Yingen Xiong and Kari Pulli

Slides:



Advertisements
Similar presentations
1 Overview Assignment 4: hints Memory management Assignment 3: solution.
Advertisements

High Dynamic Range Imaging Samu Kemppainen VBM02S.
Evaluation of processes used in screen imperfection algorithms Siavash A. Renani.
Image Processing IB Paper 8 – Part A Ognjen Arandjelović Ognjen Arandjelović
COLORCOLOR A SET OF CODES GENERATED BY THE BRAİN How do you quantify? How do you use?
Color2Gray: Salience-Preserving Color Removal
Virtual Dart: An Augmented Reality Game on Mobile Device Supervisor: Professor Michael R. Lyu Prepared by: Lai Chung Sum Siu Ho Tung.
CSCE 641 Computer Graphics: Image Mosaicing Jinxiang Chai.
Capturing and optimising digital images for research Gilles Couzin.
February 2007Aalborg University, Mobile Device Group Mobile Software Technologies Course Morten Lisborg Jørgensen.
Computational Photography: Image Mosaicing Jinxiang Chai.
1 Color Segmentation: Color Spaces and Illumination Mohan Sridharan University of Birmingham
SIGGRAPH Course 30: Performance-Driven Facial Animation Section: Markerless Face Capture and Automatic Model Construction Part 2: Li Zhang, Columbia University.
Apparent Greyscale: A Simple and Fast Conversion to Perceptually Accurate Images and Video Kaleigh SmithPierre-Edouard Landes Joelle Thollot Karol Myszkowski.
1Jana Kosecka, CS 223b Cylindrical panoramas Cylindrical panoramas.
Automatic Panoramic Image Stitching using Local Features Matthew Brown and David Lowe, University of British Columbia.
Camerabased projector calibration, investigation of the Bala method
LYU0503 Document Image Reconstruction on Mobile Using Onboard Camera Supervisor: Professor Michael R.Lyu Group Members: Leung Man Kin, Stephen Ng Ying.
Image Stitching and Panoramas
COS 429 PS3: Stitching a Panorama Due November 4 th.
Gradient Domain High Dynamic Range Compression
Creating and Exploring a Large Photorealistic Virtual Space INRIA / CSAIL / Adobe First IEEE Workshop on Internet Vision, associated with CVPR 2008.
On Error Preserving Encryption Algorithms for Wireless Video Transmission Ali Saman Tosun and Wu-Chi Feng The Ohio State University Department of Computer.
Real-Time Vision on a Mobile Robot Platform Mohan Sridharan Joint work with Peter Stone The University of Texas at Austin
Interactive Face Recognition (IFR) Nishanth Vincent Fairfield University Advisor: Professor Douglas A. Lyon, Ph.D.
MACHINE VISION GROUP Multimodal sensing-based camera applications Miguel Bordallo 1, Jari Hannuksela 1, Olli Silvén 1 and Markku Vehviläinen 2 1 University.
How Computers Work. A computer is a machine f or the storage and processing of information. Computers consist of hardware (what you can touch) and software.
FRITZ SCHNEIDERPEACHAM CYBERNETICS Introduction To Digital Photography III - Postprocessing.
Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Center for Visual Computing Stony Brook University.
MACHINE VISION GROUP Graphics hardware accelerated panorama builder for mobile phones Miguel Bordallo López*, Jari Hannuksela*, Olli Silvén* and Markku.
Efficient Editing of Aged Object Textures By: Olivier Clément Jocelyn Benoit Eric Paquette Multimedia Lab.
Computational and Biological Vision “Colors Out Of Space” Digital color representation, color spaces and more! Amir Eluk Software Engineering.
Tone Mapping Software Photomatix Pro Application to Photography Konferenz und Workshop '05 Reality-Based Visualization.
Dynamic Range And Granularity. Dynamic range is important. It is defined as the difference between light and dark areas of an image. All digital images.
High-Resolution Interactive Panoramas with MPEG-4 발표자 : 김영백 임베디드시스템연구실.
Video Based Palmprint Recognition Chhaya Methani and Anoop M. Namboodiri Center for Visual Information Technology International Institute of Information.
CS4670 / 5670: Computer Vision KavitaBala Lecture 17: Panoramas.
1 Research Question  Can a vision-based mobile robot  with limited computation and memory,  and rapidly varying camera positions,  operate autonomously.
Epitomic Location Recognition A generative approach for location recognition K. Ni, A. Kannan, A. Criminisi and J. Winn In proc. CVPR Anchorage,
An Efficient Linear Time Triple Patterning Solver Haitong Tian Hongbo Zhang Zigang Xiao Martin D.F. Wong ASP-DAC’15.
Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/10/15 “Empire of Light”, Magritte.
Eye regions localization Balázs Harangi – University of Debrecen Ciprian Pop – Technical University of Cluj-Napoca László Kovács – University of Debrecen.
Real-Time Cyber Physical Systems Application on MobilityFirst Winlab Summer Internship 2015 Karthikeyan Ganesan, Wuyang Zhang, Zihong Zheng Shantanu Ghosh,
A NOVEL METHOD FOR COLOR FACE RECOGNITION USING KNN CLASSIFIER
Student Name: Honghao Chen Supervisor: Dr Jimmy Li Co-Supervisor: Dr Sherry Randhawa.
Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.
Su-ting, Chuang 2010/8/2. Outline Introduction Related Work System and Method Experiment Conclusion & Future Work 2.
Subject Name: Computer Graphics Subject Code: Textbook: “Computer Graphics”, C Version By Hearn and Baker Credits: 6 1.
MACHINE VISION GROUP MOBILE FEATURE-CLOUD PANORAMA CONSTRUCTION FOR IMAGE RECOGNITION APPLICATIONS Miguel Bordallo, Jari Hannuksela, Olli silvén Machine.
Learning Photographic Global Tonal Adjustment with a Database of Input / Output Image Pairs.
Memory The term memory is referred to computer’s main memory, or RAM (Random Access Memory). RAM is the location where data and programs are stored (temporarily),
Hypermedia: QuickTime VR Authoring Studio 1.0 By Peter Leong & Wallace Napier.
Graphics II Image Processing I. Acknowledgement Most of this lecture note has been taken from the lecture note on Multimedia Technology course of University.
Image Stitching II Linda Shapiro CSE 455. RANSAC for Homography Initial Matched Points.
Chia-Ho Pan DSPIC/GIEE NTU
Hebrew University Image Processing Exercise Class 8 Panoramas – Stitching and Blending Min-Cut Stitching Many slides from Alexei Efros.
A Framework for Perceptual Studies in Photorealistic Augmented Reality Martin Knecht 1, Andreas Dünser 2, Christoph Traxler 1, Michael Wimmer 1 and Raphael.
Design and Calibration of a Multi-View TOF Sensor Fusion System Young Min Kim, Derek Chan, Christian Theobalt, Sebastian Thrun Stanford University.
Heechul Han and Kwanghoon Sohn
Image Stitching II Linda Shapiro EE/CSE 576.
Chapter 1 Introduction.
COSC579: Image Align, Mosaic, Stitch
PRESENTED BY Yang Jiao Timo Ahonen, Matti Pietikainen
Dingding Liu* Yingen Xiong† Linda Shapiro* Kari Pulli†
Idea: projecting images onto a common plane
Image Stitching II Linda Shapiro CSE 455.
Operating System Introduction.
Image Stitching II Linda Shapiro EE/CSE 576.
Gradient Domain Salience-preserving Color-to-gray Conversion
Histograms and Color Balancing
Presentation transcript:

Yingen Xiong and Kari Pulli Color Matching of Image Sequences with Combined Gamma and Linear Corrections Yingen Xiong and Kari Pulli Download our panorama software : http://store.ovi.com/content/51491

Outline Introduction Color correction with color matching What is the problem? Why do we need color correction? Related work Color correction with color matching Problem expression Color matching by gamma correction Color mean matching by gamma correction Combination of gamma and linear corrections Applications and results Conclusions

Introduction: Mobile Panorama System Image capturing camera Color correction Object editing Image registration Image labeling Panorama viewing Image warping or Image blending Download our panorama software: http://store.ovi.com/content/51491

What is the Problem? Image parameters (focus, exposure, WB) change for each image Changes in illumination lead to different exposure levels The same objects in different frames may have different apparent colors

Panorama Stitching without Color Correction Stitching artifacts ; visible seams; bad color transitions

Color Correction to Reduce Color Differences Perform color correction before panorama stitching

Related Work Linear-model-based color correction Luminance correction Polynomial mapping and others sRGB color space Tian et al. 2002 YCbCr color space Ha et al. 2007 Linearized RGB color space Xiong and Pulli 2009 Linearized RGB color space Meunier and Borgmann 2000 sRGB color space Brown and Lowe 2007 Pham and Pringle 1995 Polynomial mapping Histogram mapping Zhang et al. 2001

Color Correction using Linear Model Original source images with different colors Simple, fast, color saturation, low quality

Efficient Color Correction is Needed Avoid saturation problems Reduce color differences

Color Matching with Gamma Correction

Color Matching with Gamma Correction

Color Mean Matching with Gamma Correction

Color Mean Matching with Gamma Correction

Combination of Gamma and Linear Correction

Combination of Gamma and Linear Correction

Comparison of the Results

Applications and Result Analysis Application environment Implemented in a mobile panorama imaging system Runs on several mobile devices Nokia N900, N8, N95, … Nokia N900 ARM Cortex A8 600 MHz processor 256MB RAM 768MB virtual memory 3.5 inch touch display ARM 11 332 MHz processor 128MB RAM Nokia N95 8G Nokia N8

Computation Time Computational time for color correction: 5 images: 0.37, 1.08, 1.86 seconds 10 images: 0.97, 1.56, 4.12 seconds A: color correction, B image labeling, C: image blending, D: image stitching Resolution Time for 5 Images (sec.) Time for 10 Images (sec.) A B C D 1280x960 0.37 3.30 2.48 6.15 0.97 6.96 5.44 13.37 2048x1536 1.08 6.72 4.70 12.50 1.56 14.44 10.46 26.46 2576x1936 1.86 15.98 12.25 30.09 4.12 35.63 29.34 69.09

Color and Color Mean matching

Gamma Correction in Different Color Spaces

Different Color Correction Local linear correction in sRGB Local linear correction in YCbCr Global linear correction in sRGB Color matching with gamma correction Color matching with gamma mean correction

Different Color Correction Local linear correction in sRGB Local linear correction in YCbCr Global linear correction in sRGB Color matching with gamma correction Color matching with gamma mean correction

Image Sequences with Random Order

More Examples

Conclusions Color correction with color matching Implementation Gamma correction for luminance Linear correction for chrominance Implementation Runs on mobile phones, high quality download from http://store.ovi.com to your N8 / N900 Advantages No color saturation problems during color correction Good color transitions for the whole image sequence Efficient (fast) execution

Thank You

Questions? Download our mobile panorama software at http://store.ovi.com/content/51491