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Dr. Ren Ng - Stanford University
To demonstrate your success at Step 1, make an image like Figure 3.5 in . For the Lytro camera it will of course show a different large-scale structure, because of the hex grid and because the microlens images are packed in considerably tighter in the commercial product than in his prototype. You might enjoy also making a simple UI that lets you explore these ((u,v)) slices interactively, or even making stereograms using images extracted from different (u) coordinates.
Now suppose the image is blurry for a different reason. It is in focus, but it was taken out the window of a moving train (we'll continue to assume the scene is planar—maybe this is a billboard at the train station?), and the image shifted by 8 pixels during the exposure. Again explain and sketch the effect on the Fourier transform of the image, and confirm with an experiment ( is a centered line). You can safely sample this image using one fourth the samples—where should we place them? Sketch and confirm by subsampling and reconstructing and showing the effects in the frequency domain.
Ren Ng - Computer Graphics at Stanford University
Lytro has raised over $50 million in investments. It is currently working on introducing software to expand the capabilities of the existing camera model, with the 3-D upgrade expected this year. A bit further down the road, says Ng, could be cameras that will take refocusable videos.
Every photographer is familiar with the frustration of losing a shot because the camera focused too slowly, or focused on the wrong thing. Recent camera technology innovations at Stanford University provide a new solution to this old problem. The idea is to capture extra information at the sensor, which is missing in conventional cameras. Special processing enables physical functions of the lens to be implemented in software. This approach provides unprecedented photographic features, such as the ability to refocus photographs after the image is taken. The underlying technology also enables dramatic improvements in lighting and sensitivity. For the novice, this means a more reliable camera that makes it easier to take great-looking pictures. For aficionados and professionals, this technology means unprecedented control over the quality of each image pixel.
Ren Ng’s Genius: The Conception of Lytro’s Living …
His research has been featured in the press, including Wired, Popular Science, Digital Photography Review, KNTV-NBC11 TechNow, KTVU-TV Fox 5 News, Photonics Spectra, MIT Tech Review, Stanford Review, slashdot, engadget, and more.
Ren Ng recently graduated with his PhD from the Computer Science department at Stanford University, and founded Refocus Imaging to commercialize his research. His PhD dissertation won the Arthur Samuel Thesis Award for the best dissertation in Computer Science at Stanford, and was nominated for the Association of Computing Machinery’s (ACM) Dissertation Award. Ren’s interests are in digital imaging systems, computer graphics, optics and applied mathematics. He holds an MS in Computer Science and BS in Mathematical and Computational Science from Stanford University.
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The input to this process is an exposure series—a set of (LDR) images. The term “dynamic range,” which can be applied to any system that handles some kind of signal (microphones and audio amplifiers as well as cameras and displays), simply means the ratio between the highest signal that can be measured and the lowest signal that can can be meaningfully distinguished from zero. In the LDR image that comes from a single exposure in the camera, the dynamic range is not too large; maybe 100:1 or 1000:1. (Fundamentally this ratio has to do with the ratio of full well capacity to dark current and readout noise in the sensor of the camera.) LDR images are normally encoded using 8- to 14-bit fixed point numbers that represent numbers in the range 0 to 1. We want to extend the dynamic range by using many different exposures: the bright parts of the scene are measured well by the shortest exposures, and the deepest shadows are measured well by the longest exposures.
i expect to view lytro founder ren ng's phd thesis: ..
The Curtis laboratory couples innovative experimental approaches, high-throughput omic technologies, statistical inference and computational modeling to interrogate the evolutionary dynamics of tumor progression and therapeutic resistance. To this end, Dr. Curtis and her team have developed an integrated experimental and computational framework to measure clinically relevant patient-specific parameters and to measure clonal dynamics. Her research also aims to develop a systematic interpretation of genotype/phenotype associations in cancer by leveraging state-of-the-art technologies and robust data integration techniques. For example, using integrative statistical approaches to mine multiple data types she lead a seminal study that redefined the molecular map of breast cancer, revealing novel subgroups with distinct clinical outcomes and subtype-specific drivers.
Ng Ren Thesis - Dinner Party Playlist
Step 2. Extend your program to extract slices from the light field corresponding to different camera positions that are not on the ((u,v)) plane. As we discussed in lecture, this operation can be done based on the same light field resampling code, but looking up different linear subspaces of the 4D light field. You can find the requisite formulas for locating the right subspace, in one form or another, in [Isaksen et al. 2000], Ren Ng's thesis, or in your notes from lecture.
We can generalize, ren ng dissertation proposal ren ng thesis, j
Gastric cancer is a leading cause of cancer deaths, but analysis of its molecular and clinical characteristics has been complicated by histological and aetiological heterogeneity. Here we describe a comprehensive molecular evaluation of 295 primary gastric adenocarcinomas as part of The Cancer Genome Atlas (TCGA) project. We propose a molecular classification dividing gastric cancer into four subtypes: tumours positive for Epstein-Barr virus, which display recurrent PIK3CA mutations, extreme DNA hypermethylation, and amplification of JAK2, CD274 (also known as PD-L1) and PDCD1LG2 (also known as PD-L2); microsatellite unstable tumours, which show elevated mutation rates, including mutations of genes encoding targetable oncogenic signalling proteins; genomically stable tumours, which are enriched for the diffuse histological variant and mutations of RHOA or fusions involving RHO-family GTPase-activating proteins; and tumours with chromosomal instability, which show marked aneuploidy and focal amplification of receptor tyrosine kinases. Identification of these subtypes provides a roadmap for patient stratification and trials of targeted therapies.
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