Privacy Preserving Optics for Miniature Vision Sensors

Size: px
Start display at page:

Download "Privacy Preserving Optics for Miniature Vision Sensors"

Transcription

1 Privacy Preserving Optics for Miniature Vision Sensors Francesco Pittaluga and Sanjeev J. Koppal University of Florida Electrical and Computer Engineering

2 Shoham et al. 07, Wood 08, Enikov et al. 09, Agrihouse 15 The next wave of small devices Microrobots Medical devices Remote sensor nodes

3 In the future, there will be trillions of networked miniature cameras.

4 Privacy in the Face of Trillions of Eyes Some groups are particularly vulnerable

5 Our ideas 1) Pre-Capture Privacy Privacy before capture 2) Miniaturizing Algorithms - High performance - Smallest mass and volume We show mobile scale prototypes

6 Motivating Example We want to: 1) Track/Photograph everyone Group of People 2) Prevent face recognition Quantitative: accurate people tracking and low recognition rate

7 Block Diagram Light 1. Tracking people with privacy Modulating Optics Light Camera Group of People 2. Photographing people with privacy

8 Block Diagram Light 1. Tracking people with privacy Modulating Optics Light Camera Group of People 2. Photographing people with privacy

9 Ray Diagram Display Light Displayed light Camera Group of People Beam splitter

10 Ray Diagram Display Light Displayed light Group of People Beam splitter Camera Displayed light + Scene radiance

11 Pre-Capture White-Out Some overexposed pixels Light Group of People Beam splitter Image Captured

12 Optical K-Anonymity Optically superimpose face Light Group of People Beam splitter Sweeney 2002

13 Optical K-anonymity Scene Displayed Image Sensor output

14 Image Formation Model Image pixel Scene point radiance Pixel-radiance map Weight Camera-display transform Optical path split ratio Weight sum of k-1 images

15 Miniaturization (Orthographic)

16 Miniaturization (Orthographic) Display M_min (min size for res.) Camera Beam splitter

17 Miniaturization (Orthographic) Display lmask lbeam M_min (min size for res.) Camera Beam splitter

18 Volume occupied Display Camera Beam splitter

19 Miniaturization by translation Display Camera Beam splitter

20 Miniaturization (Perspective) Please see details in the paper

21 Block Diagram Light 1. Tracking people with privacy Modulating Optics Light Camera Group of People 2. Photographing people with privacy

22 Block Diagram Light 1. Tracking people with privacy Modulating Optics Light Camera Group of People 2. Photographing people with privacy

23 Our key ideas Defocus in time-of-flight (TOF) and thermal domains preserve utility and provide privacy Multiple defocus apertures allow privacy and utility even in visible domains Not effective in RGB for small blur Neustaedter 2006

24 Privacy Vision Sensor 1 of 3 Light 1. Tracking people with privacy Modulating Optics Light Camera Group of People 2. Photographing people with privacy

25 Defocused time-of-flight camera Conventional usage With Defocus

26 Defocused time-of-flight camera Conventional usage With Defocus

27 Privacy Vision Sensor 2 of 3 Light 1. Tracking people with privacy Modulating Optics Light Camera Group of People 2. Photographing people with privacy

28 Defocused thermal camera FLIR One with defocus lens

29 Miniaturization of defocus d u

30 Miniaturization of defocus d Camera with defocus u

31 Miniaturization of defocus Camera with defocus

32 Miniaturization of defocus Angular support Given desired support Given tolerance Camera with defocus Viewing direction Koppal 2013

33 Miniaturization of defocus Given Our PAMI 2013 u, d Output Defocus parameter and angular res. Biggest feature to anonymize Given

34 Miniaturization of defocus Given Our PAMI 2013 u, d Output Given Output

35 Miniaturization of defocus Camera with defocus

36 Miniaturization of defocus Camera with defocus

37 Privacy Vision Sensor 3 of 3 Light 1. Tracking people with privacy Modulating Optics Light Camera Group of People 2. Photographing people with privacy

38 Scale space analysis Gaussian pyramid Lindeberg 1998

39 Worry about privacy here! Scale space analysis with optical defocus Optical apertures Scene

40 Scale space detection with optical defocus A classifier is trained on the blobs

41 Optical Array Miniaturization

42 Optical Array Miniaturization Optical elements with mass/volume/fov Physical device has size limits

43 Optical Array Miniaturization Optical elements with mass/volume/fov Physical device has size limits

44 Optical Array Miniaturization Has two parts Selection Packing We focus on selection Korf et al. 2010

45 Knapsack Problem These could get added into the design

46 Optical Knapsack Problem

47 Optical Knapsack Problem Angular discretization No magic: Pseudo-polynomial approximation

48 Summary K-anonymity Camera Defocus in thermal and TOF preserves privacy Optical scale space analysis Optical knapsack solution

49 Future Work: Privacy in Image Formation Materials Image Formation Camera Geometry Acquired Image Lighting

50 Demo at CVPR 2015 Sensor Setup Pre-Capture White-Out

Sensor-level Privacy for Thermal Cameras

Sensor-level Privacy for Thermal Cameras Sensor-level Privacy for Thermal Cameras Francesco Pittaluga Aleksandar Zivkovic Sanjeev J. Koppal University of Florida Imaging and Tracking People Surveillance Military Gaming IoT Mobile 2 Balancing

More information

Introduction. Prof. Lina Karam School of Electrical, Computer, & Energy Engineering Arizona State University

Introduction. Prof. Lina Karam School of Electrical, Computer, & Energy Engineering Arizona State University EEE 508 - Digital Image & Video Processing and Compression http://lina.faculty.asu.edu/eee508/ Introduction Prof. Lina Karam School of Electrical, Computer, & Energy Engineering Arizona State University

More information

Design of Temporally Dithered Codes for Increased Depth of Field in Structured Light Systems

Design of Temporally Dithered Codes for Increased Depth of Field in Structured Light Systems Design of Temporally Dithered Codes for Increased Depth of Field in Structured Light Systems Ricardo R. Garcia University of California, Berkeley Berkeley, CA rrgarcia@eecs.berkeley.edu Abstract In recent

More information

Dappled Photography: Mask Enhanced Cameras for Heterodyned Light Fields and Coded Aperture Refocusing

Dappled Photography: Mask Enhanced Cameras for Heterodyned Light Fields and Coded Aperture Refocusing Dappled Photography: Mask Enhanced Cameras for Heterodyned Light Fields and Coded Aperture Refocusing Ashok Veeraraghavan, Ramesh Raskar, Ankit Mohan & Jack Tumblin Amit Agrawal, Mitsubishi Electric Research

More information

Computational Cameras. Rahul Raguram COMP

Computational Cameras. Rahul Raguram COMP Computational Cameras Rahul Raguram COMP 790-090 What is a computational camera? Camera optics Camera sensor 3D scene Traditional camera Final image Modified optics Camera sensor Image Compute 3D scene

More information

NTU CSIE. Advisor: Wu Ja Ling, Ph.D.

NTU CSIE. Advisor: Wu Ja Ling, Ph.D. An Interactive Background Blurring Mechanism and Its Applications NTU CSIE Yan Chih Yu Advisor: Wu Ja Ling, Ph.D. 1 2 Outline Introduction Related Work Method Object Segmentation Depth Map Generation Image

More information

WIDE SPECTRAL RANGE IMAGING INTERFEROMETER

WIDE SPECTRAL RANGE IMAGING INTERFEROMETER WIDE SPECTRAL RANGE IMAGING INTERFEROMETER Alessandro Barducci, Donatella Guzzi, Cinzia Lastri, Paolo Marcoionni, Vanni Nardino, Ivan Pippi CNR IFAC Sesto Fiorentino, ITALY ICSO 2012 Ajaccio 8-12/10/2012

More information

High Resolution Spectral Video Capture & Computational Photography Xun Cao ( 曹汛 )

High Resolution Spectral Video Capture & Computational Photography Xun Cao ( 曹汛 ) High Resolution Spectral Video Capture & Computational Photography Xun Cao ( 曹汛 ) School of Electronic Science & Engineering Nanjing University caoxun@nju.edu.cn Dec 30th, 2015 Computational Photography

More information

Midterm Examination CS 534: Computational Photography

Midterm Examination CS 534: Computational Photography Midterm Examination CS 534: Computational Photography November 3, 2015 NAME: SOLUTIONS Problem Score Max Score 1 8 2 8 3 9 4 4 5 3 6 4 7 6 8 13 9 7 10 4 11 7 12 10 13 9 14 8 Total 100 1 1. [8] What are

More information

Study guide for Graduate Computer Vision

Study guide for Graduate Computer Vision Study guide for Graduate Computer Vision Erik G. Learned-Miller Department of Computer Science University of Massachusetts, Amherst Amherst, MA 01003 November 23, 2011 Abstract 1 1. Know Bayes rule. What

More information

EC-433 Digital Image Processing

EC-433 Digital Image Processing EC-433 Digital Image Processing Lecture 2 Digital Image Fundamentals Dr. Arslan Shaukat 1 Fundamental Steps in DIP Image Acquisition An image is captured by a sensor (such as a monochrome or color TV camera)

More information

Lecture 18: Light field cameras. (plenoptic cameras) Visual Computing Systems CMU , Fall 2013

Lecture 18: Light field cameras. (plenoptic cameras) Visual Computing Systems CMU , Fall 2013 Lecture 18: Light field cameras (plenoptic cameras) Visual Computing Systems Continuing theme: computational photography Cameras capture light, then extensive processing produces the desired image Today:

More information

Demosaicing and Denoising on Simulated Light Field Images

Demosaicing and Denoising on Simulated Light Field Images Demosaicing and Denoising on Simulated Light Field Images Trisha Lian Stanford University tlian@stanford.edu Kyle Chiang Stanford University kchiang@stanford.edu Abstract Light field cameras use an array

More information

Coded photography , , Computational Photography Fall 2018, Lecture 14

Coded photography , , Computational Photography Fall 2018, Lecture 14 Coded photography http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2018, Lecture 14 Overview of today s lecture The coded photography paradigm. Dealing with

More information

Implementation of Adaptive Coded Aperture Imaging using a Digital Micro-Mirror Device for Defocus Deblurring

Implementation of Adaptive Coded Aperture Imaging using a Digital Micro-Mirror Device for Defocus Deblurring Implementation of Adaptive Coded Aperture Imaging using a Digital Micro-Mirror Device for Defocus Deblurring Ashill Chiranjan and Bernardt Duvenhage Defence, Peace, Safety and Security Council for Scientific

More information

A Mathematical model for the determination of distance of an object in a 2D image

A Mathematical model for the determination of distance of an object in a 2D image A Mathematical model for the determination of distance of an object in a 2D image Deepu R 1, Murali S 2,Vikram Raju 3 Maharaja Institute of Technology Mysore, Karnataka, India rdeepusingh@mitmysore.in

More information

Near-Invariant Blur for Depth and 2D Motion via Time-Varying Light Field Analysis

Near-Invariant Blur for Depth and 2D Motion via Time-Varying Light Field Analysis Near-Invariant Blur for Depth and 2D Motion via Time-Varying Light Field Analysis Yosuke Bando 1,2 Henry Holtzman 2 Ramesh Raskar 2 1 Toshiba Corporation 2 MIT Media Lab Defocus & Motion Blur PSF Depth

More information

Dynamically Reparameterized Light Fields & Fourier Slice Photography. Oliver Barth, 2009 Max Planck Institute Saarbrücken

Dynamically Reparameterized Light Fields & Fourier Slice Photography. Oliver Barth, 2009 Max Planck Institute Saarbrücken Dynamically Reparameterized Light Fields & Fourier Slice Photography Oliver Barth, 2009 Max Planck Institute Saarbrücken Background What we are talking about? 2 / 83 Background What we are talking about?

More information

Why learn about photography in this course?

Why learn about photography in this course? Why learn about photography in this course? Geri's Game: Note the background is blurred. - photography: model of image formation - Many computer graphics methods use existing photographs e.g. texture &

More information

Coded photography , , Computational Photography Fall 2017, Lecture 18

Coded photography , , Computational Photography Fall 2017, Lecture 18 Coded photography http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2017, Lecture 18 Course announcements Homework 5 delayed for Tuesday. - You will need cameras

More information

lecture 24 image capture - photography: model of image formation - image blur - camera settings (f-number, shutter speed) - exposure - camera response

lecture 24 image capture - photography: model of image formation - image blur - camera settings (f-number, shutter speed) - exposure - camera response lecture 24 image capture - photography: model of image formation - image blur - camera settings (f-number, shutter speed) - exposure - camera response - application: high dynamic range imaging Why learn

More information

6.A44 Computational Photography

6.A44 Computational Photography Add date: Friday 6.A44 Computational Photography Depth of Field Frédo Durand We allow for some tolerance What happens when we close the aperture by two stop? Aperture diameter is divided by two is doubled

More information

Module 3: Video Sampling Lecture 18: Filtering operations in Camera and display devices. The Lecture Contains: Effect of Temporal Aperture:

Module 3: Video Sampling Lecture 18: Filtering operations in Camera and display devices. The Lecture Contains: Effect of Temporal Aperture: The Lecture Contains: Effect of Temporal Aperture: Spatial Aperture: Effect of Display Aperture: file:///d /...e%20(ganesh%20rana)/my%20course_ganesh%20rana/prof.%20sumana%20gupta/final%20dvsp/lecture18/18_1.htm[12/30/2015

More information

ROBOT VISION. Dr.M.Madhavi, MED, MVSREC

ROBOT VISION. Dr.M.Madhavi, MED, MVSREC ROBOT VISION Dr.M.Madhavi, MED, MVSREC Robotic vision may be defined as the process of acquiring and extracting information from images of 3-D world. Robotic vision is primarily targeted at manipulation

More information

A moment-preserving approach for depth from defocus

A moment-preserving approach for depth from defocus A moment-preserving approach for depth from defocus D. M. Tsai and C. T. Lin Machine Vision Lab. Department of Industrial Engineering and Management Yuan-Ze University, Chung-Li, Taiwan, R.O.C. E-mail:

More information

ME 6406 MACHINE VISION. Georgia Institute of Technology

ME 6406 MACHINE VISION. Georgia Institute of Technology ME 6406 MACHINE VISION Georgia Institute of Technology Class Information Instructor Professor Kok-Meng Lee MARC 474 Office hours: Tues/Thurs 1:00-2:00 pm kokmeng.lee@me.gatech.edu (404)-894-7402 Class

More information

Light field sensing. Marc Levoy. Computer Science Department Stanford University

Light field sensing. Marc Levoy. Computer Science Department Stanford University Light field sensing Marc Levoy Computer Science Department Stanford University The scalar light field (in geometrical optics) Radiance as a function of position and direction in a static scene with fixed

More information

La photographie numérique. Frank NIELSEN Lundi 7 Juin 2010

La photographie numérique. Frank NIELSEN Lundi 7 Juin 2010 La photographie numérique Frank NIELSEN Lundi 7 Juin 2010 1 Le Monde digital Key benefits of the analog2digital paradigm shift? Dissociate contents from support : binarize Universal player (CPU, Turing

More information

Computational Approaches to Cameras

Computational Approaches to Cameras Computational Approaches to Cameras 11/16/17 Magritte, The False Mirror (1935) Computational Photography Derek Hoiem, University of Illinois Announcements Final project proposal due Monday (see links on

More information

Dr F. Cuzzolin 1. September 29, 2015

Dr F. Cuzzolin 1. September 29, 2015 P00407 Principles of Computer Vision 1 1 Department of Computing and Communication Technologies Oxford Brookes University, UK September 29, 2015 September 29, 2015 1 / 73 Outline of the Lecture 1 2 Basics

More information

Overview. Pinhole camera model Projective geometry Vanishing points and lines Projection matrix Cameras with Lenses Color Digital image

Overview. Pinhole camera model Projective geometry Vanishing points and lines Projection matrix Cameras with Lenses Color Digital image Camera & Color Overview Pinhole camera model Projective geometry Vanishing points and lines Projection matrix Cameras with Lenses Color Digital image Book: Hartley 6.1, Szeliski 2.1.5, 2.2, 2.3 The trip

More information

Camera Requirements For Precision Agriculture

Camera Requirements For Precision Agriculture Camera Requirements For Precision Agriculture Radiometric analysis such as NDVI requires careful acquisition and handling of the imagery to provide reliable values. In this guide, we explain how Pix4Dmapper

More information

Unit 1: Image Formation

Unit 1: Image Formation Unit 1: Image Formation 1. Geometry 2. Optics 3. Photometry 4. Sensor Readings Szeliski 2.1-2.3 & 6.3.5 1 Physical parameters of image formation Geometric Type of projection Camera pose Optical Sensor

More information

Ron Liu OPTI521-Introductory Optomechanical Engineering December 7, 2009

Ron Liu OPTI521-Introductory Optomechanical Engineering December 7, 2009 Synopsis of METHOD AND APPARATUS FOR IMPROVING VISION AND THE RESOLUTION OF RETINAL IMAGES by David R. Williams and Junzhong Liang from the US Patent Number: 5,777,719 issued in July 7, 1998 Ron Liu OPTI521-Introductory

More information

Deconvolution , , Computational Photography Fall 2018, Lecture 12

Deconvolution , , Computational Photography Fall 2018, Lecture 12 Deconvolution http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2018, Lecture 12 Course announcements Homework 3 is out. - Due October 12 th. - Any questions?

More information

Light-Field Database Creation and Depth Estimation

Light-Field Database Creation and Depth Estimation Light-Field Database Creation and Depth Estimation Abhilash Sunder Raj abhisr@stanford.edu Michael Lowney mlowney@stanford.edu Raj Shah shahraj@stanford.edu Abstract Light-field imaging research has been

More information

Privacy Preserving Optics for Miniature Vision Sensors

Privacy Preserving Optics for Miniature Vision Sensors Privacy Preserving Optics for Miniature Vision Sensors Francesco Pittaluga and Sanjeev J. Koppal University of Florida, Electrical and Computer Engineering Dept. 216 Larsen Hall Gainesville, FL 32611-6200

More information

OFFSET AND NOISE COMPENSATION

OFFSET AND NOISE COMPENSATION OFFSET AND NOISE COMPENSATION AO 10V 8.1 Offset and fixed pattern noise reduction Offset variation - shading AO 10V 8.2 Row Noise AO 10V 8.3 Offset compensation Global offset calibration Dark level is

More information

LENSLESS IMAGING BY COMPRESSIVE SENSING

LENSLESS IMAGING BY COMPRESSIVE SENSING LENSLESS IMAGING BY COMPRESSIVE SENSING Gang Huang, Hong Jiang, Kim Matthews and Paul Wilford Bell Labs, Alcatel-Lucent, Murray Hill, NJ 07974 ABSTRACT In this paper, we propose a lensless compressive

More information

Deconvolution , , Computational Photography Fall 2017, Lecture 17

Deconvolution , , Computational Photography Fall 2017, Lecture 17 Deconvolution http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2017, Lecture 17 Course announcements Homework 4 is out. - Due October 26 th. - There was another

More information

Coding and Modulation in Cameras

Coding and Modulation in Cameras Coding and Modulation in Cameras Amit Agrawal June 2010 Mitsubishi Electric Research Labs (MERL) Cambridge, MA, USA Coded Computational Imaging Agrawal, Veeraraghavan, Narasimhan & Mohan Schedule Introduction

More information

VC 16/17 TP2 Image Formation

VC 16/17 TP2 Image Formation VC 16/17 TP2 Image Formation Mestrado em Ciência de Computadores Mestrado Integrado em Engenharia de Redes e Sistemas Informáticos Hélder Filipe Pinto de Oliveira Outline Computer Vision? The Human Visual

More information

Spectral and Polarization Configuration Guide for MS Series 3-CCD Cameras

Spectral and Polarization Configuration Guide for MS Series 3-CCD Cameras Spectral and Polarization Configuration Guide for MS Series 3-CCD Cameras Geospatial Systems, Inc (GSI) MS 3100/4100 Series 3-CCD cameras utilize a color-separating prism to split broadband light entering

More information

HDR videos acquisition

HDR videos acquisition HDR videos acquisition dr. Francesco Banterle francesco.banterle@isti.cnr.it How to capture? Videos are challenging: We need to capture multiple frames at different exposure times and everything moves

More information

Exercise questions for Machine vision

Exercise questions for Machine vision Exercise questions for Machine vision This is a collection of exercise questions. These questions are all examination alike which means that similar questions may appear at the written exam. I ve divided

More information

IMAGE FORMATION. Light source properties. Sensor characteristics Surface. Surface reflectance properties. Optics

IMAGE FORMATION. Light source properties. Sensor characteristics Surface. Surface reflectance properties. Optics IMAGE FORMATION Light source properties Sensor characteristics Surface Exposure shape Optics Surface reflectance properties ANALOG IMAGES An image can be understood as a 2D light intensity function f(x,y)

More information

VC 14/15 TP2 Image Formation

VC 14/15 TP2 Image Formation VC 14/15 TP2 Image Formation Mestrado em Ciência de Computadores Mestrado Integrado em Engenharia de Redes e Sistemas Informáticos Miguel Tavares Coimbra Outline Computer Vision? The Human Visual System

More information

Continuous Flash. October 1, Technical Report MSR-TR Microsoft Research Microsoft Corporation One Microsoft Way Redmond, WA 98052

Continuous Flash. October 1, Technical Report MSR-TR Microsoft Research Microsoft Corporation One Microsoft Way Redmond, WA 98052 Continuous Flash Hugues Hoppe Kentaro Toyama October 1, 2003 Technical Report MSR-TR-2003-63 Microsoft Research Microsoft Corporation One Microsoft Way Redmond, WA 98052 Page 1 of 7 Abstract To take a

More information

Sensors and Sensing Cameras and Camera Calibration

Sensors and Sensing Cameras and Camera Calibration Sensors and Sensing Cameras and Camera Calibration Todor Stoyanov Mobile Robotics and Olfaction Lab Center for Applied Autonomous Sensor Systems Örebro University, Sweden todor.stoyanov@oru.se 20.11.2014

More information

Computer Vision. The Pinhole Camera Model

Computer Vision. The Pinhole Camera Model Computer Vision The Pinhole Camera Model Filippo Bergamasco (filippo.bergamasco@unive.it) http://www.dais.unive.it/~bergamasco DAIS, Ca Foscari University of Venice Academic year 2017/2018 Imaging device

More information

Coded Aperture for Projector and Camera for Robust 3D measurement

Coded Aperture for Projector and Camera for Robust 3D measurement Coded Aperture for Projector and Camera for Robust 3D measurement Yuuki Horita Yuuki Matugano Hiroki Morinaga Hiroshi Kawasaki Satoshi Ono Makoto Kimura Yasuo Takane Abstract General active 3D measurement

More information

Camera Requirements For Precision Agriculture

Camera Requirements For Precision Agriculture Camera Requirements For Precision Agriculture Radiometric analysis such as NDVI requires careful acquisition and handling of the imagery to provide reliable values. In this guide, we explain how Pix4Dmapper

More information

Coded Computational Photography!

Coded Computational Photography! Coded Computational Photography! EE367/CS448I: Computational Imaging and Display! stanford.edu/class/ee367! Lecture 9! Gordon Wetzstein! Stanford University! Coded Computational Photography - Overview!!

More information

PROCEEDINGS OF SPIE. Measurement of low-order aberrations with an autostigmatic microscope

PROCEEDINGS OF SPIE. Measurement of low-order aberrations with an autostigmatic microscope PROCEEDINGS OF SPIE SPIEDigitalLibrary.org/conference-proceedings-of-spie Measurement of low-order aberrations with an autostigmatic microscope William P. Kuhn Measurement of low-order aberrations with

More information

Bias errors in PIV: the pixel locking effect revisited.

Bias errors in PIV: the pixel locking effect revisited. Bias errors in PIV: the pixel locking effect revisited. E.F.J. Overmars 1, N.G.W. Warncke, C. Poelma and J. Westerweel 1: Laboratory for Aero & Hydrodynamics, University of Technology, Delft, The Netherlands,

More information

Single-Image Shape from Defocus

Single-Image Shape from Defocus Single-Image Shape from Defocus José R.A. Torreão and João L. Fernandes Instituto de Computação Universidade Federal Fluminense 24210-240 Niterói RJ, BRAZIL Abstract The limited depth of field causes scene

More information

Digital Image Processing

Digital Image Processing What is an image? Digital Image Processing Picture, Photograph Visual data Usually two- or three-dimensional What is a digital image? An image which is discretized, i.e., defined on a discrete grid (ex.

More information

CHARGE-COUPLED DEVICE (CCD)

CHARGE-COUPLED DEVICE (CCD) CHARGE-COUPLED DEVICE (CCD) Definition A charge-coupled device (CCD) is an analog shift register, enabling analog signals, usually light, manipulation - for example, conversion into a digital value that

More information

Multi-aperture camera module with 720presolution

Multi-aperture camera module with 720presolution Multi-aperture camera module with 720presolution using microoptics A. Brückner, A. Oberdörster, J. Dunkel, A. Reimann, F. Wippermann, A. Bräuer Fraunhofer Institute for Applied Optics and Precision Engineering

More information

Acquisition and representation of images

Acquisition and representation of images Acquisition and representation of images Stefano Ferrari Università degli Studi di Milano stefano.ferrari@unimi.it Methods for mage Processing academic year 2017 2018 Electromagnetic radiation λ = c ν

More information

Camera Image Processing Pipeline: Part II

Camera Image Processing Pipeline: Part II Lecture 13: Camera Image Processing Pipeline: Part II Visual Computing Systems Today Finish image processing pipeline Auto-focus / auto-exposure Camera processing elements Smart phone processing elements

More information

Project 4 Results http://www.cs.brown.edu/courses/cs129/results/proj4/jcmace/ http://www.cs.brown.edu/courses/cs129/results/proj4/damoreno/ http://www.cs.brown.edu/courses/csci1290/results/proj4/huag/

More information

Background. Computer Vision & Digital Image Processing. Improved Bartlane transmitted image. Example Bartlane transmitted image

Background. Computer Vision & Digital Image Processing. Improved Bartlane transmitted image. Example Bartlane transmitted image Background Computer Vision & Digital Image Processing Introduction to Digital Image Processing Interest comes from two primary backgrounds Improvement of pictorial information for human perception How

More information

Depth from Diffusion

Depth from Diffusion Depth from Diffusion Changyin Zhou Oliver Cossairt Shree Nayar Columbia University Supported by ONR Optical Diffuser Optical Diffuser ~ 10 micron Micrograph of a Holographic Diffuser (RPC Photonics) [Gray,

More information

Compact Dual Field-of-View Telescope for Small Satellite Payloads

Compact Dual Field-of-View Telescope for Small Satellite Payloads Compact Dual Field-of-View Telescope for Small Satellite Payloads James C. Peterson Space Dynamics Laboratory 1695 North Research Park Way, North Logan, UT 84341; 435-797-4624 Jim.Peterson@sdl.usu.edu

More information

LENSES. INEL 6088 Computer Vision

LENSES. INEL 6088 Computer Vision LENSES INEL 6088 Computer Vision Digital camera A digital camera replaces film with a sensor array Each cell in the array is a Charge Coupled Device light-sensitive diode that converts photons to electrons

More information

The ultimate camera. Computational Photography. Creating the ultimate camera. The ultimate camera. What does it do?

The ultimate camera. Computational Photography. Creating the ultimate camera. The ultimate camera. What does it do? Computational Photography The ultimate camera What does it do? Image from Durand & Freeman s MIT Course on Computational Photography Today s reading Szeliski Chapter 9 The ultimate camera Infinite resolution

More information

Sampling Efficiency in Digital Camera Performance Standards

Sampling Efficiency in Digital Camera Performance Standards Copyright 2008 SPIE and IS&T. This paper was published in Proc. SPIE Vol. 6808, (2008). It is being made available as an electronic reprint with permission of SPIE and IS&T. One print or electronic copy

More information

MINIATURE X-RAY SOURCES AND THE EFFECTS OF SPOT SIZE ON SYSTEM PERFORMANCE

MINIATURE X-RAY SOURCES AND THE EFFECTS OF SPOT SIZE ON SYSTEM PERFORMANCE 228 MINIATURE X-RAY SOURCES AND THE EFFECTS OF SPOT SIZE ON SYSTEM PERFORMANCE D. CARUSO, M. DINSMORE TWX LLC, CONCORD, MA 01742 S. CORNABY MOXTEK, OREM, UT 84057 ABSTRACT Miniature x-ray sources present

More information

HDR images acquisition

HDR images acquisition HDR images acquisition dr. Francesco Banterle francesco.banterle@isti.cnr.it Current sensors No sensors available to consumer for capturing HDR content in a single shot Some native HDR sensors exist, HDRc

More information

Lenses, exposure, and (de)focus

Lenses, exposure, and (de)focus Lenses, exposure, and (de)focus http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2017, Lecture 15 Course announcements Homework 4 is out. - Due October 26

More information

Introduction. Related Work

Introduction. Related Work Introduction Depth of field is a natural phenomenon when it comes to both sight and photography. The basic ray tracing camera model is insufficient at representing this essential visual element and will

More information

New applications of Spectral Edge image fusion

New applications of Spectral Edge image fusion New applications of Spectral Edge image fusion Alex E. Hayes a,b, Roberto Montagna b, and Graham D. Finlayson a,b a Spectral Edge Ltd, Cambridge, UK. b University of East Anglia, Norwich, UK. ABSTRACT

More information

Computer Vision. Howie Choset Introduction to Robotics

Computer Vision. Howie Choset   Introduction to Robotics Computer Vision Howie Choset http://www.cs.cmu.edu.edu/~choset Introduction to Robotics http://generalrobotics.org What is vision? What is computer vision? Edge Detection Edge Detection Interest points

More information

CS534 Introduction to Computer Vision. Linear Filters. Ahmed Elgammal Dept. of Computer Science Rutgers University

CS534 Introduction to Computer Vision. Linear Filters. Ahmed Elgammal Dept. of Computer Science Rutgers University CS534 Introduction to Computer Vision Linear Filters Ahmed Elgammal Dept. of Computer Science Rutgers University Outlines What are Filters Linear Filters Convolution operation Properties of Linear Filters

More information

Image acquisition. In both cases, the digital sensing element is one of the following: Line array Area array. Single sensor

Image acquisition. In both cases, the digital sensing element is one of the following: Line array Area array. Single sensor Image acquisition Digital images are acquired by direct digital acquisition (digital still/video cameras), or scanning material acquired as analog signals (slides, photographs, etc.). In both cases, the

More information

CSE Tue 10/09. Nadir Weibel

CSE Tue 10/09. Nadir Weibel CSE 118 - Tue 10/09 Nadir Weibel Today Admin Teams Assignments, grading, submissions Mini Quiz on Week 1 (readings and class material) Low-Fidelity Prototyping 1st Project Assignment Computer Vision, Kinect,

More information

VC 11/12 T2 Image Formation

VC 11/12 T2 Image Formation VC 11/12 T2 Image Formation Mestrado em Ciência de Computadores Mestrado Integrado em Engenharia de Redes e Sistemas Informáticos Miguel Tavares Coimbra Outline Computer Vision? The Human Visual System

More information

Single Camera Catadioptric Stereo System

Single Camera Catadioptric Stereo System Single Camera Catadioptric Stereo System Abstract In this paper, we present a framework for novel catadioptric stereo camera system that uses a single camera and a single lens with conic mirrors. Various

More information

Day&Night Box camera with 36x Optical Zoom WV-CZ392/CZ492

Day&Night Box camera with 36x Optical Zoom WV-CZ392/CZ492 Day&Night Box camera with 36x Optical Zoom WV-CZ392/CZ492 WV-CZ392 WV-CZ492 2011.Sep.6 Security & AV Systems Business Unit Panasonic System Networks Company Key Features 1 Day&Night Box camera with 36x

More information

Low Cost Earth Sensor based on Oxygen Airglow

Low Cost Earth Sensor based on Oxygen Airglow Assessment Executive Summary Date : 16.06.2008 Page: 1 of 7 Low Cost Earth Sensor based on Oxygen Airglow Executive Summary Prepared by: H. Shea EPFL LMTS herbert.shea@epfl.ch EPFL Lausanne Switzerland

More information

Fabrication Methodology of microlenses for stereoscopic imagers using standard CMOS process. R. P. Rocha, J. P. Carmo, and J. H.

Fabrication Methodology of microlenses for stereoscopic imagers using standard CMOS process. R. P. Rocha, J. P. Carmo, and J. H. Fabrication Methodology of microlenses for stereoscopic imagers using standard CMOS process R. P. Rocha, J. P. Carmo, and J. H. Correia Department of Industrial Electronics, University of Minho, Campus

More information

Wavefront coding. Refocusing & Light Fields. Wavefront coding. Final projects. Is depth of field a blur? Frédo Durand Bill Freeman MIT - EECS

Wavefront coding. Refocusing & Light Fields. Wavefront coding. Final projects. Is depth of field a blur? Frédo Durand Bill Freeman MIT - EECS 6.098 Digital and Computational Photography 6.882 Advanced Computational Photography Final projects Send your slides by noon on Thrusday. Send final report Refocusing & Light Fields Frédo Durand Bill Freeman

More information

PLazeR. a planar laser rangefinder. Robert Ying (ry2242) Derek Xingzhou He (xh2187) Peiqian Li (pl2521) Minh Trang Nguyen (mnn2108)

PLazeR. a planar laser rangefinder. Robert Ying (ry2242) Derek Xingzhou He (xh2187) Peiqian Li (pl2521) Minh Trang Nguyen (mnn2108) PLazeR a planar laser rangefinder Robert Ying (ry2242) Derek Xingzhou He (xh2187) Peiqian Li (pl2521) Minh Trang Nguyen (mnn2108) Overview & Motivation Detecting the distance between a sensor and objects

More information

Study of self-interference incoherent digital holography for the application of retinal imaging

Study of self-interference incoherent digital holography for the application of retinal imaging Study of self-interference incoherent digital holography for the application of retinal imaging Jisoo Hong and Myung K. Kim Department of Physics, University of South Florida, Tampa, FL, US 33620 ABSTRACT

More information

TSBB09 Image Sensors 2018-HT2. Image Formation Part 1

TSBB09 Image Sensors 2018-HT2. Image Formation Part 1 TSBB09 Image Sensors 2018-HT2 Image Formation Part 1 Basic physics Electromagnetic radiation consists of electromagnetic waves With energy That propagate through space The waves consist of transversal

More information

Cardinal Points of an Optical System--and Other Basic Facts

Cardinal Points of an Optical System--and Other Basic Facts Cardinal Points of an Optical System--and Other Basic Facts The fundamental feature of any optical system is the aperture stop. Thus, the most fundamental optical system is the pinhole camera. The image

More information

Single-shot three-dimensional imaging of dilute atomic clouds

Single-shot three-dimensional imaging of dilute atomic clouds Calhoun: The NPS Institutional Archive Faculty and Researcher Publications Funded by Naval Postgraduate School 2014 Single-shot three-dimensional imaging of dilute atomic clouds Sakmann, Kaspar http://hdl.handle.net/10945/52399

More information

BIG PIXELS VS. SMALL PIXELS THE OPTICAL BOTTLENECK. Gregory Hollows Edmund Optics

BIG PIXELS VS. SMALL PIXELS THE OPTICAL BOTTLENECK. Gregory Hollows Edmund Optics BIG PIXELS VS. SMALL PIXELS THE OPTICAL BOTTLENECK Gregory Hollows Edmund Optics 1 IT ALL STARTS WITH THE SENSOR We have to begin with sensor technology to understand the road map Resolution will continue

More information

High Dynamic Range Imaging

High Dynamic Range Imaging High Dynamic Range Imaging 1 2 Lecture Topic Discuss the limits of the dynamic range in current imaging and display technology Solutions 1. High Dynamic Range (HDR) Imaging Able to image a larger dynamic

More information

General Imaging System

General Imaging System General Imaging System Lecture Slides ME 4060 Machine Vision and Vision-based Control Chapter 5 Image Sensing and Acquisition By Dr. Debao Zhou 1 2 Light, Color, and Electromagnetic Spectrum Penetrate

More information

Sensing Increased Image Resolution Using Aperture Masks

Sensing Increased Image Resolution Using Aperture Masks Sensing Increased Image Resolution Using Aperture Masks Ankit Mohan, Xiang Huang, Jack Tumblin Northwestern University Ramesh Raskar MIT Media Lab CVPR 2008 Supplemental Material Contributions Achieve

More information

Realistic Image Synthesis

Realistic Image Synthesis Realistic Image Synthesis - HDR Capture & Tone Mapping - Philipp Slusallek Karol Myszkowski Gurprit Singh Karol Myszkowski LDR vs HDR Comparison Various Dynamic Ranges (1) 10-6 10-4 10-2 100 102 104 106

More information

December 28, Dr. Praveen Sankaran (Department of ECE NIT Calicut DIP)

December 28, Dr. Praveen Sankaran (Department of ECE NIT Calicut DIP) Dr. Praveen Sankaran Department of ECE NIT Calicut December 28, 2012 Winter 2013 December 28, 2012 1 / 18 Outline 1 Piecewise-Linear Functions Review 2 Histogram Processing Winter 2013 December 28, 2012

More information

A Review over Different Blur Detection Techniques in Image Processing

A Review over Different Blur Detection Techniques in Image Processing A Review over Different Blur Detection Techniques in Image Processing 1 Anupama Sharma, 2 Devarshi Shukla 1 E.C.E student, 2 H.O.D, Department of electronics communication engineering, LR College of engineering

More information

SUPER RESOLUTION INTRODUCTION

SUPER RESOLUTION INTRODUCTION SUPER RESOLUTION Jnanavardhini - Online MultiDisciplinary Research Journal Ms. Amalorpavam.G Assistant Professor, Department of Computer Sciences, Sambhram Academy of Management. Studies, Bangalore Abstract:-

More information

technology meets pathology Institute of Pathology, Charité Universitätsmedizin Berlin, Berlin, Germany 3 Overview

technology meets pathology Institute of Pathology, Charité Universitätsmedizin Berlin, Berlin, Germany 3 Overview ASSESSMENT OF TECHNICAL PARAMETERS A. Alekseychuk 1, N. Zerbe 2, Y. Yagi 3 1 Computer Vision and Remote Sensing, TU Berlin, Berlin, Germany 2 Institute of Pathology, Charité Universitätsmedizin Berlin,

More information

Image Restoration and Super- Resolution

Image Restoration and Super- Resolution Image Restoration and Super- Resolution Manjunath V. Joshi Professor Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, Gujarat email:mv_joshi@daiict.ac.in Overview Image

More information

Optical Engineering 421/521 Sample Questions for Midterm 1

Optical Engineering 421/521 Sample Questions for Midterm 1 Optical Engineering 421/521 Sample Questions for Midterm 1 Short answer 1.) Sketch a pechan prism. Name a possible application of this prism., write the mirror matrix for this prism (or any other common

More information

Selection of Temporally Dithered Codes for Increasing Virtual Depth of Field in Structured Light Systems

Selection of Temporally Dithered Codes for Increasing Virtual Depth of Field in Structured Light Systems Selection of Temporally Dithered Codes for Increasing Virtual Depth of Field in Structured Light Systems Abstract Temporally dithered codes have recently been used for depth reconstruction of fast dynamic

More information