We propose a novel camera setup in which both the lens and the sensor are perturbed during the exposure. We analyze the defocus effects produced by such a setup, and use it to demonstrate new methods for simulating a lens with a larger effective aperture size (i.e., shallower depth of field) and methods for achieving approximately depth-independent defocus blur size. We achieve exaggerated, programmable, and pleasing bokeh with relatively small aperture sizes such as those found on cell phone cameras. Destabilizing the standard alignment of the sensor and lens allows us to introduce programmable defocus effects and achieve greater flexibility in the image capture process.
Medical Imaging Developed by Academia-Industry Collaboration in Hamamatsu, Japanechangeurba
Medical Imaging Developed by Academia-Industry Collaboration in Hamamatsu, Japan
by M. Susumu TERAKAWA
Hamamatsu Univ. Sch. Med.. Fiber-Coupled Confocal Microscopefor Living Animals.
Numeriglobe 2009
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
Medical Imaging Developed by Academia-Industry Collaboration in Hamamatsu, Japanechangeurba
Medical Imaging Developed by Academia-Industry Collaboration in Hamamatsu, Japan
by M. Susumu TERAKAWA
Hamamatsu Univ. Sch. Med.. Fiber-Coupled Confocal Microscopefor Living Animals.
Numeriglobe 2009
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.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
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Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
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- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
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Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
AI for Every Business: Unlocking Your Product's Universal Potential by VP of ...
Image Destabilization ICCP 2009
1. MIT Media Lab Camera Culture
Image Destabilization:
Programmable Defocus using
Lens and Sensor Motion
Ankit Mohan, Douglas Lanman,
Shinsaku Hiura, Ramesh Raskar
MIT Media Lab
2. MIT Media Lab Camera Culture
Defocus Blur
Lots of glass; Heavy; Bulky; Expensive
3. MIT Media Lab Camera Culture
Image Destabilization
Camera
Lens Sensor
Static
Scene
4. MIT Media Lab Camera Culture
Image Destabilization
Camera
Static
Scene
Lens Motion Sensor Motion
5. MIT Media Lab Camera Culture
f8
Related Work
f/4
f/2
[Bae and Durand 2007] extrapolated
aperture
f/1
[Hasinoff and Kutulakos 2007]
[Vaish et al. 2004]
[Hiura et al. 2009]
6. MIT Media Lab Camera Culture
Laminography
Motion direction
X-Ray Source
Plane of focus
X-Ray Sensor
Motion direction
Related technique: Time Delay and Integration (TDI)
7. MIT Media Lab Camera Culture
Lens based Focusing
Lens Sensor
A B’
A’
B
8. MIT Media Lab Camera Culture
Lens based Focusing
Lens Sensor
A B’
A’
B
9. MIT Media Lab Camera Culture
Smaller aperture Smaller defocus blur
Lens Sensor
A B’
A’
B
10. MIT Media Lab Camera Culture
Pinhole: All In-Focus
Pinhole Sensor
A B’
A’
B
11. MIT Media Lab Camera Culture
Shifting Pinhole
Pinhole Sensor
vp
A
B’
B
A’
12. MIT Media Lab Camera Culture
Shifting Pinhole
Pinhole Sensor
vp
A
B’
B A’
13. MIT Media Lab Camera Culture
Shifting Pinhole
Pinhole Sensor
vp
B’
A
A’
B
14. MIT Media Lab Camera Culture
Shifting Pinhole
Pinhole Sensor
vp
B’
A
A’
B
15. MIT Media Lab Camera Culture
Shifting Pinhole
Pinhole Sensor
vp
B’
A
tp
A’
B
da
db ds
16. MIT Media Lab Camera Culture
Shifting Pinhole and Sensor
Pinhole Sensor
vp vs
A
B’
B
A’
da
db ds
Focus Here
17. MIT Media Lab Camera Culture
Shifting Pinhole and Sensor
Pinhole Sensor
vp vs
B’
A
A’
B
da
db ds
Focus Here
18. MIT Media Lab Camera Culture
Shifting Pinhole and Sensor
Pinhole Sensor
B’
vp vs
A
A’
B
da
db ds
Focus Here
19. MIT Media Lab Camera Culture
Shifting Pinhole and Sensor
Pinhole Sensor
B’
vp vs
A
A’
B
da
db ds
Focus Here
20. MIT Media Lab Camera Culture
A Lens in Time!
Lens Equation:
Virtual Focal Length:
Virtual F-Number:
Analogous to shift and sum based
Light field re-focusing.
22. MIT Media Lab Camera Culture
Adjusting the Focus Plane
all-in-focus pinhole image
23. MIT Media Lab Camera Culture
Adjusting the Focus Plane
focused in the front using destabilization (10 second exposure)
24. MIT Media Lab Camera Culture
Adjusting the Focus Plane
focused in the middle using destabilization (5 second exposure)
25. MIT Media Lab Camera Culture
Adjusting the Focus Plane
focused in the back using destabilization (10 second exposure)
26. MIT Media Lab Camera Culture
Adjusting the Virtual Aperture
focused in the middle using destabilization (5mm pinhole translation)
27. MIT Media Lab Camera Culture
Adjusting the Virtual Aperture
focused in the middle using destabilization (30mm pinhole translation)
28. MIT Media Lab Camera Culture
Shifting Lens and Sensor Defocus
Defocus Exaggeration
• Physical vs. synthetic focus focus
Similar physical and synthetic
real focus
virtual focus
aperture
sensor
29. MIT Media Lab Camera Culture
Defocus Exaggeration
static lens with an f/2.8 aperture
30. MIT Media Lab Camera Culture
Defocus Exaggeration
destabilization simulates a reduced f-number
31. MIT Media Lab Camera Culture
Defocus Invariance
• Differing physical and synthetic focus
real focus
virtual focus
aperture
sensor
32. MIT Media Lab Camera Culture
Defocus Invariance
• Related work
– [Nagahara et al. 2008]
– [Cathey and Dowski 1995]
[Nagahara et al. 2008] – [Levin et al. 2008]
• PSF not depth invariant
* = – only size is depth invariant
real PSF virtual PSF overall PSF
• Gaussian special case
– depth invariant PSF
* = – inversion is ill-conditioned
real PSF virtual PSF overall PSF
33. MIT Media Lab Camera Culture
Defocus Invariance
depth-invariant blur size (horizontal slit + destabilization)
34. MIT Media Lab Camera Culture
Defocus Invariance
Richardson-Lucy deconvolution result
35. MIT Media Lab Camera Culture
Tilted Sensor
focus plane aperture plane sensor plane
dC
C
D′
D
C′
dD
d′D
d′C
Scheimpflug intersection
36. MIT Media Lab Camera Culture
Tilted Sensor
focus plane aperture plane sensor plane
dC
C vp vs?
D
dD
37. MIT Media Lab Camera Culture
Tilted Sensor
focus plane aperture plane sensor plane
dC
C vp vs
D
dD
α
α
d′C
d′D
D′ C′
38. MIT Media Lab Camera Culture
Tuning the PSF
real focus
aperture
sensor
pinhole image (static f/22 aperture)
39. MIT Media Lab Camera Culture
Tuning the PSF
real focus
aperture
sensor
large aperture image (static f/2.8 aperture)
40. MIT Media Lab Camera Culture
Tuning the PSF
virtual focus
aperture
sensor
destabilized image using a pinhole (translated f/22 aperture)
41. MIT Media Lab Camera Culture
Tuning the PSF
real focus
virtual focus
aperture
sensor
destabilized image using a large aperture (translated f/2.8 aperture)
42. MIT Media Lab Camera Culture
Tuning the PSF
real focus
virtual focus
aperture
sensor
simulated aspheric lens using a vertical slit aperture and destabilization
43. MIT Media Lab Camera Culture
Extension to 2D Displacements
linear circular elliptical
“figure 8” hypocycloidal trispiral
44. MIT Media Lab Camera Culture
Large apertures with tiny lenses?
Benefits Limitations
• No time or light inefficiency • Coordinated mechanical
wrt cheap cameras movement required
• Exploits unused area around • Diffraction (due to small aperture)
the lens cannot be eliminated
• Compact design [Zhang and Levoy, tomorrow]
[Our group: augmented LF for wave analysis]
• With near-pinhole apertures
(mobile phones) many • Scene motion during exposure
possibilities
45. MIT Media Lab Camera Culture
Acknowledgements
Grace Woo Quinn Smithwick Gabriel Taubin Jaewon Kim
MIT CSAIL MIT Media Lab Brown University MIT Media Lab
MIT Media Lab: Camera Culture
46. MIT Media Lab Camera Culture
SLRs with tiny lenses?
• Analysis of space of relative lens/sensor displacement
• Destabilization as virtual focusing mechanism
• Shallower depth of field than physical aperture
• Depth-independent defocus blur size
Editor's Notes
The technique was pioneered by radar engineer Edward Dowski and his thesis adviser Thomas Cathey at the University of Colorado in the United States in the 1990s. "Flexible Depth of Field Photography," H. Nagahara, S. Kuthirummal, C. Zhou, and S.K. Nayar, European Conference on Computer Vision (ECCV), Oct, 2008. Motion-Invariant Photography Anat Levin Peter Sand Taeg Sang Cho Fredo Durand William T. Freeman Computer Science and Artificial Intelligence Lab (CSAIL) Massachusetts Institute of Technology