Tic tac toe is (exactly what re your thinking) and it s the first game I made. Made it in one whole day in Turbo C. It uses primitive graphics drawing and also demonstrates how to output an image. Written in C, also uses a library I got from the net (included) for image output.
The potential of solving real-time demanding industrial applications, using vision-based
algorithms, drastically grew due to an increasing availability of computational power.
In this thesis a novel real-time, vision-based Blackjack analysis system is presented. The
embedding of the vision algorithms in a compound system of other information sources such
as an electronic chip tray, reduces the vision task to detect cards and chips. Robust results
are achieved by not just analyzing single frames but an image stream regarding game-ß ow
informations. The requirements for such a system are a highly robust and adaptive behav-
ior. This is motivated by the vital interest of casino entrepreneurs in a 100 statistical
analysis of their offered gambling in order to support the business plan, measuring table
and dealer performance and give accurate player rating. Extensive experiments show the
robustness and applicability of the proposed system.
It’s your first day in the lab.Undoubtedly you are experiencing a range
of emotions: excitement, curiosity, anxiety. You will be working in
this lab and with a group of people, as well as with your supervisor,
for several years to come. This is the first day of a long commitment
and, for some, a hard road ahead. Which is why it’s important to get
off on the right foot.
Finally, after a great deal of effort and hard work, you have obtained
the results you were trying to get for such a long time. You may be
so busy (and tired) that you don’t even realize that you have indeed
achieved a certain measure of success. Perhaps it will take a fewmore
months before you can present your work at a conference or submit it
to a scientific journal.
Reconstruction- and example-based super-resolution
(SR) methods are promising for restoring a high-resolution
(HR) image from low-resolution (LR) image(s). Under large
magnification, reconstruction-based methods usually fail
to hallucinate visual details while example-based methods
sometimes introduce unexpected details. Given a generic
LR image, to reconstruct a photo-realistic SR image and
to suppress artifacts in the reconstructed SR image, we
introduce a multi-scale dictionary to a novel SR method
that simultaneously integrates local and non-local priors.
The local prior suppresses artifacts by using steering kernel regression to predict the target pixel from a small local
area. The non-local prior enriches visual details by taking
a weighted average of a large neighborhood as an estimate
of the target pixel. Essentially, these two priors are complementary to each other. Experimental results demonstrate
that the proposed method can produce high quality SR recovery both quantitatively and perceptually.
