SCIEN Talk

Cruise is developing and testing a fleet of self driving cars on the streets of San Francisco. Getting these cars to drive is a hard engineering and science problem - this talk explains roughly how self driving cars work and how computer vision, from camera hardware to deep learning, helps make a self driving car go.

More Information: https://www.getcruise.com/
see also http://www.theverge.com/2017/1/19/14327954/gm-self-driving-car-cruise-chevy-bolt-video

Lab experiments have long played an important role in behavioral science, in part because they allow for carefully designed tests of theory, and in part because randomized assignment facilitates identification of causal effects. At the same time, lab experiments have traditionally suffered from numerous constraints (e.g. short duration, small-scale, unrepresentative subjects, simplistic design, etc.) that limit their external validity. In this talk I describe how the web in general—and crowdsourcing sites like Amazon's Mechanical Turk in particular—allow researchers to create "virtual labs" in which they can conduct behavioral experiments of a scale, duration, and realism that far exceed what is possible in physical labs. To illustrate, I describe some recent experiments that showcase the advantages of virtual labs, as well as some of the limitations. I then discuss how this relatively new experimental capability may unfold in the future, along with some implications for social and behavioral science.

The continuous increase in performance and the versatility of ARRI´s digital motion picture camera systems led to our development of the first fully digital stereoscopic operating microscope, the ARRISCOPE. For the last 18 months' multiple units have been used in clinical trials at renowned clinics in the field of Otology in Germany.
During our presentation we will cover the obstacles, initial applications and future potentials of 3D camera based surgical microscopes and give an insight into the technical preconditions and advantages of the digital imaging chain. In conclusion of the presentation, examples of different surgical procedures recorded with the ARRISCOPE and near future augmented and virtual reality 3D applications will be demonstrated.

More Information: http://www.arrimedical.com/

In 1950, Alan Turing introduced the Turing Test, an essential concept in the philosophy of Artificial Intelligence (AI). He proposed an "imitation game" to test the sophistication of an AI software. Similar tests have been suggested for fields including Computer Graphics and Visual Computing. In this talk, we will propose an Augmented Reality Turing Test (ARTT).

Augmented Reality (AR) embeds spatially-registered computer graphics in the user's view in realtime. This capability can be used for a lot of purposes; for example, AR hands can demonstrate manual repair steps to a mechanic. To pass the ARTT, we must create AR objects that are indistinguishable from real objects. Ray Kurzweil bet USD 20,000 that the Turing Test will be passed by 2029. We think that the ARTT can be passed significantly earlier.

We will discuss the grand challenges for passing the ARTT, including: calibration, localization & tracking, modeling, rendering, display technology, and multimodal AR. We will also show examples from our previous and current work at Nara Institute of Science and Technology in Japan.

Augmented and Virtual Reality have been hailed as "the next big thing" several times in the past 25 years. Some are predicting that VR will be the next computing platform, or at least the next platform for social media. Others worry that today's VR systems are closer to the 1990s Apple Newton than the 2007 Apple iPhone. This talk will feature a short, personal history of AR and VR, a survey of some of current work, sample applications, and remaining problems. Current work with encouraging results include 3D acquisition of dynamic, populated spaces; compact and wide field-of-view AR displays; low-latency and high-dynamic range AR display systems; and AR lightfield displays that may reduce the accommodation-vergence conflict.

More information: http://henryfuchs.web.unc.edu/

LIDAR systems use single-photon detectors to enable long-range reflectivity and depth imaging. By exploiting an inhomoheneous Poisson process observation model and the typical structure of natural scenes, first-photon imaging demonstrates the possibility of accurate LIDAR with only 1 detected photon per pixel, where half of the detections are due to (uninformative) ambient light. I will explain the simple ideas behind first-photon imaging. Then I will touch upon related subsequent works that mitigate the limitations of detector arrays, withstand 25-times more ambient light, allow for unknown ambient light levels, and capture multiple depths per pixel.

The Stanford Center for Image Systems Engineering (SCIEN) is a partnership between the Stanford School of Engineering and technology companies developing imaging systems for the enhancement of human communication.

Davis will show how video can be a powerful way to measure physical vibrations. By relating the frequencies of subtle, often imperceptible changes in video to the vibrations of visible objects, we can reason about the physical properties of those objects and the forces that drive their motion. In my talk I'll show how this can be used to recover sound from silent video (Visual Microphone), estimate the material properties of visible objects (Visual Vibrometry), and learn enough about the physics of objects to create plausible image-space simulations (Dynamic Video).

The diversity of painting styles represents a rich visual vocabulary for the construction of an image. The degree to which one may learn and parsimoniously capture this visual vocabulary measures our understanding of the higher level features of paintings, if not images in general. In this work we investigate the construction of a single, scalable deep network that can parsimoniously capture the artistic style of a diversity of paintings. We demonstrate that such a network generalizes across a diversity of artistic styles by reducing a painting to a point in an embedding space. Importantly, this model permits a user to explore new painting styles by arbitrarily combining the styles learned from individual paintings. We hope that this work provides a useful step towards building rich models of paintings and offers a window on to the structure of the learned representation of artistic style.

High-speed imaging is an indispensable tool for blur-free observation and monitoring of fast transient dynamics in today's scientific research, industry, defense, and energy. The field of high-speed imaging has steadily grown since Eadweard Muybridge demonstrated motion-picture photography in 1878. High-speed cameras are commonly used for sports, manufacturing, collision testing, robotic vision, missile tracking, and fusion science and are even available to professional photographers. Over the last few years, high-speed imaging has been shown highly effective for single-cell analysis – the study of individual biological cells among populations for identifying cell-to-cell differences and elucidating cellular heterogeneity invisible to population-averaged measurements. The marriage of these seemingly unrelated disciplines has been made possible by exploiting high-speed imaging's capability of acquiring information-rich images at high frame rates to obtain a snapshot library of numerous cells in a short duration of time (with one cell per frame), which is useful for accurate statistical analysis of the cells. This is a paradigm shift in the field of high-speed imaging since the approach is radically different from its traditional use in slow-motion analysis. In this talk, I introduce a few different methods for high-speed imaging and their application to single-cell analysis for precision medicine and green energy.