In this course participants will learn the latest trends and newest technologies to develop an imaging strategy that will create competitive advantage through visual data mining techniques. Advances in Imaging surveys the landscape of imaging hardware, optics, sensors, and computational techniques through a mix of theory, hands-on open-ended exercises (rapid prototyping), and practical applications. You will learn about and observe high-end imaging devices in up-close demonstrations and explore computer vision using installed cameras, connected cameras in the cloud, and OpenCV on mobile platforms. Focus areas include self-driving cars and AR/ VR where we explore current approaches and potential disruptions to the fields including review of hardware, sensor technology, imaging devices, and algorithms. In addition, we will examine emerging solutions, like machine learning, that are opening up new research and commercial opportunities in immediate and future applications, including VR-AR, self-driving cars, and others.
Group discussions will let participants share their industry or academic experiences on what imaging technology trends are emerging. As a group, at the end of each day, we will try to predict the evolution of imaging technology on 5 and 10+ year timeframes. You will tour the Media Lab and learn about its research, its philosophy, and how it works through demos, case studies, and discussions with the members of the Camera Culture Group.
“Most creative work is a process of people passing ideas and inspirations from the past into the future and adding their own creativity along the way.” -Joichi Ito, Director, MIT Media Lab
Key topics include:
It is highly recommended that you apply for a course at least 6-8 weeks before the start date to guarantee there will be space available. After that date you may be placed on a waitlist. Courses with low enrollment may be cancelled up to 4 weeks before start date if sufficient enrollments are not met. If you are able to access the online application form, then registration for that particular course is still open.
Takeaways from this course include:
- Understanding the basics of a variety of computational imaging and visual mining techniques, both those used in industry today and cutting-edge techniques from the laboratory
- Observing demonstrations of various imaging hardware and visual analysis software
- Learning new methods for overcoming the traditional constraints in imaging using optics, sensors, and computer vision
- Understanding of current self-driving car technologies and potential disruptions
- Exploring new emerging solutions that are opening up new research and commercial opportunities in current and future applications
- Participating in small group discussions about the future of imaging, including future products, services, and societal impact
- Meeting and networking with the Boston-area imaging community via tours and social events planned each evening
Who Should Attend:
The course is suitable for decision-makers and planners for the next generation of imaging solutions, engineers and designers of imaging systems, and anyone interested in reviewing existing and emerging solutions in optics, sensors, image analysis, and computer vision. Application areas include consumer photography (including mobile phones), industrial machine vision, and scientific and medical imaging.
Background: There are no pre-requisites but a general knowledge of technologies that involve image processing, optics, and sensors is a plus.
Laptops are required for this course.
Day 1 – Morning: Beyond a 2D Image
A computational camera attempts to digitally capture the essence of visual information by exploiting the synergistic combination of task-specific optics, illumination, sensors, and processing. Demonstrations and discussions will center on thermal cameras, multi-spectral cameras, high-speed cameras, and 3D range-sensing time-of-flight cameras and camera arrays. Participants will address opportunities in scientific and medical imaging, mobile phone-based photography, cameras for human-computer interaction (HCI), and sensors mimicking animal eyes.
Day 1 – Afternoon: Rethinking Cameras and New Devices in the Market
Several new imaging devices are now in the market. They include: Google Glass, Microsoft Kinect, Leap Motion, lifelogging cameras, time-of-flight cameras, Google Tango phones, Lytro light field cameras, and FLIR thermal IR cameras. They are being paired with new interaction and display solutions such as Oculus Rift VR, E-Ink, and glassesfree 3D displays.
This session covers the complete camera pipeline. The instructor will explore several physical imaging prototypes plus commercial devices and help participants understand how each stage of the imaging process can be manipulated.
The day will end with a tour of the MIT Media Lab where many of these solutions are being used and new technologies are being developed.
Day 2 – Morning: Impact of Other Fields
This field—at the intersection of signal processing, applied optics, computer graphics and vision, electronics, art, and online sharing through social networks—is emerging as truly multidisciplinary. It cannot be studied in isolation. This session will examine several multidisciplinary impacts, such as whether innovative camera-like sensors can overcome the tough problems in scene understanding and generate insightful awareness, creating actionable information. In addition, participants will see how new algorithms are emerging to exploit unusual optics, programmable wavelength control, and femtosecond-accurate photon counting to decompose the sensed values into perceptually critical elements.
Day 2 – Afternoon: Computational Imaging and Computational Photography
Participants will explore modern methods for capturing and sharing visual information. If novel cameras can be designed to sample light in radically new ways, then rich and useful forms of visual information may be recorded—beyond those present in traditional photographs. Furthermore, if the computational process can be made aware of these novel imaging models, then the scene can be analyzed in higher dimensions (beyond 2D and 3D) and novel aesthetic renderings of the visual information can be synthesized. As an introduction to computer vision lectures on day three, participants will study the abilities of human vision versus machine vision and computer vision.
The day will end with a networking event with the Boston area imaging community including students, researchers, entrepreneurs, and professionals. This is part of the monthly meetup group called Imaging Café (http://tinyurl.com/imagingcafe).
Day 3 – Morning: Self Driving Cars, Methods and Progress
Self-driving car technology is progressing quickly and we eminently approach a future with autonomous vehicles. As these machines learn more from their environments and navigate more complicated parameters, we must focus and understand the imaging systems and sensors to ensure best accuracy. We will explore current technologies like RADAR and LIDAR, and new innovative approaches including sensor design and ultra fast imaging systems. What technologies and companies are currently in the self-driving space? What will self-d riving cars look like in developing countries? How do we determine which technologies to research to have the greatest impact in this space? Speakers answer these questions and introduce recent MIT research in this space.
Day 3 – Afternoon: Computer Vision, Low-level Image Features, and Analysis
Mid-level and High Level, Shape Recovery, Object Recognition, and Machine Learning
We don’t see with our eyes. We record with our eyes and see with our brains. Machines don’t see with cameras. Cameras record the photons and computers reconstruct the photo and also make a sense of the world. Computer vision techniques include low-level methods such as image processing and analysis that including filtering (think Instagram) and image statistics. Participants will study the range of low-level techniques and perform some hands-on experiments.
More sophisticated computer vision methods mimic visual analysis in the human brain. In fact, human vision is less concerned with pixel values and the majority of processing is dedicated to mid-level and high-level vision. Mid-level processing includes segmentation and shape understanding and this course will look at techniques and standard methods for both. In addition, 3D shape recovery is a very important operation in industrial and scientific imaging. Participants will survey the state-of-the-art techniques. High-level computer vision deals with object/face detection and recognition and generic scene understanding. Instructors will explore these topics and also introduce new topics in machine learning, clustering, compressive sensing, and sparse representations. The emerging new field of visual social computing is at the intersection of Internet vision, human computing, and social computing.
The day will end with an optional tour of an MIT imaging startup where participants will meet the team and hear their stories of development and innovation of MIT technologies in a commercial setting.
Day 4 – Morning: AR/VR
We explore current optical setups using current technologies, Oculus Rift, Meta, and Hololens. Test the latest tech, play with gaming and logistical solutions, and question the future of the industry – How will it be used? What interesting applications can we conceive? What does an augmented future look like? This emerging field has the potential to change the way we work and play. Gain competitive advantage by understanding the systems and how they work.
Day 4 – Afternoon: Roadmap for Imaging Applications and The Future of Imaging
The final day explores the impact of new imaging technology and applications on society—how imaging will change our world in the next five years. A series of short presentations will be followed by discussions as a class or in small groups.
With more than a billion people now using networked mobile cameras, we are seeing a rapid evolution in activities based on visual exchange. The capture and analysis of visual information plays an important role in photography, art, medical imaging, tele-presence, worker safety, scene understanding, and robotics. But current computational approaches analyze images from cameras with limited abilities. The goal of MIT’s Camera Culture Group is to go beyond post-capture software methods and exploit unusual optics, modern sensors, programmable illumination, and bio-inspired processing to decompose sensed values into perceptually critical elements. A significant enhancement in the next billion cameras to support scene analysis and mechanisms for superior metadata tagging for effective sharing will bring about a revolution in visual communication.
Guided by the questions that follow, the course will end by addressing the future of imaging. What will a camera/display look like in coming years? How will the next billion cameras change social culture? How can we augment the camera to support best “image search?” How will portable health diagnostics impact health care? Will we live mostly in virtual/augmented reality? How will ultra-high-speed/resolution imaging change us? How can we improve “trust” in imaging? Can we print anything? What are the opportunities in pervasive recording? What features will be in Photoshop in coming years? What is the future of moviemaking, news reporting, or sports viewing? These questions will shape the future of imaging products and services.
Class runs 9:30 am - 5:15 pm each day.
There will be a reception and dinner for faculty and participants on Monday evening. On Wednesday evening, there will be a networking event with the Boston-area imaging community.
PROJECT MANAGER, U.S. NAVY
"A lot of the concepts were used immediately to start interactions with my team."
VICE PRESIDENT FOR RESEARCH AND DEVELOPMENT, NEXTERRA FOUNDATION
"The instructors seemed to have a goal of exposing the class to the spirit of innovation in which they perform their research at MIT, and the interactions really seemed driven by that goal. It was good."
"The course was very enlightening and stretched my imagination alot."
PROFESSOR, UNIVERSITY OF NEW BRUNSWICK
"The course provided us with a platform to not just learn latest technologies but also meet top leaders in imaging. What I learned from the course is beyond what I could learn from a normal course."
"This is an excellent course not just for learning new knowledge but also for finding new collaboration opportunities."
Ramesh Raskar is an Associate Professor at the MIT Media Lab and heads the Lab’s Camera Culture research group. He joined MIT from Mitsubishi Electric Research Laboratories (MERL) in 2008. Raskar's research interests span the fields of computational light transport, computational photography, inverse problems in imaging, and human-computer interaction. Recent projects and inventions include transient imaging to look around a corner (CORNAR), low-cost eye care devices (NETRA, CATRA), a next-generation CAT-scan machine, imperceptible markers for motion capture (Prakash), long distance barcodes (Bokode), touch+hover 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).
Raskar is a recipient of the TR100 award from Technology Review magazine (2004), Global Indus Technovator Award to recognize the top 20 Indian technology innovators world wide (2003), Alfred P. Sloan Research Fellowship (2009), and DARPA Young Faculty award (2010). Other awards include the Marr Prize honorable mention (2009), LAUNCH Health Innovation Award (2010), Vodafone Wireless Innovation Award (first place, 2011), and the Edison Award (2012). He holds over 40 U.S. patents and has received four Mitsubishi Electric Invention Awards. He is currently co-authoring a book on computational photography.
This course takes place on the MIT campus in Cambridge, Massachusetts. We can also offer this course for groups of employees at your location. Please complete the Custom Programs request form for further details.
|Fundamentals: Core concepts, understandings, and tools (30%)||30|
|Latest Developments: Recent advances and future trends (40%)||40|
|Industry Applications: Linking theory and real-world (30%)||30|
|Lecture: Delivery of material in a lecture format (60%)||60|
|Discussion or Groupwork: Participatory learning (25%)||25|
|Labs: Demonstrations, experiments, simulations (15%)||15|
|Introductory: Appropriate for a general audience (50%)||50|
|Specialized: Assumes experience in practice area or field (30%)||30|
|Advanced: In-depth explorations at the graduate level (20%)||20|