Digital Photography with Flash and No-Flash Image Pairs Gabriela Martínez Processamento de Imagem IMPA.

Slides:



Advertisements
Similar presentations
CSCE 643 Computer Vision: Template Matching, Image Pyramids and Denoising Jinxiang Chai.
Advertisements

Digital Photography with Flash and No-Flash Image Pairs By: Georg PetschniggManeesh Agrawala Hugues HoppeRichard Szeliski Michael CohenKentaro Toyama,
Lecture 07 Segmentation Lecture 07 Segmentation Mata kuliah: T Computer Vision Tahun: 2010.
Multiple People Detection and Tracking with Occlusion Presenter: Feifei Huo Supervisor: Dr. Emile A. Hendriks Dr. A. H. J. Stijn Oomes Information and.
Templates, Image Pyramids, and Filter Banks Slides largely from Derek Hoeim, Univ. of Illinois.
A LOW-COMPLEXITY, MOTION-ROBUST, SPATIO-TEMPORALLY ADAPTIVE VIDEO DE-NOISER WITH IN-LOOP NOISE ESTIMATION Chirag Jain, Sriram Sethuraman Ittiam Systems.
Computer Vision Group Edge Detection Giacomo Boracchi 5/12/2007
January 19, 2006Computer Vision © 2006 Davi GeigerLecture 1.1 Image Measurements and Detection Davi Geiger
Edge detection. Edge Detection in Images Finding the contour of objects in a scene.
EE663 Image Processing Edge Detection 2 Dr. Samir H. Abdul-Jauwad Electrical Engineering Department King Fahd University of Petroleum & Minerals.
Robust Object Segmentation Using Adaptive Thresholding Xiaxi Huang and Nikolaos V. Boulgouris International Conference on Image Processing 2007.
??? Eyes Brain (Inside) Conclusion: Ideally Suited for Image Processing.
SUSAN: structure-preserving noise reduction EE264: Image Processing Final Presentation by Luke Johnson 6/7/2007.
CSE 291 Final Project: Adaptive Multi-Spectral Differencing Andrew Cosand UCSD CVRR.
Shadow Removal Seminar
The Segmentation Problem
A Novel 2D To 3D Image Technique Based On Object- Oriented Conversion.
[cvPONG] A 3-D Pong Game Controlled Using Computer Vision Techniques Quan Yu and Chris Wagner.
1 An Implementation Sanun Srisuk of EdgeFlow.
Robust estimation Problem: we want to determine the displacement (u,v) between pairs of images. We are given 100 points with a correlation score computed.
Feature extraction Feature extraction involves finding features of the segmented image. Usually performed on a binary image produced from.
Brief overview of ideas In this introductory lecture I will show short explanations of basic image processing methods In next lectures we will go into.
CS448f: Image Processing For Photography and Vision Denoising.
VINCENT URIAS, CURTIS HASH Detection of Humans in Images Using Skin-tone Analysis and Face Detection.
Sana Naghipour, Saba Naghipour Mentor: Phani Chavali Advisers: Ed Richter, Prof. Arye Nehorai.
Tone mapping with slides by Fredo Durand, and Alexei Efros Digital Image Synthesis Yung-Yu Chuang 11/08/2005.
A Gentle Introduction to Bilateral Filtering and its Applications 08/10: Applications: Advanced uses of Bilateral Filters Jack Tumblin – EECS, Northwestern.
Xiaojiang Ling CSE 668, Animate Vision Principles for 3D Image Sequences CSE Department, SUNY Buffalo
Abstract Some Examples The Eye tracker project is a research initiative to enable people, who are suffering from Amyotrophic Lateral Sclerosis (ALS), to.
1 Color Processing Introduction Color models Color image processing.
Templates, Image Pyramids, and Filter Banks Computer Vision Derek Hoiem, University of Illinois 01/31/12.
1 © 2010 Cengage Learning Engineering. All Rights Reserved. 1 Introduction to Digital Image Processing with MATLAB ® Asia Edition McAndrew ‧ Wang ‧ Tseng.
Manipulating contrast/point operations. Examples of point operations: Threshold (demo) Threshold (demo) Invert (demo) Invert (demo) Out[x,y] = max – In[x,y]
Templates, Image Pyramids, and Filter Banks
Joon Hyung Shim, Jinkyu Yang, and Inseong Kim
03/05/03© 2003 University of Wisconsin Last Time Tone Reproduction If you don’t use perceptual info, some people call it contrast reduction.
Depth Edge Detection with Multi- Flash Imaging Gabriela Martínez Final Project – Processamento de Imagem IMPA.
Image Filtering Computer Vision CS 543 / ECE 549 University of Illinois Derek Hoiem 02/02/10.
Image Enhancement [DVT final project]
Bo QIN, Zongshun MA, Zhenghua FANG, Shengke WANG Computer-Aided Design and Computer Graphics, th IEEE International Conference on, p Presenter.
DSP final project proosal From Bilateral-filter to Trilateral-filter : A better improvement on denoising of images R 張錦文.
Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/10/15 “Empire of Light”, Magritte.
Templates, Image Pyramids, and Filter Banks
` Tracking the Eyes using a Webcam Presented by: Kwesi Ackon Kwesi Ackon Supervisor: Mr. J. Connan.
Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.
Introduction to JPEG m Akram Ben Ahmed
Tone mapping Digital Visual Effects, Spring 2007 Yung-Yu Chuang 2007/3/13 with slides by Fredo Durand, and Alexei Efros.
Edge Segmentation in Computer Images CSE350/ Sep 03.
Instructor: Mircea Nicolescu Lecture 7
Chapter 24: Perception April 20, Introduction Emphasis on vision Feature extraction approach Model-based approach –S stimulus –W world –f,
Digital Image Processing CSC331
We are :- Soma Datta, Pramit Ghosh and Debotosh Bhattacherjee
An Introduction to Digital Image Processing Dr.Amnach Khawne Department of Computer Engineering, KMITL.
Non-linear filtering Example: Median filter Replaces pixel value by median value over neighborhood Generates no new gray levels.
Course : T Computer Vision
Ido Omer Michael Werman
CPSC 6040 Computer Graphics Images
Image Processing and Reconstructions Tools
Dr. Chang Shu COMP 4900C Winter 2008
Digital Visual Effects, Spring 2006 Yung-Yu Chuang 2006/3/8
Estimation of Skin Color Range Using Achromatic Features
Face Detection in Color Images
Midterm Exam Closed book, notes, computer Similar to test 1 in format:
Fourier Transform of Boundaries
Lecture 2: Image filtering
Midterm Exam Closed book, notes, computer Similar to test 1 in format:
The Image The pixels in the image The mask The resulting image 255 X
Image segmentation Grey scale image Binary image
Computer Graphics Image processing 紀明德
Histograms and Color Balancing
Presentation transcript:

Digital Photography with Flash and No-Flash Image Pairs Gabriela Martínez Processamento de Imagem IMPA

Red eyes Detection ● The idea here is to compute the "red" difference in both images and keep the extreme points. ● Here the challenge becomes which color space to use.

Red eyes Detection ● RGB - ineffective since RGB is correlated with luminosity ● HSV - ineffective because “redness” difference is split among S and V ● YCrCb - ok

Red eyes Detection ● First we segment the "chrominance" difference image using a low threshold. This gives a binary image of a few probable blobs that could be considered as red-eye. ● The next step is to only select the blobs which pass the high threshold. We obtain a rather robust classification.

Red eyes Detection ● CIE L*a*b - effective since “a” is a luminosity independent measure of red ● But if there is another surface that reflects red light in the image, we could have troubles.

Implementation ● Using Matlab's Image Processing Tool box. ● Calculate: R=F Cr -A Cr, where F is the flash image and A is the ambient image. ● Create detection mask ● Find seed pixels with command find and mark them with bwselect function.

Results

Denoising and Detail Transfer ● Reducing noise in photographic images, long- standing problem. ● Classical solution: Bilateral Filter proposed by Tomasi and Manduchi 1998.

Denoising and Detail Transfer ● Bilateral filter: average together pixels spatiually near and have similar intensity values. A p =(1/k(p))sum(g d (p'-p)g r (A p -A p' )A p' ) k(p)=sum(g d (p'-p)g r (Ap-Ap')) gd, gr are Gaussians with width controlled sig d ( geometric closeness), sig r (intensity range)

Denoising and Detail Transfer ● Joint Bilateral filter: Relies on the flash image as an estimator of the ambient image. A p =(1/k(p))sum(g d (p'-p)g r (F p -F p' )A p' ) k(p)=sum(g d (p'-p)g r (Fp-Fp')) gd, gr are Gaussians with width controlled sig d ( geometric closeness), sig r (intensity range)

Algorithm ● Bilateral Filter calcule: A_base, F_base ● Calcule: F_detail=(F+0.02)/(F_base+0.02) ● Joint Bilateral Filter calcule: A_nr ● Calcule shadow mask: diff(F,A) <t shadow ● Result: (1-M)A_nrF_detail+MA_base

Results

References ● PETSCHNIGG.,G., Digital Photography with Flash and No-Flash Image Pairs. ACM 2004 ● GONZALEZ.,R., Digital Image Processing using MATLAB. Editorial Prentice Hall. ● m ● Bilateral Filtering for Gray and Color Images. COPIES/MANDUCHI1/Bilateral_Filtering.html