A Survey of Augmented Reality - Computer Science

In Presence: Teleoperators and Virtual Environments 6 , 4 (August 1997), 355-385. A Survey of Augmented Reality Ronald T. Azuma Hughes Research Laboratories

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LASzip: lossless compression of LiDAR data

LASzip: lossless compression of LiDAR data Martin Isenburg LAStools http://laszip.org Abstract—Airborne laser scanning technology (LiDAR) makes it easy to collect large amounts of point data that sample the

  Data, Lidar, Compression, Laszip, Lossless compression of lidar data, Lossless

A Multi-scale Approach to Nonuniform Diffusion

A Multi-scale Approach to Nonuniform Diffusion TR91-040 September, 1991 Ross T. Whitaker ... the example in Fig. 1a. ... and yet the outline of the shape formed by the overlapping circles is easily distinguishable. This visual boundary includes not only the smooth outline of the

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History of AI - Computer Science

A dose of reality 1940s McCulloch & Pitts neurons; Hebb’s learning rule 1950 Turing’s “Computing Machinery and Intelligence” 1954 Georgetown-IBM machine translation experiment

  Computing, Intelligence, History, Machinery, Computing machinery and intelligence

Real-Time Rendering of Real-World Environments

To appear in Rendering Techniques ’99, Proc. of Eurographics Workshop on Rendering 1 Real-Time Rendering of Real World Environments David K. McAllister, Lars Nyland, Voicu Popescu, Anselmo Lastra, Chris McCue

  World, Environment, Real, Rendering, Rendering of real world environments

An Introduction to the Kalman Filter - Computer Science

Welch & Bishop, An Introduction to the Kalman Filter 2 UNC-Chapel Hill, TR 95-041, July 24, 2006 1 T he Discrete Kalman Filter In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discrete-

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SSD: Single Shot MultiBox Detector

SSD: Single Shot MultiBox Detector Wei Liu1, Dragomir Anguelov2, Dumitru Erhan3, Christian Szegedy3, Scott Reed4, Cheng-Yang Fu 1, Alexander C. Berg 1UNC Chapel Hill 2Zoox Inc. 3Google Inc. 4University of Michigan, Ann-Arbor 1wliu@cs.unc.edu, 2drago@zoox.com, 3fdumitru,szegedyg@google.com, 4reedscot@umich.edu, 1fcyfu,abergg@cs.unc.edu Abstract. …

Virtual Memory: Paging - Computer Science

–E.g. 5 levels of tables. • Instead of making tables proportional to size of virtual address space, make them proportional to the size of physical address space. –Virtual address space is growing faster than physical. • Use one entry for each physical page with a hash table –Translation table occupies a very small fraction of physical ...

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An Introduction to the Kalman Filter - Computer Science

1. Introduction The Kalman filter is a mathematical power tool that is playing an increasingly important role in computer graphics as we include sensing of the real world in our systems. The good news is you don’t have to be a mathematical genius to understand and effectively use Kalman filters.

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A New Approach to Linear Filtering and Prediction Problems

a linear dynamic system (Wiener filter) which accomplishes the prediction, separation, or detection of a random signal.4 ——— 1 This research was supported in part by the U. S. Air Force Office of Scientific Research under Contract AF 49 (638)-382. 2 7212 Bellona Ave. 3 Numbers in brackets designate References at end of paper.

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Motion Estimation for Video Coding - Stanford University

Impact of the Affine Parameters-150-100-50 0 50 100 150-150 -100 -50 0 50 100 150 x y-150-100-50 0 50 100 150-150 -100 -50 0 50 100 150 x-150-100-50 0 50 100 150-150 -100 -50 0 50 100 150 x y ... EE398A Image and Video Compression Motion estimation no. 12 Differential Motion Estimation Assume small displacements d x,d y: Aperture problem ...

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Chapter 13 The Multivariate Gaussian

discuss maximum likelihood estimation for the multivariate Gaussian. 13.1 Parameterizations The multivariate Gaussian distribution is commonly expressed in terms of the parameters µ ... need to explicitly invert the block triangular matrices in Eq. 13.12 (although this is easily

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Generalized Linear Model Theory - Princeton University

The numerator of this expression does not depend on unknown parameters and is called the deviance: D(y,µˆ) = 2 Xn i=1 p i[y i(θ˜ i −θˆ i)−b(θ˜ i)+b(θˆ i)]. (B.12) The likelihood ratio criterion −2logL is the deviance divided by the scale parameter φ, and is called the scaled deviance.

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