The document discusses a class on computational cameras and photography. It provides an overview of topics to be covered in the class including dual photography, synthetic lighting, image-based relighting, and emerging sensors. It also outlines assignments, homework, and potential final project ideas involving novel uses of cameras, illumination, and computational photography techniques.
Though revolutionary in many ways, digital photography is essentially electronically implemented film photography. By contrast, computational photography exploits plentiful low-cost computing and memory, new kinds of digitally enabled sensors, optics, probes, smart lighting, and communication to capture information far beyond just a simple set of pixels. It promises a richer, even a multilayered, visual experience that may include depth, fused photo-video representations, or multispectral imagery. Professor Raskar will discuss and demonstrate advances he is working on in the areas of generalized optics, sensors, illumination methods, processing, and display, and describe how computational photography will enable us to create images that break from traditional constraints to retain more fully our fondest and most important memories, to keep personalized records of our lives, and to extend both the archival and the artistic possibilities of photography.
We have built a camera that can look around corners and beyond the line of sight. The camera uses light that travels from the object to the camera indirectly, by reflecting off walls or other obstacles, to reconstruct a 3D shape.
Though revolutionary in many ways, digital photography is essentially electronically implemented film photography. By contrast, computational photography exploits plentiful low-cost computing and memory, new kinds of digitally enabled sensors, optics, probes, smart lighting, and communication to capture information far beyond just a simple set of pixels. It promises a richer, even a multilayered, visual experience that may include depth, fused photo-video representations, or multispectral imagery. Professor Raskar will discuss and demonstrate advances he is working on in the areas of generalized optics, sensors, illumination methods, processing, and display, and describe how computational photography will enable us to create images that break from traditional constraints to retain more fully our fondest and most important memories, to keep personalized records of our lives, and to extend both the archival and the artistic possibilities of photography.
We have built a camera that can look around corners and beyond the line of sight. The camera uses light that travels from the object to the camera indirectly, by reflecting off walls or other obstacles, to reconstruct a 3D shape.
Night vision technology, by definition, literally allows one to see in the dark. Originally
developed for military use, it has provided the United States with a strategic military
advantage, the value of which can be measured in lives. Federal and state agencies now
routinely utilize the technology for site security, surveillance as well as search and
rescue. Night vision equipment has evolved from bulky optical instruments in lightweight
goggles through the advancement of image intensification technology.
COSC 426 Graduate class in Augmented Reality, lecture on AR tracking. Taught by Mark Billinghurst of the HIT Lab NZ at the University of Canterbury, July 25th 2012
Troubleshooting, Designing & Installing Digital & Analog Closed Circuit TV Sy...Living Online
The objective of this workshop is to give you practical know-how in designing, installing, commissioning, maintaining and troubleshooting analog and digital CCTV systems. The poor quality of CCTV images often seen doesn't inspire much confidence in the technology. However the purpose of this workshop is to ensure you apply best practice in all your work with CCTV systems. With the past terrorist outrages in London and other cities, CCTV systems have been essential as a key tool in fighting crime. And have perhaps shifted from being part of "Big Brother" to a useful tool. CCTV systems have undergone a remarkable technology transformation in the past decade from analog to digital and in operating on a wireless or cabled network, with a host of additional features. This has made the design and maintenance considerably more complex.
This workshop thus provides you with useful expertise in building and maintaining a high quality CCTV system. The workshop commences with a detailed review of the fundamentals; progressing to optics and TV systems. Modern CCTV cameras and monitors are then examined followed by a review of video processing equipment and analog video recording. The vital changes from the analog to digital world are then examined in considerable depth. The essentials of networking as applied to CCTV systems are then discussed with practical examples. The workshop is concluded with best practice in CCTV system design and commissioning and maintenance.
http://www.idc-online.com/content/troubleshooting-designing-and-installing-digital-and-analog-closed-circuit-tv-systems-25
Lytro Light Field Camera: from scientific research to a $50-million businessWeili Shi
I prepared these slides while I had somehow lost myself. Lytro and its story make one willing to believe again, those brave crazy ones who would like to change the world.
Digital Media for the Classroom
Part 2 of 2
This is the second part of the APOP workshop on how to use digital media creation in the classroom for a variety of subject matters.
How to do research, Idea Hexagon, Rank and Sparsity in imaging problems, Looking around corners, compressive sensing of periodic phenomena, 3D displays, fast computation
Night vision technology, by definition, literally allows one to see in the dark. Originally
developed for military use, it has provided the United States with a strategic military
advantage, the value of which can be measured in lives. Federal and state agencies now
routinely utilize the technology for site security, surveillance as well as search and
rescue. Night vision equipment has evolved from bulky optical instruments in lightweight
goggles through the advancement of image intensification technology.
COSC 426 Graduate class in Augmented Reality, lecture on AR tracking. Taught by Mark Billinghurst of the HIT Lab NZ at the University of Canterbury, July 25th 2012
Troubleshooting, Designing & Installing Digital & Analog Closed Circuit TV Sy...Living Online
The objective of this workshop is to give you practical know-how in designing, installing, commissioning, maintaining and troubleshooting analog and digital CCTV systems. The poor quality of CCTV images often seen doesn't inspire much confidence in the technology. However the purpose of this workshop is to ensure you apply best practice in all your work with CCTV systems. With the past terrorist outrages in London and other cities, CCTV systems have been essential as a key tool in fighting crime. And have perhaps shifted from being part of "Big Brother" to a useful tool. CCTV systems have undergone a remarkable technology transformation in the past decade from analog to digital and in operating on a wireless or cabled network, with a host of additional features. This has made the design and maintenance considerably more complex.
This workshop thus provides you with useful expertise in building and maintaining a high quality CCTV system. The workshop commences with a detailed review of the fundamentals; progressing to optics and TV systems. Modern CCTV cameras and monitors are then examined followed by a review of video processing equipment and analog video recording. The vital changes from the analog to digital world are then examined in considerable depth. The essentials of networking as applied to CCTV systems are then discussed with practical examples. The workshop is concluded with best practice in CCTV system design and commissioning and maintenance.
http://www.idc-online.com/content/troubleshooting-designing-and-installing-digital-and-analog-closed-circuit-tv-systems-25
Lytro Light Field Camera: from scientific research to a $50-million businessWeili Shi
I prepared these slides while I had somehow lost myself. Lytro and its story make one willing to believe again, those brave crazy ones who would like to change the world.
Digital Media for the Classroom
Part 2 of 2
This is the second part of the APOP workshop on how to use digital media creation in the classroom for a variety of subject matters.
How to do research, Idea Hexagon, Rank and Sparsity in imaging problems, Looking around corners, compressive sensing of periodic phenomena, 3D displays, fast computation
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to analyze how Light Field Technology is becoming economic feasible for an increasing number of applications. Light Field Cameras record all of the light fields in a picture instead of just one light field. This capability enables users to change the focus of pictures after they have been taken and to more easily record 3D data. These features are becoming economically feasible improvements because of rapid improvements in camera chips and micro-lens arrays (an example of micro-electronic mechanical systems, MEMS). These features offer alternative ways to do 3D sensing for automated vehicles and augmented reality and can enable faster data collection with telescopes.
These are the slides from the 3rd talk of our series on 19th July 2018, presented by Dr. Matt Edgar. This presents an overview of the research conducted within the Optics group in the School of Physics and Astronomy at the University of Glasgow.
Name - Aveek Gupta
Mechanical Engineering student form Surendra Institute of Engineering and management.
This was a Humanities assignment we had to do and present it in form of a seminar.
Feel free to download and use.
IJRET-V1I1P2 -A Survey Paper On Single Image and Video Dehazing MethodsISAR Publications
Most of the computer applications use digital images. Digital image processing acts an important
role in the analysis and interpretation of data, which is in the digital form. Images taken in foggy
weather condition often suffer from poor visibility and clarity. After the study of several fast
dehazing methods like Tan’s dehazing technique, Fattal’s dehazing technique and aiming Heat al
dehazing technique, Dark Channel Prior (DCP) intended by He et al is most substantive technique
for dehazing.This survey aims to study about various existing methods such as polarization, dark
channel prior, depth map based method etc. are used for dehazing.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2018-embedded-vision-summit-kanade
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Dr. Takeo Kanade, U.A. and Helen Whitaker Professor at Carnegie Mellon University, presents the "Think Like an Amateur, Do As an Expert: Lessons from a Career in Computer Vision" tutorial at the May 2018 Embedded Vision Summit.
In this keynote presentation, Dr. Kanade shares his experiences and lessons learned in developing a vast range of pioneering computer vision systems and autonomous robots, including face recognition, autonomously-driven cars, computer-assisted surgical robots, robot helicopters, biological live cell tracking and a system for sports broadcasts. Most researchers, when asked their fondest desire, respond that they want to do good research. If asked what constitutes “good research,” they often find it difficult to give a clear answer. For Dr. Kanade, good research derives from solving real-world problems, delivering useful results to society.
“Think like an amateur, do as an expert” is Dr. Kanade's research motto: When conceptualizing a problem and its possible solution, think simply and openly, as a novice in that field, without preconceived notions. When implementing a solution, on the other hand, do so thoroughly, meticulously and with expert skill. In his research projects, Dr. Kanade has met and worked with people from diverse backgrounds, and has encountered many challenges. While exploring the technical side of some of his most important projects, he also describes experiences that highlight the enjoyable aspects of a researcher’s life—those that have occurred accidentally or inevitably as his “Think like an amateur, do as an expert” approach has guided his interactions with problems and people.
ACM SIGGRAPH is delighted to present the 2017 Computer Graphics Achievement Award to Ramesh Raskar in recognition of his pioneering contributions to the fields of computational photography and light transport and for applying these technologies for social impact.
https://www.siggraph.org/about/awards/2017-cg-achievement-award-ramesh-raskar/
I recently gave a talk at ICCP 2015 and clarified that we should stop working on coded aperture for focus effects! (Thus negating my team's work in this area.). I also spoke about the lost decade of computational photography and how we have wasted too many years working on the wrong problems.
The way back to normal starts here
We all want to get out of the house. To reopen the economy. To feel secure again. Safe Paths builds tools that help communities flatten the curve of COVID-19 — together. CovidSafePaths.org
Video of the talk at https://www.youtube.com/watch?v=x9TCYuMUnco
Friction in data sharing is a large challenge for large scale machine learning. Emerging technologies in domains such as biomedicine, health and finance benefit from distributed deep learning methods which can allow multiple entities to train a deep neural network without requiring data sharing or resource aggregation at one single place. The talk will explore the main challenges in data friction that make capture, analysis and deployment of ML. The challenges include siloed and unstructured data, privacy and regulation of data sharing and incentive models for data transparent ecosystems. The talk will compare distributed deep learning methods of federated learning and split learning. Our team at MIT has pioneered a range of approaches including automated machine learning (AutoML), privacy preserving machine learning (PrivateML) and intrinsic as well as extrinsic data valuation (Data Markets). One of the programs at MIT aims to create a standard for data transparent ecosystems that can simultaneously address the privacy and utility of data.
Bio: Ramesh Raskar is an Associate Professor at MIT Media Lab and directs the Camera Culture research group. His focus is on AI and Imaging for health and sustainability. They span research in physical (e.g., sensors, health-tech), digital (e.g., automated and privacy-aware machine learning) and global (e.g., geomaps, autonomous mobility) domains. He received the Lemelson Award (2016), ACM SIGGRAPH Achievement Award (2017), DARPA Young Faculty Award (2009), Alfred P. Sloan Research Fellowship (2009), TR100 Award from MIT Technology Review (2004) and Global Industry Technovator Award (2003). He has worked on special research projects at Google [X], Apple Privacy Team and Facebook and co-founded/advised several companies. Project page https://splitlearning.github.io/" Ramesh Raskar is an Associate Professor at MIT Media Lab and directs the Camera Culture research group. His focus is on Machine Learning and Imaging for health and sustainability. They span research in physical (e.g., sensors, health-tech), digital (e.g., automated and privacy-aware machine learning) and global (e.g., geomaps, autonomous mobility) domains.
In his recent role at Facebook, he launched and led innovation teams in Digital Health, Health-tech, Satellite Imaging, TV and Bluetooth bandwidth for Connectivity, VR/AR and ‘Emerging Worlds’ initiative for FB.
At MIT, his co-inventions include camera to see around corners, femto-photography, automated machine learning (auto-ML), private ML, low-cost eye care devices (Netra,Catra, EyeSelfie), a novel CAT-Scan machine, motion capture (Prakash), long distance barcodes (Bokode), 3D interaction displays (BiDi screen), new theoretical models to augment light fields (ALF) to represent wave phenomena and algebraic rank constraints for 3D displays(HR3D).
Video: https://www.youtube.com/watch?v=2jq_5FaQbTg
After different rejections, the project of a lifetime Ramesh Raskar (associate professor at MIT) finally comes to life.
How did he manage to get his way out of this jungle of misleading signs and career traps? By becoming a pathfinder: always tense towards your goal but also critical and ready to adjust his strategy to reach it.
An incredible life lesson that he gave us in this talk at the last FAIL at Massachusetts Institute of Technology (MIT).
https://www.youtube.com/watch?v=2jq_5FaQbTg&feature=youtu.be&fbclid=IwAR3aAo7SIiCuHY_6ICTjXLOpNBUBwEEJUq72pD-V8N2nX2cWaVIxtPM1gBM
Ramesh Raskar is an Associate Professor at MIT Media Lab and directs the Camera Culture research group. His focus is on AI and Imaging for health and sustainability. These interfaces span research in physical (e.g., sensors, health-tech), digital (e.g., automating machine learning) and global (e.g., geomaps, autonomous mobility) domains. He received the Lemelson Award (2016), ACM SIGGRAPH Achievement Award (2017), DARPA Young Faculty Award (2009), Alfred P. Sloan Research Fellowship (2009), TR100 Award from MIT Technology Review (2004) and Global Indus Technovator Award (2003). He has worked on special research projects at Google [X] and Facebook and co-founded/advised several companies.
http://raskar.info or CameraCulture Wiki Page
How to come up w ideas: Idea Hexagon
How to write a paper
How to give a talk
Open research problems
How to decide merit of a project
How to attend a conference, brainstorm
Strive for Five
Before 5 teams
Be early, let others do details
Beyond 5 years
What no one is thinking about
Within 5 steps of Human Impact
Relevance
Beyond 5 mins of instruction
Deep, iterative, participatory
Fusing 5+ Expertise
Fun, barrier for others
Associate Professor, MIT Media Lab
Ramesh Raskar is founder of the Camera Culture research group at the Massachusetts Institute of Technology (MIT) Media Lab and associate professor of Media Arts and Sciences at MIT. Raskar is the co-inventor of radical imaging solutions including femto-photography, an ultra-fast imaging camera that can see around corners, low-cost eye-care solutions for the developing world and a camera that allows users to read pages of a book without opening the cover. He is a pioneer in the fields of imaging, computer vision and machine learning.
Raskar’s focus is on building interfaces between social systems and cyber-physical systems. These interfaces span research in physical (e.g., sensors, health-tech), digital (e.g., tools to enable keeping data private in distributed machine learning applications) and global (e.g., geomaps, autonomous mobility) domains. Recent inventions by Raskar’s team include transient imaging to look around a corner, a next-generation CAT-scan machine, imperceptible markers for motion capture, long-distance barcodes, touch + hover 3D interaction displays and new theoretical models to augment light fields to represent wave phenomena.
Raskar has dedicated his career to linking the best of the academic and entrepreneurial worlds with young engineers, igniting a passion for impact inventing. Raskar seeks to catalyze change on a massive scale by launching platforms that empower inventors to create solutions to improve lives globally.
Raskar has received the Lemelson Award, ACM SIGGRAPH Achievement Award, DARPA Young Faculty Award, Alfred P. Sloan Research Fellowship, TR100 Award from MIT Technology Review and Global Indus Technovator Award. He has worked on special research projects at Google [X] and Facebook and co-founded and advised several companies. He holds more than 80 US patents.
Making the Invisible Visible: Within Our Bodies, the World Around Us, and Beyond
We need to transition from analysis to synthesis when it comes to large scale image based studies of satellite or street level images.
Large scale, image based studies have the ability to unlock the human potential and really address some of the most important societal problems. The question really is, are we going to do that through analysis or are we going to step up to the game and actually start doing synthesis? Are we only go to study and observations or are we going to go and actually make an impact in the society?
Can global image repositories help UN's sustainable development goals (SDGs)? help us understand the social determinants of health? Satellite imagery, Google street view and user contributed photos from a global image repository are being used for large scale image-based studies, visual census and sentiment analysis [Ermon][http://StreetScore.media.mit.edu]. But we need to go beyond simply relying on big data for investigating social questions via remote analysis. We need to transition from analysis to synthesis. For deployable social solutions, we need to consider the full stack of physical devices, organizational interests and sector-specific resources.
Image-based large studies allow us to predict poverty from daytime and nighttime satellite imagery which can influence critical decisions for aid and development planning. In project ‘StreetScore’, our group has shown that semantic analysis of street level imagery such as Google Streetview, can provide varied insights rich in urban perception; our recent project ‘StreetChange’ shows the benefits of time-series data in driving these insights (http://streetchange.media.mit.edu).
We have seen some amazing work and you'll hear from Stephano about poverty mapping my glove previous collaborators to a population density crop maps, Betaine. So we had been, that's been fantastic progress in, in using a global industry, uh, in, in these areas that are taken from satellites or drones and then a street level imagery is also very widely available, either very structured like Google street view, but also from a user contributor photos and to that Nikki like and others in my group have been working on can we do a sentiment analysis of, of this imagery in this case, sentiment analysis of the perceived safety just for Google Street and main street and then create kind of citywide maps of a perceived safety that can be used by city planners and urban planners. So, which is great. But coming back to analysis versus synthesis opportunities, I'm going to give you a flavor of one of the projects we worked on a which is street addresses.
Project page: https://splitlearning.github.io/
Papers: https://arxiv.org/search/cs?searchtype=author&query=Raskar
Video: https://www.youtube.com/watch?v=8GtJ1bWHZvg
Split learning for health: Distributed deep learning without sharing raw patient data: https://arxiv.org/pdf/1812.00564.pdf
Distributed learning of deep neural network over multiple agents
https://www.sciencedirect.com/science/article/pii/S1084804518301590
Otkrist Gupta, Ramesh Raskar,
In domains such as health care and finance, shortage of labeled data and computational resources is a critical issue while developing machine learning algorithms. To address the issue of labeled data scarcity in training and deployment of neural network-based systems, we propose a new technique to train deep neural networks over several data sources. Our method allows for deep neural networks to be trained using data from multiple entities in a distributed fashion. We evaluate our algorithm on existing datasets and show that it obtains performance which is similar to a regular neural network trained on a single machine. We further extend it to incorporate semi-supervised learning when training with few labeled samples, and analyze any security concerns that may arise. Our algorithm paves the way for distributed training of deep neural networks in data sensitive applications when raw data may not be shared directly.
What is SIGGRAPH NEXT?
By Juliet Fiss
What will be the next big thing at SIGGRAPH, and how can the SIGGRAPH community contribute in an impactful way to fields outside of traditional computer graphics? SIGGRAPH NEXT at SIGGRAPH 2015 explored these questions. In this new addition to the SIGGRAPH program, an eclectic set of speakers gave TED-style talks and posed grand challenges to the SIGGRAPH community. In this blog post, Professor Ramesh Raskar of the MIT Media Lab introduces SIGGRAPH NEXT and outlines his vision for it.
What will be the next big thing at SIGGRAPH?
The SIGGRAPH community has a set of hammers that it uses to solve problems: geometry processing, rendering, animation, and imaging. What will be the next hammer, the next major field of study, appear at SIGGRAPH? Let’s examine where our research ideas come from. Often, advances in machine learning, optimization, signal processing, and optics forge our hammers. Our selection of hammer also depends on the nails we see. The most common application areas of computer graphics currently include computer-aided design, movies, games, and photography.
We often ask: “Does this work contribute to SIGGRAPH techniques?”
We should also ask, “Does this work contribute SIGGRAPH techniques to _____?”
When we answer the challenges posed by these traditional application areas of computer graphics, we are “drinking our own champagne.” We have made amazing progress in these application areas, and we should celebrate! SIGGRAPH NEXT is about finding new varieties of champagne; for that, we need new varieties of grapes. We should invite others from nontraditional and emerging application areas to enjoy our champagne with us, and they will become part of our community. First, we can expand our work in existing areas like mobile, user interaction, virtual reality, fabrication, and new types of cameras. We can also expand into emerging areas such as healthcare, energy, education, entrepreneurship, materials, tissue fabrication, and social media. What’s next?
Professor Raskar highlights three top areas where we can make an impact. One big take-home message is that many of these applications involve biology: bio is the new digital, and it will affect us ubiquitously.
'Media' is a plural for medium. The medium for impact of digital technologies at MIT Media Lab can be photons, electrons, neurons, atoms, cells, musical notes and more.
Over the last 40 years, computing has moved from processor, network, social and more sensory.
MIT Media Lab works at the intersection of computing and such media for human-centric technologies.
Ramesh Raskar
MIT Media Lab
Ramesh Raskar is an Associate Professor at MIT Media Lab. Ramesh Raskar joined the Media Lab from Mitsubishi Electric Research Laboratories in 2008 as head of the Lab’s Camera Culture research group. His research interests span the fields of computational photography, inverse problems in imaging and human-computer interaction. Recent projects and inventions include transient imaging to look around a corner, a next generation CAT-Scan machine, imperceptible markers for motion capture (Prakash), long distance barcodes (Bokode), touch+hover 3D interaction displays (BiDi screen), low-cost eye care devices (Netra,Catra), new theoretical models to augment light fields (ALF) to represent wave phenomena and algebraic rank constraints for 3D displays(HR3D).
In 2004, Raskar received the TR100 Award from Technology Review, which recognizes top young innovators under the age of 35, and in 2003, the Global Indus Technovator Award, instituted at MIT to recognize the top 20 Indian technology innovators worldwide. In 2009, he was awarded a Sloan Research Fellowship. In 2010, he received the Darpa Young Faculty award. Other awards include Marr Prize honorable mention 2009, LAUNCH Health Innovation Award, presented by NASA, USAID, US State Dept and NIKE, 2010, Vodafone Wireless Innovation Project Award (first place), 2011. He holds over 50 US patents and has received four Mitsubishi Electric Invention Awards. He is currently co-authoring a book on Computational Photography.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
9. Image-Based Actual Re-lighting Film the background in Milan, Measure incoming light, Light the actress in Los Angeles Matte the background Matched LA and Milan lighting. Debevec et al., SIGG2001
10.
11. Dual photography from diffuse reflections: Homework Assignment 2 the camera’s view Sen et al, Siggraph 2005
32. 2 Sept 18th Modern Optics and Lenses, Ray-matrix operations 3 Sept 25th Virtual Optical Bench, Lightfield Photography, Fourier Optics, Wavefront Coding 4 Oct 2nd Digital Illumination , Hadamard Coded and Multispectral Illumination 5 Oct 9th Emerging Sensors : High speed imaging, 3D range sensors, Femto-second concepts, Front/back illumination, Diffraction issues 6 Oct 16th Beyond Visible Spectrum: Multispectral imaging and Thermal sensors, Fluorescent imaging, 'Audio camera' 7 Oct 23rd Image Reconstruction Techniques, Deconvolution, Motion and Defocus Deblurring, Tomography, Heterodyned Photography, Compressive Sensing 8 Oct 30th Cameras for Human Computer Interaction (HCI): 0-D and 1-D sensors, Spatio-temporal coding, Frustrated TIR, Camera-display fusion 9 Nov 6th Useful techniques in Scientific and Medical Imaging: CT-scans, Strobing, Endoscopes, Astronomy and Long range imaging 10 Nov 13th Mid-term Exam, Mobile Photography, Video Blogging, Life logs and Online Photo collections 11 Nov 20th Optics and Sensing in Animal Eyes. What can we learn from successful biological vision systems? 12 Nov 27th Thanksgiving Holiday (No Class) 13 Dec 4th Final Projects
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34. Anti-Paparazzi Flash The anti-paparazzi flash: 1. The celebrity prey. 2. The lurking photographer. 3. The offending camera is detected and then bombed with a beam of light. 4. Voila! A blurry image of nothing much.
35.
36.
37.
38. Kitchen Sink: Volumetric Scattering Volumetric Scattering : Chandrasekar 50, Ishimaru 78 Direct Global
39. “ Origami Lens”: Thin Folded Optics (2007) “ Ultrathin Cameras Using Annular Folded Optics, “ E. J. Tremblay , R. A. Stack, R. L. Morrison, J. E. Ford Applied Optics , 2007 - OSA Slides by Shree Nayar
55. Ramesh Raskar, Karhan Tan, Rogerio Feris, Jingyi Yu, Matthew Turk Mitsubishi Electric Research Labs (MERL), Cambridge, MA U of California at Santa Barbara U of North Carolina at Chapel Hill Non-photorealistic Camera: Depth Edge Detection and Stylized Rendering using Multi-Flash Imaging
86. Flash Matting Flash Matting, Jian Sun, Yin Li, Sing Bing Kang, and Heung-Yeung Shum, Siggraph 2006
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88. Multi-light Image Collection [Fattal, Agrawala, Rusinkiewicz] Sig’2007 Input Photos ShadowFree, Enhanced surface detail, but Flat look Some Shadows for depth but Lost visibility
89. Multiscale decomposition using Bilateral Filter, Combine detail at each scale across all the input images. Fuse maximum gradient from each photo, Reconstruct from 2D integration all the input images. Enhanced shadows
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91. Dual Photography Pradeep Sen, Billy Chen, Gaurav Garg, Steve Marschner Mark Horowitz, Marc Levoy, Hendrik Lensch Stanford University Cornell University * * August 2, 2005 Los Angeles, CA
105. = pq x 1 mn x 1 Mathematical notation 1 0 0 0 0 0 T mn x pq
106. = pq x 1 mn x 1 Mathematical notation 0 1 0 0 0 0 T mn x pq
107. = pq x 1 mn x 1 Mathematical notation 0 0 1 0 0 0 T mn x pq
108. = Mathematical notation little interreflection -> sparse matrix many interreflections -> dense matrix T mn x pq P pq x 1 C mn x 1
109. ? Mathematical notation T = mn x pq primal space dual space = pq x mn P pq x 1 C mn x 1 C mn x 1 P pq x 1 j i i j T ij T T ji T = T T T = T ji ij
110. Definition of dual photography = primal space dual space = T mn x pq pq x mn T T T mn x pq mn x 1 C pq x 1 P pq x 1 P mn x 1 C
134. Photosensor experiment = primal space dual space = T mn x pq pq x 1 P mn x 1 C C T 1 x pq T 1 x pq pq x 1 P pq x 1 T T C
135. Example X X X X X X O O 8 x 8 pixel projector projected patterns
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141. Visual Chatter in the Real World Shree K. Nayar Computer Science Columbia University With: Guru Krishnan, Michael Grossberg, Ramesh Raskar Eurographics Rendering Symposium June 2006, Nicosia, Cyprus Support: ONR
142. source surface P Direct and Global Illumination camera A A : Direct B B : Interrelection C C : Subsurface D participating medium D : Volumetric translucent surface E E : Diffusion
146. Compute Direct and Global Images of a Scene from Two Captured Images Create Novel Images of the Scene Enhance Brightness Based Vision Methods New Insights into Material Properties
147. Direct and Global Components: Interreflections surface i camera source direct global radiance j BRDF and geometry
156. F G C A D B E A: Diffuse Interreflection (Board) B : Specular Interreflection (Nut) C : Subsurface Scattering (Marble) D : Subsurface Scattering (Wax) E : Translucency (Frosted Glass) F : Volumetric Scattering (Dilute Milk) G : Shadow (Fruit on Board) Verification
157. Verification Results 0 0.2 0.4 0.6 0.8 1 3 5 7 9 11 15 19 23 27 31 35 39 43 47 p C D F E G B A Checker Size marble D F E G B A 0 20 40 60 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Fraction of Activated Pixels
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159. V-Grooves: Diffuse Interreflections concave convex Psychophysics: Gilchrist 79, Bloj et al. 04 Direct Global
178. Scene Direct Global Marble: When BSSRDF becomes BRDF Subsurface Measurements: Jensen et al. 01, Goesele et al. 04 1 4 Resolution 1 1 6 1 2
179. Hand Skin: Hanrahan and Krueger 93, Uchida 96, Haro 01, Jensen et al. 01, Igarashi et al. 05, Weyrich et al. 05 Direct Global
180. Hands Afric. Amer. Female Chinese Male Spanish Male Direct Global Afric. Amer. Female Chinese Male Spanish Male Afric. Amer. Female Chinese Male Spanish Male
201. Background is captured from day-time scene using the same fixed camera Night Image Day Image Context Enhanced Image http://web.media.mit.edu/~raskar/NPAR04/
202. Factored Time Lapse Video Factor into shadow, illumination, and reflectance. Relight, recover surface normals, reflectance editing. [Sunkavalli, Matusik, Pfister, Rusinkiewicz], Sig’07
New techniques are trying decrease this distance using a folded optics approach. The origami lens uses multiple total internal reflection to propagate the bundle of rays.
CPUs and computers don’t mimic the human brain. And robots don’t mimic human activities. Should the hardware for visual computing which is cameras and capture devices, mimic the human eye? Even if we decide to use a successful biological vision system as basis, we have a range of choices. For single chambered to compounds eyes, shadow-based to refractive to reflective optics. So the goal of my group at Media Lab is to explore new designs and develop software algorithms that exploit these designs.
Current explosion in information technology has been derived from our ability to control the flow of electrons in a semiconductor in the most intricate ways. Photonic crystals promise to give us similar control over photons - with even greater flexibility because we have far more control over the properties of photonic crystals than we do over the electronic properties of semiconductors.
Changes in the index of refraction of air are made visible by Schlieren Optics. This special optics technique is extremely sensitive to deviations of any kind that cause the light to travel a different path. Clearest results are obtained from flows which are largely two-dimensional and not volumetric. In schlieren photography, the collimated light is focused with a lens, and a knife-edge is placed at the focal point, positioned to block about half the light. In flow of uniform density this will simply make the photograph half as bright. However in flow with density variations the distorted beam focuses imperfectly, and parts which have focussed in an area covered by the knife-edge are blocked. The result is a set of lighter and darker patches corresponding to positive and negative fluid density gradients in the direction normal to the knife-edge.
Full-Scale Schlieren Image Reveals The Heat Coming off of a Space Heater, Lamp and Person
But what if the user is not wearing the special clothing. Can we still understand the gestures using a simple camera? The problem is that in a cluttered scene, it is often difficult to do image processing.
But what if the user is not wearing the special clothing. Can we still understand the gestures using a simple camera? The problem is that in a cluttered scene, it is often difficult to do image processing.
Good afternoon and thank you for attending our talk entitled “Dual Photography”.
I will start off by giving you a quick overview of our technique. Suppose you had the scene shown which is being imaged by a camera on the left and is illuminated by a projector on the right. If you took a picture with the camera, here’s what it would look like. You can see the scene is being illuminated from the right, from the position of the projector located off camera. Dual photography allows us to virtually exchange the positions of the camera and the projector, generating this image. This image is synthesized by our technique. We never had a camera in this position. You can see that the technique has captured shadows, refraction, reflection and other global illumination effects.
In this talk I will start off by discussing how dual photography works. I will give a motivation For dual photography by applying it to the problem of scene relighting, and show that it can be Used to greatly accelerate the acquisition of the data needed. I will then talk about an algorithm We developed to accelerate the acquisition of the light transport needed to perform dual photography And I will end with some conclusions.
Dual photography is based on the principle of Helmholtz reciprocity. Suppose we have a ray leaving the light with intensity I and scattering off the scene towards the eye with a certain attenuation. Let’s call this the primal configuration. In the dual configuration, the positions of the eye and the light are interchanged. Helmholtz reciprocity says that the scattering is symmetric, thus the same ray in the opposite direction will have the same attenuation.
This photocell configuration might remind us of imaging techniques. For example, in the early days of television a similar method was use to create one of the first TV cameras. Known as a “flying-spot” camera, a beam of extremely bright light would scan the scene and a bank of photosensors would measure the reflected light. The value measured at these sensors would be immediately sent out via closed-circuit to television sets whose electron beam was synchronized with the beam of light. Thus they drew out the image as it was being measured by the Photosensors. This allowed for the creation of a television system that did not have to have a means to store “frames” of video. Another related applications. Finally scanning electron microscopes (and other scanned beam systems for that matter) can be thought of as employing the principle of dual photography. Thus while some of these applications might be new, what is new is the framework that establishes dual photography in this manner and gives us insights to possible applications such as relighting as we shall see in a moment.
Suppose we had the scene shown and we illuminated it with a projector from the left and imaged it with a camera on the right. Now the pixels of the projector and the camera form solid angles in space whose size depends on the resolution of each. Lets assume a resolution of pxq for the projector and mxn for the camera. What dual photography does is transform the camera into a projector and the projector into a camera. Note that the properties of the new projector (such as position, field-of-view, and resolution) are the same as that of the old camera, and vice versa. We call this the dual configuration. In this work we shall see that it is possible to attain this dual configuration by making measurements only in the original primal configuration. We will do this by measuring the light transport between individual pixels of the projector to individual pixels of the camera. Because there are 2D pixels in the projector and 2D pixels in the camera, this light transport is a 4D function. Now lets see how we can represent this system with mathematical notation.
Fortunately, the superposition of light makes this a linear system and so we can represent this setup with a simple linear equation. We can represent the projected pattern as a column vector of resolution pq x 1 and likewise we can represent the camera as a column vector of resolution mn x 1. This means that the 4-D light transport function that specifies the transport of light from a pixel in the projector to a pixel in the camera can be represented as a matrix of resolution mn x pq.
If we put these elements together, we can see that it forms a simple linear equation. Here we apply the projected pattern at vector P to the transport matrix, which we call the “T” matrix in the paper and we get a result at vector C, which is our resulting camera image for that projector pattern. I must mention that if T is properly measured, it will contain all illumination paths from the projector to the camera, including multiple bounces, subsurface scattering, and other global illumination contributions which are often desireable. At this point, let’s gain an intuition as to the composition of T. What does this matrix look like?
We can gain insight into this by illuminating patterns at the projector that have only a single pixel turned on as shown here. We can see if we apply this vector at P, it will address a single column of T which will be output at C.
This is true for each vector P with a single pixel turned on; they will extract a different column of T.
Thus we can see that the columns of T are composed of the images we would take at C with a different pixel turned on at the projector.
So this is the way light flows in our primal configuration…
We’re going to put primes above the P and C vectors to indicate that they are in the primal space. So what will happen when we go to the dual space and interchange the roles of the camera and the projector? Now light will be emitted by the camera and photographed by the projector. So this gives us the linear equation shown at the bottom. It is obviously still a linear system, except that now light leaves the camera is transformed by this new transport matrix (let’s call it T2) and results in an image at the projector. Note that the dimensions of the camera and the projector stay the same. So dimensional analysis indicates that the dimension of T2 is now pq x mn. The key observation of dual photography is that the new matrix T2 is related to the original T. We can see that if we look at the transport for a particular pixel of the projector to a particular pixel of the camera. Let’s look at the transport from pixel “j” of the projector to pixel “i” of the camera. The transport between this pair is specified by a single element in the T matrix, in this case element Tij. Now let’s look at the same pixels in at the dual configuration with the camera emitting light and the projector capturing it. The pixel Of interest in the camera is still pixel I and the pixel of interest in the projector is still j. In this case the element that relates These two is T2ji. Dual Photography is made possible by Helmholtz reciprocity which can be shown to indicate that the pixel To pixel transport is symmetric, that is the transport will be the same whether the light is leaving the projector pixel and going to The camera pixel, or going from the camera pixel to the projector pixel. Thus we can write that T2ji = Tij. This means that T2 is simply the transpose the original T.
Thus we can define “Dual Photography” as the process of transposing this transport matrix to generate pictures from the point of view of the projector as illuminated by the camera. To create a dual image, we must first capture the transport matrix T between the projector and camera in the primary configuration. As I indicated earlier, lighting up individual pixels of the projector extract single columns of the T matrix, and if we do that for all the columns T can be acquired in that manner. We shall talk about an acceleration technique later in the talk. Again, dual photography is based only on the fact that the pixel-to-pixel transport is symmetric. We formally prove this in the Appendix of the paper.
Before we continue, let’s take a look at some initial results taken by our system. Here we show the primal image of a set of famous graphics objects. Here the projector is to the right. If we take a look at the dual image, we can see that we are now looking at these objects face on and the illumination is coming in from where the camera used to be. Note that the shading on all the objects is correct.
In this next example, we have a few objects viewed from above by the camera. The projector is in front of them and forms a fairly grazing angle with the floor So it is gray. If we look at the dual image, we can see the objects from in front being lit from above. Note that the floor is now brighter because the new light source (which was the original camera) is viewing it from a more perpendicular direction. Also see for example that the shadow on the horse in the dual image corresponds To the portion of the horse that the pillar is occluding. So in some ways, what we have here is a real-life shadow map, where the primal is the shadow map fro the dual. One thing I really like about this image is that you can see detail in the dual that is not visible in the primal. Take a look at the concentric rings in the detail at the base Of the pillar. This detail is simply not visible in the primal because of the angle but is very clear in the dual. Also the detail of the lions heads is more clear in the dual than in the primal.
We observe that since we have the complete pixel-to-pixel transport, we can relight either the primal or dual images with a new 2D projector pattern.
As far as the equations are concerned, what the photosensor is doing is integrating all of the values of the C vector into a single scalar value. Assume that this integration is being done uniformly across the field-of-view of the photosensor. So this is our new primal equation. Since the T matrix is no longer relating a vector to a vector, it collapses into a row vector of dimensions pq x 1 as shown here. We can measure this T vector in the same manner, by illuminating single pixels at the projector to extract the elements of T. If we transpose this vector into a column vector, we get the dual configuration, meaning the photograph taken by the projector and illuminated by the photocell. Here the incident illumination provided by c cannot be spatially varying since C is a scalar. This means that our light is a Uniform scaling of T. The picture shown here is an image that we acquired using a photocell shown and a projector.
I will now show some videos that show the projector patterns animating.
As far as the equations are concerned, what the photosensor is doing is integrating all of the values of the C vector into a single scalar value. Assume that this integration is being done uniformly across the field-of-view of the photosensor. So this is our new primal equation. Since the T matrix is no longer relating a vector to a vector, it collapses into a row vector of dimensions pq x 1 as shown here. We can measure this T vector in the same manner, by illuminating single pixels at the projector to extract the elements of T. If we transpose this vector into a column vector, we get the dual configuration, meaning the photograph taken by the projector and illuminated by the photocell. Here the incident illumination provided by c cannot be spatially varying since C is a scalar. This means that our light is a Uniform scaling of T. The picture shown here is an image that we acquired using a photocell shown and a projector.