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ACTIVATE 2009: Computational Thinking
CMU - Carnegie Mellon University
Pittsburgh,PA
July 10-13, 2009
Participant teacher presentation - cellular automata.
This photo is from July 13, 2009.
Photos by CMU - www.cs.cmu.edu/activate/photos/comthink/index9.html
These result images are from the first homework assignment of my Computational Photography class at Columbia University. For each image I applied a number of face detectors to the images and determined the best rotation give the number of faces. I also classified the image as having being individuals or group shots.
These result images are from the first homework assignment of my Computational Photography class at Columbia University. For each image I applied a number of face detectors to the images and determined the best rotation give the number of faces. I also classified the image as having being individuals or group shots.
On December 30, Ambassador Heidt and Minister of Education Hang Chuon Naron were on hand for a lecture at RUPP by world-famous scientist and entrepreneur Dr. Stephen Wolfram entitled “The Future of Computation and Knowledge.”
Dr. Wolfram is the founder and CEO of software company Wolfram Research, based in the United States, and the creator of the Wolfram Language, which powers the free “answer engine” Wolfram Alpha. The talk was organized by the Ministry of Education, Youth, and Sports; the U.S. Embassy; the Cambodia Science & Engineering Festival; and the Cambodian Mathematical Society.
[U.S. Embassy photo by Un Yarat]
These result images are from the first homework assignment of my Computational Photography class at Columbia University. For each image I applied a number of face detectors to the images and determined the best rotation give the number of faces. I also classified the image as having being individuals or group shots.
The 17th of May is in the sky.
the 18th is on the top of the mountain.
the 19th is on the left mountain, going all the way to the ground
the 20th is on the ground.
These result images are from the first homework assignment of my Computational Photography class at Columbia University. For each image I applied a number of face detectors to the images and determined the best rotation give the number of faces. I also classified the image as having being individuals or group shots.
These result images are from the first homework assignment of my Computational Photography class at Columbia University. For each image I applied a number of face detectors to the images and determined the best rotation give the number of faces. I also classified the image as having being individuals or group shots.
fyre1.0 screencap
yre is a tool for producing computational artwork based on histograms of iterated chaotic functions. At the moment, it implements the Peter de Jong map in a fixed-function pipeline with an interactive GTK+ frontend and a command line interface for easy and efficient rendering of high-resolution, high quality images.
This program was previously known as 'de Jong Explorer', but has been renamed to make way for supporting other chaotic functions.
All the images you can create with this program are based on the simple Peter de Jong map equations:
x' = sin(a * y) - cos(b * x)
y' = sin(c * x) - cos(d * y)
For most values of a,b,c and d the point (x,y) moves chaotically. The resulting image is a map of the probability that the point lies within the area represented by each pixel. As you let Fyre render longer it collects more samples and this probability map and the image becomes more accurate.
The resulting probability map is treated as a High Dynamic Range image. This software includes some image manipulation features that let you apply linear interpolation and gamma correction at the full internal precision, producing a much higher quality image than if you tried to create the same effects using standard image processing tools. Additionally, Gaussian blurs can be applied to the image using a stochastic process that produces much more natural-looking images than most programs, again without losing any of the image's original precision.
Bleue Capellette
HDA : Consultant
Client : ICADE
Architect: Arquitectonica
Date : 2008
See more at : www.hda-paris.com/
These result images are from the first homework assignment of my Computational Photography class at Columbia University. For each image I applied a number of face detectors to the images and determined the best rotation give the number of faces. I also classified the image as having being individuals or group shots.
These result images are from the first homework assignment of my Computational Photography class at Columbia University. For each image I applied a number of face detectors to the images and determined the best rotation give the number of faces. I also classified the image as having being individuals or group shots.
2014 Masters Position In Computational Fluid Dynamics At University Of British Columbia, Canada. Applications are invited for funded masters of Applied Science (M.A.Sc.) position in the field of Mechanical Engineering at University of British Columbia-Okanagan campus. Position is open for Canadian and international candidates. Applicant should have an undergraduate degree in Mechanical or Aerospace Engineering or equivalent. Knowledge of fluid mechanics/aerodynamics, numerical methods and computer programming is essential. - See more at: www.scholarshipsbar.com/2014-masters-position-in-computat...
These result images are from the first homework assignment of my Computational Photography class at Columbia University. For each image I applied a number of face detectors to the images and determined the best rotation give the number of faces. I also classified the image as having being individuals or group shots.
On December 30, Ambassador Heidt and Minister of Education Hang Chuon Naron were on hand for a lecture at RUPP by world-famous scientist and entrepreneur Dr. Stephen Wolfram entitled “The Future of Computation and Knowledge.”
Dr. Wolfram is the founder and CEO of software company Wolfram Research, based in the United States, and the creator of the Wolfram Language, which powers the free “answer engine” Wolfram Alpha. The talk was organized by the Ministry of Education, Youth, and Sports; the U.S. Embassy; the Cambodia Science & Engineering Festival; and the Cambodian Mathematical Society.
[U.S. Embassy photo by Un Yarat]
These result images are from the first homework assignment of my Computational Photography class at Columbia University. For each image I applied a number of face detectors to the images and determined the best rotation give the number of faces. I also classified the image as having being individuals or group shots.
These result images are from the first homework assignment of my Computational Photography class at Columbia University. For each image I applied a number of face detectors to the images and determined the best rotation give the number of faces. I also classified the image as having being individuals or group shots.
These result images are from the first homework assignment of my Computational Photography class at Columbia University. For each image I applied a number of face detectors to the images and determined the best rotation give the number of faces. I also classified the image as having being individuals or group shots.
These result images are from the first homework assignment of my Computational Photography class at Columbia University. For each image I applied a number of face detectors to the images and determined the best rotation give the number of faces. I also classified the image as having being individuals or group shots.
These two images are screenshots from a program I just wrote in Processing. The were taken just a few seconds apart under the same lighting conditions. The dramatic change in perceived lighting is due to a selective emphasis that has been applied automatically, in live, real-time, to images coming from the webcam on top of a modern iMac.
A region of interest is selected by the user by either moving object or the camera to place the interesting region in the center of the image. Given a rudimentary guess of a foreground-background segmentation using a circular lump about the center of the screen, the algorithm begins to repeatedly build a model of color likelihood given a segmentation label (a value between 0 and 255) then relabel each pixel with its most likely label. At the end of each pass the label image is smoothed with a small Gaussian kernel. Passes are synchronized with grabbing of new frames from the camera so, in this way, the label image from the previous frame becomes the prior labels for the next frame, exploiting temporal coherence.
The combined sharing of information across space and time allows the algorithm to track moving regions of interest even under drastic appearance changes. This comes with a trade-off for the region of interest shifting undesirably in some occasions. Though it is uncommon, it is quite possible for the region of interest to become disconnected. In the right image, several distinct blobs are visible on the door.
To create visual emphasis, the areas outside of the region of interest are darkened and blurred slightly.
Source and binary (128k, requires quicktime for camera access): adamsmith.as/typ0/sketch_070813a-001.zip
matlab-recognition-code.com/iris-recognition-low-computat...
Abstract
A shifting common filter averages numerous enter samples and produce a single output pattern. This averaging motion removes the excessive frequency elements current within the sign. Moving common filters are usually used as low move filters. In recursive filtering algorithm, earlier output samples are also taken for averaging. This is the rationale why it is impulse response extends to infinity. We have developed a low computational strategy for iris recognition based mostly on 1D shifting common filter. Simple averaging is used to scale back the consequences of noise and a significative enchancment in computational effectivity may be achieved if we carry out the calculation of the imply in a recursive style.
This code makes use of an optimized model of Libor Masek’s routines for iris segmentation obtainable here.
Libor Masek, Peter Kovesi. MATLAB Source Code for a Biometric Identification System Based on Iris Patterns. The School of Computer Science and Software Engineering, The University of Western Australia, 2003.
Keyword: Matlab, supply, code, iris, recognition, shifting, common, filter, low, computational.
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Pacific Place - Hong Kong, China
HDA : Consultant Design & Enginneer
Client : Swire Properties Inc.
Architect: Heatherwick Studio
Date : 2005 - 2012
See more at : www.hda-paris.com/
Bleue Capellette
HDA : Consultant
Client : ICADE
Architect: Arquitectonica
Date : 2008
See more at : www.hda-paris.com/
Bleue Capellette
HDA : Consultant
Client : ICADE
Architect: Arquitectonica
Date : 2008
See more at : www.hda-paris.com/
ACTIVATE 2009: Computational Thinking
CMU - Carnegie Mellon University
Pittsburgh,PA
July 10-13, 2009
This photo is from July 12, 2009.
These result images are from the first homework assignment of my Computational Photography class at Columbia University. For each image I applied a number of face detectors to the images and determined the best rotation give the number of faces. I also classified the image as having being individuals or group shots.
These result images are from the first homework assignment of my Computational Photography class at Columbia University. For each image I applied a number of face detectors to the images and determined the best rotation give the number of faces. I also classified the image as having being individuals or group shots.
These result images are from the first homework assignment of my Computational Photography class at Columbia University. For each image I applied a number of face detectors to the images and determined the best rotation give the number of faces. I also classified the image as having being individuals or group shots.
drawing on canvas with trear physics tendrils using texones creative computing framework which is based on processing
These result images are from the first homework assignment of my Computational Photography class at Columbia University. For each image I applied a number of face detectors to the images and determined the best rotation give the number of faces. I also classified the image as having being individuals or group shots.
These result images are from the first homework assignment of my Computational Photography class at Columbia University. For each image I applied a number of face detectors to the images and determined the best rotation give the number of faces. I also classified the image as having being individuals or group shots.
Attempt to compare the viewing angle of blech's 480mm telescope with my 500mm tele... and in fact it looks like those numbers are pretty much the same.