SCIEN Talk

Exploiting to see beyond the diffraction-limit of optical microscopies is of great significance. State-of-the-art solutions of super-resolution microscopy, like STED and STORM approaches, rely on the fluorescent effect of labeling samples. It is still challenging to obtain the super-resolution for unlabeling samples without fluorescent effect. To this end, we have developed a novel super-resolution method, called Spatial Frequency Shift (SFS), to realize the deep super-resolution with or without fluorescent effect in wide field imaging. The principle and the applications of this SFS technique will be presented.

Accurate and reliable 3D perception is the key remaining bottleneck to making self-driving vehicles safe and ubiquitous. Today, it's relatively easy to get an autonomous car to work 99% of the time, but it's the incredibly long tail of edge cases that's preventing them from reaching real-world deployment without a backup driver constantly watching over. All of this comes down to how well the autonomous car can see and understand the world around it. The key to achieving accurate, safer-than-human level 3D perception all starts with the LiDAR. That said, both legacy LiDAR solutions and newer upstarts, which largely leverage off-the-shelf components, have still struggled to meet the stringent performance requirements needed to solve key edge cases encountered in everyday driving scenarios.
Luminar, founded in 2012 by Austin Russell, has taken an entirely new approach to LiDAR, building its' system from the ground up at the component level for over 5 years. The result was the first and only solution that meets and exceeds all of the key performance requirements demanded by Car/Truck OEM's and technology leaders to achieve safe autonomy, in addition to unit economics that can enable widespread adoption across even mainstream consumer vehicle platforms. This has culminated with last years' release of their first scalable product for autonomous test and development fleets, which has subsequently led to rapidly accelerating adoption in the market. During this talk, raw Luminar LiDAR data from autonomous test vehicles will be presented to the audience, demonstrating real world examples of life-threatening edge cases and how they can now be avoided.

Encoded microparticles have become a powerful tool for a wide array of applications, including high-throughput sample tracking and massively parallel biological multiplexing. Spectral encoding, where particles are encoded with distinct luminescence spectra, provides a particularly appealing encoding strategy because of the ease of reading codes and assay flexibility. We recently developed a microfluidic method for producing microparticles with > 1,100 spectral codes by ratiometrically embedding different amounts of lanthanide nanophosphorsn within them, which we term MRBLEs (Microspheres with Ratiometric Barcode Lanthanide Encoding). We are now applying these MRBLEs towards a wide variety of biological problems, from high-throughput and quantitative profiling of protein-peptide interactions to specific and sensitive detection of multiple bacterial species from blood for fast diagnosis of sepsis.

We start to lack the processing power and bandwidth to drive 8K and high-resolution head-mounted displays. However, as the human eye and visual system have their own limitations, the relevant question is what spatial and temporal resolution is the ultimate limit for any technology. In this talk, I will review the visual models of spatio-temporal and chromatic contrast sensitivity which can explain such limitations. Then I will show how they can be used to reduce rendering cost in VR-applications, find more efficient encoding of high dynamic range images and compress images in a visually lossless manner.

Image domain transfer includes methods that transform an image based on an example, commonly used in photorealistic and artistic style transfer, as well as learning-based methods that learn a transfer function based on a training set. These are usually based on generative adversarial networks (GANs), and can be supervised or unsupervised as well as unimodal or multimodal. I will present a number of our recent methods in this space that can be used to translate, for instance, a label map to a realistic street image, a day time street image to a night time street image, a dog to different cat breeds, and many more.

In this presentation, I would like to discuss with you how to establish a sustainable and smart society through the internet of energy for connecting at any time and any place. I suspect that you have heard the phrase, "Internet of Energy" less often. The meaning of this phrase is simple. Because of a ubiquitous energy transmission system, you do not need to worry about a shortage of electric power. One of the most important items for establishing a sustainable society is [...]

"Inaugural Linvill Distinguished Seminar on Electronic Systems Technology," EE News, July 2018

SCIEN Industry Affiliates Meeting gives you the opportunity to meet new SCIEN faculty and the postdocs and graduate students who are working in image systems engineering, with expertise in optics, computational imaging, human vision and machine learning. Read Poster Abstracts and bios.

Registration is required

Optical imaging probes like otoscopes and laryngoscopes are essential tools used by doctors to see deep into the human body. Until now, they have been crucially limited to two-dimensional (2D) views of tissue lesions in vivo that frequently jeopardize their diagnostic usefulness. Depth imaging is critically needed in medical diagnostics because most tissue lesions manifest themselves as abnormal 3D structural changes. In this talk, I will talk our recent effort to develop three-dimensional (3D) plenoptic imaging tool that revolutionizes diagnosis with unprecedented sensitivity and specificity in the images produced. Particularly, I will discuss two plenoptic medical cameras, a plenoptic otoscope and a plenoptic laryngoscope, and their applications for in-vivo imaging.

The research of human perception has enabled many visual applications in computer graphics that efficiently utilize computation resources to deliver a high quality experience within the limitations of the hardware. Beyond vision, humans perceive their surrounding using variety of senses to build a mental model of the world and act upon it. This mental image is often incomplete or incorrect which may have safety implications. As we cannot directly see inside the head, we need to read indirect signals projected outside. In the first part of the talk I will show how perceptual modeling can be used to overcome and exploit limitations of one specific human sense - the vision. Then, I will describe how we can build sensors to observe other human interactions connected first with physical touch and then with eye gaze patterns. Finally, I will outline how such readings can be used to teach computers to understand human behavior, to predict and to provide assistance or safety.

The past few years have seen a startling and troubling rise in the fake-news phenomena in which everyone from individuals to state-sponsored entities produce and distribute mis-information, which is then widely promoted and disseminated on social media. The implications of fake news range from a mis-informed public to an existential threat to democracy, and horrific violence. At the same time, recent and rapid advances in machine learning are making it easier than ever to create sophisticated and compelling fake images and videos, making the fake-news phenomena even more powerful and dangerous. I will start by providing a broad overview of the field of image and video forensics and then I will describe in detail a suite of image forensic techniques that explicitly detect inconsistencies in JPEG coding artifacts.