Camerabased projector calibration, investigation of the Bala method

Slides:



Advertisements
Similar presentations
Introduction to Colour Management
Advertisements

Digital Imaging of Photographs Jenn Riley IU Digital Library Program September 19, 2003.
Multispectral Format from Perspective of Remote Sensing
Experiments and Variables
Photoshop Lab colorspace A quick and easy 26 step process for enhancing your photos.
Introduction to compositing. What is compositing?  The combination of two images to produce a single image  Many ways we can do this, especially in.
Digital Image Processing
UNDERSTANDING RAW Joe Sukenick DigiQuest
Full Gamut Color Matching for Tiled Display Walls Grant Wallace, Han Chen, Kai Li Princeton University.
Digital Imaging and Image Analysis
 Image Characteristics  Image Digitization Spatial domain Intensity domain 1.
Image Processing IB Paper 8 – Part A Ognjen Arandjelović Ognjen Arandjelović
DREAM PLAN IDEA IMPLEMENTATION Introduction to Image Processing Dr. Kourosh Kiani
Bit Depth and Spatial Resolution SIMG-201 Survey of Imaging Science © 2002 CIS/RIT.
The eyes have three different kinds of color receptors; One kind is most sensitive to short wavelengths, one to middle wavelengths, and one to long wavelengths.
Capturing and optimising digital images for research Gilles Couzin.
Color Mixing There are two ways to control how much red, green, and blue light reaches the eye: “Additive Mixing” Starting with black, the right amount.
Digital Cameras CCD (Monochrome) RGB Color Filter Array.
Comparison of two eye tracking devices used on printed images Barbora Komínková The Norwegian Color Research Laboratory Faculty of Computer Science and.
Perceptual Evaluation of Colour Gamut Mapping Algorithms Fabienne Dugay The Norwegian Color Research Laboratory Faculty of Computer Science and Media Technology.
Introduction to Image Quality Assessment
1 Comp300a: Introduction to Computer Vision L. QUAN.
Colorimetric characterization of input media in motion picture production Steffen Paul The Norwegian Color Research Laboratory Faculty of Computer Science.
Example-Based Color Transformation of Image and Video Using Basic Color Categories Youngha Chang Suguru Saito Masayuki Nakajima.
Digital Audio, Image and Video Hao Jiang Computer Science Department Sept. 6, 2007.
Computer Science 111 Fundamentals of Programming I Introduction to Digital Image Processing.
Color Management and Correction in Video Production Oke Mudiaga Innocent Digital Information Provision.
Importance of region-of-interest on image difference metrics Marius Pedersen The Norwegian Color Research Laboratory Faculty of Computer Science and Media.
Dye Sublimation Color Management
Trevor McCasland Arch Kelley.  Goal: reduce the size of stored files and data while retaining all necessary perceptual information  Used to create an.
Colour Digital Multimedia, 2nd edition Nigel Chapman & Jenny Chapman
Image Processing.  a typical image is simply a 2D array of color or gray values  i.e., a texture  image processing takes as input an image and outputs.
Technology and digital images. Objectives Describe how the characteristics and behaviors of white light allow us to see colored objects. Describe the.
By Meidika Wardana Kristi, NRP  Digital cameras used to take picture of an object requires three sensors to store the red, blue and green color.
IDL GUI for Digital Halftoning Final Project for SIMG-726 Computing For Imaging Science Changmeng Liu
Wavelet-Based Multiresolution Matching for Content-Based Image Retrieval Presented by Tienwei Tsai Department of Computer Science and Engineering Tatung.
Computational and Biological Vision “Colors Out Of Space” Digital color representation, color spaces and more! Amir Eluk Software Engineering.
3D SLAM for Omni-directional Camera
© 1999 Rochester Institute of Technology Introduction to Digital Imaging.
High-Resolution Interactive Panoramas with MPEG-4 발표자 : 김영백 임베디드시스템연구실.
COLOR HISTOGRAM AND DISCRETE COSINE TRANSFORM FOR COLOR IMAGE RETRIEVAL Presented by 2006/8.
SIGNAL DETECTION IN FIXED PATTERN CHROMATIC NOISE 1 A. J. Ahumada, Jr., 2 W. K. Krebs 1 NASA Ames Research Center; 2 Naval Postgraduate School, Monterey,
Purdue University Page 1 Color Image Fidelity Assessor Color Image Fidelity Assessor * Wencheng Wu (Xerox Corporation) Zygmunt Pizlo (Purdue University)
1 Introduction to Computer Graphics with WebGL Ed Angel Professor Emeritus of Computer Science Founding Director, Arts, Research, Technology and Science.
Visual Cryptography Advanced Information Security March 11, 2010 Presenter: Semin Kim.
Image Representation. Digital Cameras Scanned Film & Photographs Digitized TV Signals Computer Graphics Radar & Sonar Medical Imaging Devices (X-Ray,
Digital Image Processing Part 1 Introduction. The eye.
Computer Science 111 Fundamentals of Programming I Introduction to Digital Image Processing.
CS6825: Color 2 Light and Color Light is electromagnetic radiation Light is electromagnetic radiation Visible light: nm. range Visible light:
How digital cameras work The Exposure The big difference between traditional film cameras and digital cameras is how they capture the image. Instead of.
The Reason Tone Curves Are The Way They Are. Tone Curves in a common imaging chain.
Three-Receptor Model Designing a system that can individually display thousands of colors is very difficult Instead, colors can be reproduced by mixing.
Content-Based Image Retrieval Using Block Discrete Cosine Transform Presented by Te-Wei Chiang Department of Information Networking Technology Chihlee.
Visual Computing Computer Vision 2 INFO410 & INFO350 S2 2015
An Image Retrieval Approach Based on Dominant Wavelet Features Presented by Te-Wei Chiang 2006/4/1.
Image: Susanne Rafelski, Marshall lab Introduction to Digital Image Analysis Part I: Digital Images Kurt Thorn NIC UCSF.
Introduction to Digital Image Analysis Kurt Thorn NIC.
1 of 32 Computer Graphics Color. 2 of 32 Basics Of Color elements of color:
Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 1 Integrated Color Solutions A presentation.
Stimuli were presented on a 17 inch monitor (in a dimly lit room), operating at 60 Hz with a resolution of 1280 x Two objects of the same type (teapot.
Coin Recognition Using MATLAB - Emad Zaben - Bakir Hasanein - Mohammed Omar.
Heechul Han and Kwanghoon Sohn
Fundamentals of Programming I Introduction to Digital Image Processing
Introduction to Computer Graphics with WebGL
Video System TTFs Part (I): Basic Design Strategy.
School of Electrical and
CS654: Digital Image Analysis
By: Mohammad Qudeisat Supervisor: Dr. Francis Lilley
Digital Image Processing
DIGITAL IMAGE PROCESSING Elective 3 (5th Sem.)
Presentation transcript:

Camerabased projector calibration, investigation of the Bala method Espen Bårdsnes Mikalsen The Norwegian Color Research Laboratory Faculty of Computer Science and Media Technology Gjøvik University College, Gjøvik, Norway ebmikalsen@hotmail.com, http://www.colorlab.no, Supervisors: Jon Yngve Hardeberg and Jean-Baptiste Thomas Master thesis presentations, Gjøvik, 07.06.2007

Outline Introduction Experimental setup Results Conclusion Implementing the Bala method Experimental setup Results Conclusion

The Bala method Calibration method for projectors presented by Raja Bala and Karen Braun, Xerox co, 2006. Focuses on tone response calibration, no 3 x 3 transform matrix. Using a digital photo camera as a luminance measurement device by calibrating camera through visual luminance matching. Using the calibrated camera to tone response calibrate projector.

Research questions Q1: Verification of the Bala method An evaluation of the calibration method suggested by Bala and Braun. Implementation and performance testing with a final numeric results and analysis as a measure of performance. Separate evaluation of methods parts to identify strengths and weaknesses in the approach. Q2: Extensions to the Bala method In the original paper where the calibration method was presented the authors purposed some extensions that could increase the methods performance. Through implemetation and analysis these will be evaluated. Extensions are visual matching of three luminance values per estimated curve instead of one, and separate correction of R, G and B color channel replacing uniform luminance correction.

Simple walkthrough of the Bala method Step 1 - gather information Step 2 - process information to estimate projector tone response and correction curve

Step 1 - Visual matching of luminance A binary rasterpattern consisting of 50% black and 50% white pixels. Adjust the background colors luminance to perceptually match binary pattern. The adjusted luminance of background is the perceptually found 50% luminance value.

Display calibration target Luminance patch chart ranging from min to max luminance, with horizontal and vertical duplications of matched 50% luminance for use with non-uniformity correction.

Step 1 – Capture image Capture an image of the projected chart with the uncalibrated camera.

Step 2 – Retrive data from captured image Rotate and crop image

Step 2 – Retrive data from captured image Read RGB data from image. Gives 24 individual sets of RGB. RGB sets are converted to a luminance value for each patch.

Step 2 – Perform non-uniformity correction Devices like cameras and projectors often suffer from some kind of spatial non-uniformity. Non-uniformity is when a device responds spatially non-uniform to a uniform input. When capturing an image of a projection, the image will suffer from both non-uniformity of projector and of camera. This method does not correct for non-uniformity in projection, but corrects for non-uniformity in data used for calibration of camera and projector. Correction are based on calculating differances between the spread out duplications of the 50% luminance patch.

Step 2 – Estimating camera tone response curve Knowing the relationship between projected luminances and target luminances ( min, max and 50% ) makes it possible to interpolate an estimation of the cameras tone response curve. Description Luminance Captured camera value Projector white Yw = 1 Ypatch white Projector black Yb Ypatch black Mid-gray ( 1 + Yb ) / 2 Ypatch 50% lum Perfect black

Step 2 - Example of estimated camera TRC

Extensions to original method Instead of only determining camera TRC based on the 50% luminance point. Add two new luminances ( 25% and 75% ) to help determine curve. Instead of using same correction curve for R, G and B channel. Estimate curves separatly.

Experiment setup Projectors Projectiondesign ActionOne DLP, 2003 model Panasonic AX-100 LCD, 2006 model Cameras Nikon D200 DSLR FujiFilm s7000 compact digital camera Spectroradiometer Minolta Room conditions Dark room, only luminance from projection

Results – Visual matching Experiment set up to determine if visually matched luminance values deviate from person to person and when repeating matching of same value. Matching done at 3 luminance levels for R, G, B and gray channel. 6 observers visually matching 12 luminances 3 times. A total of 216 values were matched. Channel Mean Minimum Maximum % Deviance Gray 25% 0,5114 0,4748 0,5262 5,14 Gray 50% 0,6959 0,6889 0,7020 1,31 Gray 75% 0,8554 0,8419 0,8706 2,87 Red 25% 0,5096 0,4850 0,5377 5,27 Red 50% 0,6957 0,6848 0,7076 2,28 Red 75% 0,8423 0,8141 0,8594 4,53 Green 25% 0,5099 0,4831 0,5237 4,06 Green 50% 0,6939 0,6894 0,7023 1,29 Green 75% 0,8521 0,8328 0,8656 3,28 Blue 25% 0,5302 0,5204 0,5622 4,18 Blue 50% 0,7169 0,7082 0,7308 2,26 Blue 75% 0,8628 0,8496 0,8748 2,52

Results - non-uniformity correction

Results – Camera TRC estimation (ActionOne - Nikon)

Results – Camera TRC estimation (Panasonic - Nikon)

Results – Camera TRC estimation (ActionOne - Nikon)

Results – Estimated projector TRC (ActionOne - Nikon)

Results – Estimated projector TRC (Panasonic - Nikon)

Results – Correction w/ correction curve (ActionOne-Nikon)

Results – Correction w/ separate RGB correction curves (ActionOne-Nikon)

Results – Correction w/ correction curve (ActionOne-Nikon)

Conclusion Q1: Verification of the Bala method It has to some extent been proven that calibration of projectors using this method will result in a more exact reproduction of color then for example using standard sRGB gamma correction. Correction results are better for DLP then LCD projector, probably because of LCD conforms better to the sRGB gamma curve, and correction will therefore be less necessary. Q2: Extensions to the Bala method It has been proven that interpolating camera TRC with not only one visually matched point, but several will improve accuracy of camera TRC, and therefore also estimated projection TRC and correction curve. If retrieving separate camera TRC for R, G and B color channel has a positive effect on method performance has not yet been proven.