adaptation Mark D. Fairchild RIT Munsell Color Science Laboratory IS&T/SID 14th Color Imaging Conference Scottsdale 2006 Chromatic Adaptation Spectra-to-XYZ-to-LMS Chromatic adaptation models
More informationLocal Linear Models for Improved von Kries Adaptation
Appeared in Proc. of 10th Colour Imaging Conf., Scottsdale AZ, 2002 1 Local Linear Models for Improved von Kries Adaptation G. D. Finlayson, A. Alsam, and S. D. Hordley School of Information Systems University
More informationAnalysis of colour constancy algorithms using the knowledge of variation of correlated colour temperature of daylight with solar elevation
Ratnasingam et al. EURASIP Journal on Image and Video Processing 213, 213:14 RESEARCH Open Access Analysis of colour constancy algorithms using the knowledge of variation of correlated colour temperature
More informationIntelligent Method for Dipstick Urinalysis Using Smartphone Camera
Intelligent Method for Dipstick Urinalysis Using Smartphone Camera R.V. Hari Ginardi, Ahmad Saikhu, Riyanarto Sarno, Dwi Sunaryono, Ali Sofyan Kholimi, and Ratna Nur Tiara Shanty Department of Informatics,
More informationInvestigation of Color Constancy for Ubiquitous Wireless LAN/Camera
Investigation of Color Constancy for Ubiquitous Wireless LAN/Camera Positioning: An Initial Outcome 1 Wan Mohd Yaakob Wan Bejuri, 2 Mohd Murtadha Mohamad, 3 Maimunah Sapri, 4 Mohd Adly Rosly 1, 2,4 Faculty
More informationEffective Features of Remote Sensing Image Classification Using Interactive Adaptive Thresholding Method
Effective Features of Remote Sensing Image Classification Using Interactive Adaptive Thresholding Method T. Balaji 1, M. Sumathi 2 1 Assistant Professor, Dept. of Computer Science, Govt. Arts College,
More informationColor-Based Classification of Natural Rock Images Using Classifier Combinations
Color-Based Classification of Natural Rock Images Using Classifier Combinations Leena Lepistö, Iivari Kunttu, and Ari Visa Tampere University of Technology, Institute of Signal Processing, P.O. Box 553,
More informationCS231A Course Project Final Report Sign Language Recognition with Unsupervised Feature Learning
CS231A Course Project Final Report Sign Language Recognition with Unsupervised Feature Learning Justin Chen Stanford University justinkchen@stanford.edu Abstract This paper focuses on experimenting with
More informationLecture 1 Image Formation.
Lecture 1 Image Formation peimt@bit.edu.cn 1 Part 3 Color 2 Color v The light coming out of sources or reflected from surfaces has more or less energy at different wavelengths v The visual system responds
More informationarxiv: v1 [cs.cv] 2 May 2016
16-811 Math Fundamentals for Robotics Comparison of Optimization Methods in Optical Flow Estimation Final Report, Fall 2015 arxiv:1605.00572v1 [cs.cv] 2 May 2016 Contents Noranart Vesdapunt Master of Computer
More informationA GENERIC FACE REPRESENTATION APPROACH FOR LOCAL APPEARANCE BASED FACE VERIFICATION
A GENERIC FACE REPRESENTATION APPROACH FOR LOCAL APPEARANCE BASED FACE VERIFICATION Hazim Kemal Ekenel, Rainer Stiefelhagen Interactive Systems Labs, Universität Karlsruhe (TH) 76131 Karlsruhe, Germany
More informationFruit Maturity Estimation based on Color Scales
Fruit Maturity Estimation based on Color Scales Marco Mora, Miguel Oyarce, and Claudio Fredes Laboratory of Technological Research in Pattern Recognition Universidad Catolica del Maule Chile {mora@spock.ucm.cl,moyarce@litrp.cl}
More informationRemoving Shadows from Images
Removing Shadows from Images Zeinab Sadeghipour Kermani School of Computing Science Simon Fraser University Burnaby, BC, V5A 1S6 Mark S. Drew School of Computing Science Simon Fraser University Burnaby,
More informationDigital Image Forgery detection using color Illumination and Decision Tree Classification
RESEARCH ARTICLE OPEN ACCESS Digital Image Forgery detection using color Illumination and Decision Tree Classification Chitra Ganesan, V.R. Bhuma Chitra Ganesan, the author is currently pursuing M.E (Software
More informationLecture 24: More on Reflectance CAP 5415
Lecture 24: More on Reflectance CAP 5415 Recovering Shape We ve talked about photometric stereo, where we assumed that a surface was diffuse Could calculate surface normals and albedo What if the surface
More informationSupervised Object Class Colour Normalisation
Supervised Object Class Colour Normalisation Ekaterina Riabchenko 1, Jukka Lankinen 1, Anders Glent Buch 2, Joni-Kristian Kämäräinen 3, and Norbert Krüger 2 1 Lappeenranta University of Technology (Kouvola
More informationColor Space Projection, Feature Fusion and Concurrent Neural Modules for Biometric Image Recognition
Proceedings of the 5th WSEAS Int. Conf. on COMPUTATIONAL INTELLIGENCE, MAN-MACHINE SYSTEMS AND CYBERNETICS, Venice, Italy, November 20-22, 2006 286 Color Space Projection, Fusion and Concurrent Neural
More informationColor and Color Constancy in a Translation Model for Object Recognition
Color and Color Constancy in a Translation Model for Object Recognition Kobus Barnard 1 and Prasad Gabbur 2 1 Department of Computer Science, University of Arizona Email: kobus@cs.arizona.edu 2 Department
More informationImprovements to Gamut Mapping Colour Constancy Algorithms
Improvements to Gamut Mapping Colour Constancy Algorithms Kobus Barnard Department of Computing Science, Simon Fraser University, 888 University Drive, Burnaby, BC, Canada, V5A 1S6 email: kobus@cs.sfu.ca
More informationthen assume that we are given the image of one of these textures captured by a camera at a different (longer) distance and with unknown direction of i
Image Texture Prediction using Colour Photometric Stereo Xavier Lladó 1, Joan Mart 1, and Maria Petrou 2 1 Institute of Informatics and Applications, University of Girona, 1771, Girona, Spain fllado,joanmg@eia.udg.es
More informationColor Constancy from Hyper-Spectral Data
Color Constancy from Hyper-Spectral Data Th. Gevers, H. M. G. Stokman, J. van de Weijer Faculty of Science, University of Amsterdam, The Netherlands fgevers, stokman, joostwg@wins.uva.nl Abstract This
More informationBayesian Color Constancy Revisited
Bayesian Color Constancy Revisited Peter Vincent Gehler Max Planck Institute Tübingen, Germany pgehler@tuebingen.mpg.de Carsten Rother, Andrew Blake, Tom Minka, Toby Sharp Microsoft Research Cambridge
More informationA Novel Video Enhancement Based on Color Consistency and Piecewise Tone Mapping
A Novel Video Enhancement Based on Color Consistency and Piecewise Tone Mapping Keerthi Rajan *1, A. Bhanu Chandar *2 M.Tech Student Department of ECE, K.B.R. Engineering College, Pagidipalli, Nalgonda,
More informationSalient Region Detection and Segmentation in Images using Dynamic Mode Decomposition
Salient Region Detection and Segmentation in Images using Dynamic Mode Decomposition Sikha O K 1, Sachin Kumar S 2, K P Soman 2 1 Department of Computer Science 2 Centre for Computational Engineering and
More informationCSE 167: Lecture #7: Color and Shading. Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2011
CSE 167: Introduction to Computer Graphics Lecture #7: Color and Shading Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2011 Announcements Homework project #3 due this Friday,
More informationExperimentation on the use of Chromaticity Features, Local Binary Pattern and Discrete Cosine Transform in Colour Texture Analysis
Experimentation on the use of Chromaticity Features, Local Binary Pattern and Discrete Cosine Transform in Colour Texture Analysis N.Padmapriya, Ovidiu Ghita, and Paul.F.Whelan Vision Systems Laboratory,
More informationExtending color constancy outside the visible region
Ratnasingam et al. Vol. 28, No. 4 / April 2 / J. Opt. Soc. Am. A 54 Extending color constancy outside the visible region Sivalogeswaran Ratnasingam,, * Steve Collins, and Javier Hernández-Andrés 2 Department
More informationA Statistical Model of Tristimulus Measurements Within and Between OLED Displays
7 th European Signal Processing Conference (EUSIPCO) A Statistical Model of Tristimulus Measurements Within and Between OLED Displays Matti Raitoharju Department of Automation Science and Engineering Tampere
More informationMultispectral Image Invariant to Illumination Colour, Strength, and Shading
Digital Photography VII, San Francisco, 23-27 January 2011. http://www.cs.sfu.ca/~mark/ftp/ei2011/ei2011.pdf Multispectral Image Invariant to Illumination Colour, Strength, and Shading Mark S. Drew and
More informationDIGITAL COLOR RESTORATION OF OLD PAINTINGS. Michalis Pappas and Ioannis Pitas
DIGITAL COLOR RESTORATION OF OLD PAINTINGS Michalis Pappas and Ioannis Pitas Department of Informatics Aristotle University of Thessaloniki, Box 451, GR-54006 Thessaloniki, GREECE phone/fax: +30-31-996304
More informationSupervised Object Class Colour Normalisation
Supervised Object Class Colour Normalisation Ekaterina Riabchenko 1, Jukka Lankinen 1, Anders Glent Buch 2, Joni-Kristian Kämäräinen 3,andNorbertKrüger 2 1 Lappeenranta University of Technology (Kouvola
More informationMinimalist surface-colour matching
Perception, 2005, volume 34, pages 1007 ^ 1011 DOI:10.1068/p5185 Minimalist surface-colour matching Kinjiro Amano, David H Foster Computational Neuroscience Group, Faculty of Life Sciences, University
More informationTRANSPARENT OBJECT DETECTION USING REGIONS WITH CONVOLUTIONAL NEURAL NETWORK
TRANSPARENT OBJECT DETECTION USING REGIONS WITH CONVOLUTIONAL NEURAL NETWORK 1 Po-Jen Lai ( 賴柏任 ), 2 Chiou-Shann Fuh ( 傅楸善 ) 1 Dept. of Electrical Engineering, National Taiwan University, Taiwan 2 Dept.
More informationAnalysis and extensions of the Frankle-McCann
Analysis and extensions of the Frankle-McCann Retinex algorithm Jounal of Electronic Image, vol.13(1), pp. 85-92, January. 2004 School of Electrical Engineering and Computer Science Kyungpook National
More informationEE795: Computer Vision and Intelligent Systems
EE795: Computer Vision and Intelligent Systems Spring 2012 TTh 17:30-18:45 WRI C225 Lecture 02 130124 http://www.ee.unlv.edu/~b1morris/ecg795/ 2 Outline Basics Image Formation Image Processing 3 Intelligent
More informationELEC Dr Reji Mathew Electrical Engineering UNSW
ELEC 4622 Dr Reji Mathew Electrical Engineering UNSW Dynamic Range and Weber s Law HVS is capable of operating over an enormous dynamic range, However, sensitivity is far from uniform over this range Example:
More informationEE795: Computer Vision and Intelligent Systems
EE795: Computer Vision and Intelligent Systems Spring 2012 TTh 17:30-18:45 FDH 204 Lecture 14 130307 http://www.ee.unlv.edu/~b1morris/ecg795/ 2 Outline Review Stereo Dense Motion Estimation Translational
More information2D Versus 3D Colour Space Face Detection
2D Versus 3D Colour Space Face Detection Jure Kovač, Peter Peer, Franc Solina jure.kovac@link.si, {peter.peer, franc.solina}@fri.uni-lj.si University of Ljubljana, Faculty of Computer and Information Science
More informationCGS Publishing Technologies International, LLC
G7 System Certification Application Data Sheet ORIS Lynx, COLORLynx Standard & COLORLynx Profiler The Idealliance Print Properties Working Group has established a certification process for G7 Systems.
More informationIlluminant retrieval for fixed location cameras
Illuminant retrieval for fixed location cameras Joanna Marguier and Sabine Süsstrunk School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland Abstract
More informationColour Reading: Chapter 6. Black body radiators
Colour Reading: Chapter 6 Light is produced in different amounts at different wavelengths by each light source Light is differentially reflected at each wavelength, which gives objects their natural colours
More informationSpecularity Removal using Dark Channel Prior *
JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 29, 835-849 (2013) Specularity Removal using Dark Channel Prior * School of Information Science and Engineering Central South University Changsha, 410083
More informationPattern recognition. Classification/Clustering GW Chapter 12 (some concepts) Textures
Pattern recognition Classification/Clustering GW Chapter 12 (some concepts) Textures Patterns and pattern classes Pattern: arrangement of descriptors Descriptors: features Patten class: family of patterns
More informationSLMRACE: A noise-free new RACE implementation with reduced computational time
SLMRACE: A noise-free new RACE implementation with reduced computational time Juliet Chauvin, Edoardo Provenzi To cite this version: Juliet Chauvin, Edoardo Provenzi. SLMRACE: A noise-free new RACE implementation
More informationNon-Linear Masking based Contrast Enhancement via Illumination Estimation
https://doi.org/10.2352/issn.2470-1173.2018.13.ipas-389 2018, Society for Imaging Science and Technology Non-Linear Masking based Contrast Enhancement via Illumination Estimation Soonyoung Hong, Minsub
More informationHybrid filters for medical image reconstruction
Vol. 6(9), pp. 177-182, October, 2013 DOI: 10.5897/AJMCSR11.124 ISSN 2006-9731 2013 Academic Journals http://www.academicjournals.org/ajmcsr African Journal of Mathematics and Computer Science Research
More information2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media,
6 IEEE Personal use of this material is permitted Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising
More information