FREE-VIEW WATERMARKING FOR FREE VIEW TELEVISION Alper Koz, Cevahir Çığla and A.Aydın Alatan.

Slides:

Advertisements

Similar presentations

Image Registration  Mapping of Evolution. Registration Goals Assume the correspondences are known Find such f() and g() such that the images are best.

Advertisements

3D Model Matching with Viewpoint-Invariant Patches(VIP) Reporter ：鄒嘉恆 Date ： 10/06/2009.

The fundamental matrix F

Change Detection C. Stauffer and W.E.L. Grimson, “Learning patterns of activity using real time tracking,” IEEE Trans. On PAMI, 22(8): , Aug 2000.

An Introduction to Light Fields Mel Slater. Outline Introduction Rendering Representing Light Fields Practical Issues Conclusions.

Light Fields PROPERTIES AND APPLICATIONS. Outline  What are light fields  Acquisition of light fields  from a 3D scene  from a real world scene 

MASKS © 2004 Invitation to 3D vision Lecture 7 Step-by-Step Model Buidling.

Multiple People Detection and Tracking with Occlusion Presenter: Feifei Huo Supervisor: Dr. Emile A. Hendriks Dr. A. H. J. Stijn Oomes Information and.

Light Field Rendering Shijin Kong Lijie Heng.

Camera Models A camera is a mapping between the 3D world and a 2D image The principal camera of interest is central projection.

Image alignment Image from

1 Robust Video Stabilization Based on Particle Filter Tracking of Projected Camera Motion (IEEE 2009) Junlan Yang University of Illinois,Chicago.

Multi video camera calibration and synchronization.

Uncalibrated Geometry & Stratification Sastry and Yang

Flexible Bump Map Capture From Video James A. Paterson and Andrew W. Fitzgibbon University of Oxford Calibration Requirement:

Image-Based Rendering using Hardware Accelerated Dynamic Textures Keith Yerex Dana Cobzas Martin Jagersand.

Linear View Synthesis Using a Dimensionality Gap Light Field Prior

Computational Photography Light Field Rendering Jinxiang Chai.

Multiple View Geometry Marc Pollefeys University of North Carolina at Chapel Hill Modified by Philippos Mordohai.

Automatic Camera Calibration for Image Sequences of a Football Match Flávio Szenberg (PUC-Rio) Paulo Cezar P. Carvalho (IMPA) Marcelo Gattass (PUC-Rio)

Projected image of a cube. Classical Calibration.

Automatic Image Alignment (feature-based) : Computational Photography Alexei Efros, CMU, Fall 2006 with a lot of slides stolen from Steve Seitz and.

Lec 21: Fundamental Matrix

CSE473/573 – Stereo Correspondence

Camera parameters Extrinisic parameters define location and orientation of camera reference frame with respect to world frame Intrinsic parameters define.

The Story So Far The algorithms presented so far exploit: –Sparse sets of images (some data may not be available) –User help with correspondences (time.

Automatic Camera Calibration

Image Stitching Ali Farhadi CSE 455

My Research Experience Cheng Qian. Outline 3D Reconstruction Based on Range Images Color Engineering Thermal Image Restoration.

CSC 589 Lecture 22 Image Alignment and least square methods Bei Xiao American University April 13.

Computer vision.

Lecture 11 Stereo Reconstruction I Lecture 11 Stereo Reconstruction I Mata kuliah: T Computer Vision Tahun: 2010.

WP3 - 3D reprojection Goal: reproject 2D ball positions from both cameras into 3D space Inputs: – 2D ball positions estimated by WP2 – 2D table positions.

Image Formation. Input - Digital Images Intensity Images – encoding of light intensity Range Images – encoding of shape and distance They are both a 2-D.

Recognition and Matching based on local invariant features Cordelia Schmid INRIA, Grenoble David Lowe Univ. of British Columbia.

1 Preview At least two views are required to access the depth of a scene point and in turn to reconstruct scene structure Multiple views can be obtained.

3D SLAM for Omni-directional Camera

Digital Face Replacement in Photographs CSC2530F Project Presentation By: Shahzad Malik January 28, 2003.

Reconstructing 3D mesh from video image sequences supervisor : Mgr. Martin Samuelčik by Martin Bujňák specifications Master thesis

Image Preprocessing: Geometric Correction Image Preprocessing: Geometric Correction Jensen, 2003 John R. Jensen Department of Geography University of South.

Zhejiang University Wavelet-based 3D mesh model watermarking Shi Jiao-Ying State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou

CS654: Digital Image Analysis Lecture 8: Stereo Imaging.

CSCE 643 Computer Vision: Structure from Motion

Metrology 1.Perspective distortion. 2.Depth is lost.

Image Based Rendering. Light Field Gershun in 1936 –An illuminated objects fills the surrounding space with light reflected of its surface, establishing.

Acquiring 3D models of objects via a robotic stereo head David Virasinghe Department of Computer Science University of Adelaide Supervisors: Mike Brooks.

© 2005 Martin Bujňák, Martin Bujňák Supervisor : RNDr.

CSL 859: Advanced Computer Graphics Dept of Computer Sc. & Engg. IIT Delhi.

Raquel A. Romano 1 Scientific Computing Seminar May 12, 2004 Projective Geometry for Computer Vision Projective Geometry for Computer Vision Raquel A.

Scene-Consistent Detection of Feature Points in Video Sequences Ariel Tankus & Yehezkel Yeshurun CVPR - Dec Tel-AvivUniversity.

A Flexible New Technique for Camera Calibration Zhengyou Zhang Sung Huh CSPS 643 Individual Presentation 1 February 25,

Data Hiding in Image and Video: Part II—Designs and Applications Min Wu, Heather Yu, and Bede Liu.

Photo VR Editor: A Panoramic and Spherical Environment Map Authoring Tool for Image-Based VR Browsers Jyh-Kuen Horng, Ming Ouhyoung Communications and.

Computer vision: models, learning and inference M Ahad Multiple Cameras

CSCE 641 Computer Graphics: Image-based Rendering (cont.) Jinxiang Chai.

3D Reconstruction Using Image Sequence

Auto-calibration we have just calibrated using a calibration object –another calibration object is the Tsai grid of Figure 7.1 on HZ182, which can be used.

Correspondence and Stereopsis Original notes by W. Correa. Figures from [Forsyth & Ponce] and [Trucco & Verri]

Image-Based Rendering Geometry and light interaction may be difficult and expensive to model –Think of how hard radiosity is –Imagine the complexity of.

Outline Carrier design Embedding and extraction for single tile and Multi-tiles (improving the robustness) Parameter α selection and invisibility Moment.

Lec 26: Fundamental Matrix CS4670 / 5670: Computer Vision Kavita Bala.

Correspondence and Stereopsis. Introduction Disparity – Informally: difference between two pictures – Allows us to gain a strong sense of depth Stereopsis.

Presented by 翁丞世  View Interpolation  Layered Depth Images  Light Fields and Lumigraphs  Environment Mattes  Video-Based.

CSE 185 Introduction to Computer Vision

CS4670 / 5670: Computer Vision Kavita Bala Lec 27: Stereo.

Uncalibrated Geometry & Stratification

Course 6 Stereo.

Fourier Transform of Boundaries

Recognition and Matching based on local invariant features

Image Registration  Mapping of Evolution

Presentation transcript:

FREE-VIEW WATERMARKING FOR FREE VIEW TELEVISION Alper Koz, Cevahir Çığla and A.Aydın Alatan

Outline Introduction Proposed Watermarking Method Robustness Results for the case of known external calibration. Analysis on the watermark transformations during Light Field Rendering Proposed solution for unknown external calibration Robustness results Summary and future works

Free View TV Rendered Video Cam. 0 Cam. 1 (Arbitrary view)

A New Problem: Free-view Video Watermarking Cam. 0 Cam. 1 METU EEE METU EEE METU EEE (?) Arbitrary View PROBLEM: How to embed the watermark such that the watermark can be extracted from a generated video for an arbitrary view?

Fundamental Case: One object static scenes Rendered Image Cam. 0 Cam. 1 Imagery Camera

Light Field Rendering (LFR) Light field: Static scene No occlusion Lambertian Surfaces

Light Field Rendering For each desired ray: - Compute intersection with uv and st plane. -Take closest ray Variants: -Bilinear in uv only -Bilinear in st only -Quadrilinear in (u,v,s,t)

Light Field Watermarking W W W W Direct Approach: Embed the same watermark into each frame of light field slab. uv plane (Camera plane) st plane (focal plane) I uv : Frame corresponds to the camera at (u,v). H uv : Output image after high pass filtering I uv.  : Watermark strength. W: Watermark sequence generated from a Gaussian distribution with zero mean unit variance.

Light Field Watermarking The watermark is inserted to the off-sheared images rather than embedding directly to camera frames. The same watermark is embedded to the pixels of different camera frames whose corresponding light rays intersect at the same point in the focal plane.

Watermark Detection Rendered watermark WW ren Rendered Image W I ren High pass filtering Normalized Correlation W ren Comparison to a threshold 1/0

Robustness Results 32x32 camera 256x256 pixels 24 Bits RGB Geometry of Buddha Light Field :

Robustness Results Camera position=[ ]; Image plane normal: [0 0 1]; focal length=2 (default); Rendered image Rendered watermarked image

Robustness Results Camera position = [ ]; Rotation : [0 0 0] Rendered image Rendered watermarked image

Transformation on Watermark Sequence in LFR  Bilinear Interpolation is utilized during LFR  Planar projective transformation between the original and rendered watermark.  Same transformation between imagery camera plane and focal plane.

Transformation on Watermark Sequence in LFR  How to find the projective planar transformation?  Find the points at which object surface and focal plane intersect.  Find the matches in the arbitrary view.  Determine the transformation between match pairs.

Transformation on Watermark Sequence in LFR  How to find the projective planar transformation?  Utilize two properties of these special points:  Light rays have same intensity  Observed at similar coordinates in LF images  Properties are related with Light Field parametrization.

Transformation on Watermark Sequence in LFR  How to find the projective planar transformation?  Find feature points between rendered and all LF images.  Determine images having higher number of correspondences (four images).  Select feature points at same coordinates with similar intensity values.  Fit a planar transformation model between match pairs.

Transformation on Watermark Sequence in LFR

 Apply the transformation to the original watermark.  Normalized correlation between the rendered watermark and the rendered image.  To handle small shidts in pixel locations :  utilize magnitude coefficients of the 2D Fourier transformation of the images

Robustness Results

Summary and future works A novel problem is introduced, Free-view watermarking. Analyses on watermark transformation are examined.  Known external calibrations.  Unknown external calibrations  Bilinear interpolation in u-v plane. Watermark is successfully detected from an aritrary view. Extension of the algorithm for multiple depths and static scenes