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Algorithmically our red cup replacement algorithm breaks down into three main sections: cup identification, finding a suitable replacement image, and the merger of the found image into the original.  Each part presents its own technical challenges and solutions.


Our test identification algorithm is based on simple template matching.  Basically, the template image of a desired object is convolved with the original image and the correlation between the two is found at every point.  The correlation is then normalized with respect to the intensity of the original image, giving a correlation value in the range between -1 and 1.  This process is encapsulated in the matlab function normxcorr2, whith takes two grayscale image matrices and returns one correlation matrix whose width and height are the sum of the widths and heights of the original matrices.  

The program sets a threshold value (around .7 by experimentation) to determine if our template has matched a cup in the original image.  Each color channel runs and is compared with the threshold separately.  The program then ands the resulting filtered correlation matrices together so a match is only found if it matches in terms of red, green, and blue.  This prevents a red (100% red, 0% green, 0% blue) from matching with white (100% red, 100% green, 100% blue).  At this stage, all points that exceed the threshold are considered matches.  Inorder to find the actual location of the cup the algorithm finds the maximum correlation overall, records a cup at that location, and then masks out the area of the found cup.  This neutralizes the other over threshold points around corresponding to the same cup, preventing overlapping cup hits.  The algorithm then finds the next greatest maximum value and repeats until all points over threshold have been accounted.

Unfortunately, this approach only works for one size of cup in the source image (the size of the template).  To detect all cup sizes the scale of the template relative to the source image must change and the correlation must be run for each respective size.  Our algorithm scales down the original image using imresize and leaves the template small (to save on runtime by reducing the correlation size instead of increasing it).  After each small change in size the correlation function runs and saves matched regions to an accumulation array.  The function also keeps track of the masks of previous match regions so smaller cups aren’t found erroneously inside of larger cups.  The match regions are recorded at the scale of the original image, so the algorithm keeps track of the scale factor at each step and sizes the recorded region accordingly.

The search algorithm builds on the idea of template matching and expands it to a wider scope.  Ideally the program would exactly match the regions around each cup and ignore the cup itself.  Since our correlation function can not exclude the middle area, we had to use a different approach.  The replacement algorithm generates blocks around the found cup with a width proportional to the size of the cup to be replaced.  Each individual block is then correlated through the image bank (similarly to as explained above).  The main difference is that the search algorithm must consider all blocks simultaneously-- a match is only a match if it works all the way around the suspect region.  To achieve this, the correlation matrices for each block are shifted and merged by the displacement of the block from the origin of the replacement image.  This generates a correlation matrix that takes all blocks into account.  The algorithm then finds the region with the highest correlation from all the images, and passes that region to the merge algorithm.

We built our test image bank from a relatively small number of images and just used Matlab’s imread function to load each one serially.  The program runs the above block based correlation on each image, keeping track of the highest correlation value and its assosciated region.

Because of the block nature of the search algorithm, one simple improvement we made was to give the blocks different weights based on their importance to the continuity of the image.  The human eye sees lines and edges more than muted textures, so we gave more weight (by multipying their correlation matrices by a factor before the final correlation sum) to blocks that contained more edges.  This modification helped ensure that arms stayed continuous and helped with the hand problem (the frequent presence of hands over the cup).  


After finding a region to suitably replace the excised region from the original image, the new image is blended with the original.  We used a conditional blend to completely replace the red cup, and then gradually blended the surrounding buffer regions together with the original image.   Our blend algorithm used a linear intensity blend (scaled sum of the two images), but could be quickly improved with bicubic blur (taking blur information from above and below as well) and a more consistent merger (angled corners).  

Questions & Answers

Is there any normative that regulates the use of silver nanoparticles?
Damian Reply
what king of growth are you checking .?
What fields keep nano created devices from performing or assimulating ? Magnetic fields ? Are do they assimilate ?
Stoney Reply
why we need to study biomolecules, molecular biology in nanotechnology?
Adin Reply
yes I'm doing my masters in nanotechnology, we are being studying all these domains as well..
what school?
biomolecules are e building blocks of every organics and inorganic materials.
anyone know any internet site where one can find nanotechnology papers?
Damian Reply
sciencedirect big data base
Introduction about quantum dots in nanotechnology
Praveena Reply
what does nano mean?
Anassong Reply
nano basically means 10^(-9). nanometer is a unit to measure length.
do you think it's worthwhile in the long term to study the effects and possibilities of nanotechnology on viral treatment?
Damian Reply
absolutely yes
how to know photocatalytic properties of tio2 nanoparticles...what to do now
Akash Reply
it is a goid question and i want to know the answer as well
characteristics of micro business
for teaching engĺish at school how nano technology help us
Do somebody tell me a best nano engineering book for beginners?
s. Reply
there is no specific books for beginners but there is book called principle of nanotechnology
what is fullerene does it is used to make bukky balls
Devang Reply
are you nano engineer ?
fullerene is a bucky ball aka Carbon 60 molecule. It was name by the architect Fuller. He design the geodesic dome. it resembles a soccer ball.
what is the actual application of fullerenes nowadays?
That is a great question Damian. best way to answer that question is to Google it. there are hundreds of applications for buck minister fullerenes, from medical to aerospace. you can also find plenty of research papers that will give you great detail on the potential applications of fullerenes.
what is the Synthesis, properties,and applications of carbon nano chemistry
Abhijith Reply
Mostly, they use nano carbon for electronics and for materials to be strengthened.
is Bucky paper clear?
carbon nanotubes has various application in fuel cells membrane, current research on cancer drug,and in electronics MEMS and NEMS etc
so some one know about replacing silicon atom with phosphorous in semiconductors device?
s. Reply
Yeah, it is a pain to say the least. You basically have to heat the substarte up to around 1000 degrees celcius then pass phosphene gas over top of it, which is explosive and toxic by the way, under very low pressure.
Do you know which machine is used to that process?
how to fabricate graphene ink ?
for screen printed electrodes ?
What is lattice structure?
s. Reply
of graphene you mean?
or in general
in general
Graphene has a hexagonal structure
On having this app for quite a bit time, Haven't realised there's a chat room in it.
what is biological synthesis of nanoparticles
Sanket Reply
how did you get the value of 2000N.What calculations are needed to arrive at it
Smarajit Reply
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Source:  OpenStax, Red cup replacement. OpenStax CNX. Dec 19, 2011 Download for free at http://cnx.org/content/col11390/1.1
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