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Multiresolution Analysis, Form based Features, Thresholding, Edge Detection, Morphological Operators (28.04.2011)

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Automatisierte Medienanalyse

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So what I'm all to on new lecture multimedia databases and the today want to talk to you about a couple of things last time and we rode the with textured so we were talking about what makes attacked what makes a structure of both of them some some some image and were considering some from load of the feature on 1 end like like contrast all direct melody and we will be talking about some probabilistic ways of dealing with with Texas so we could predicts the colour of the intensity of the some pixel based on their surroundings of that pixelated the pattern and we were talking about high level features about Fourier transformation about a leaflet transfer owns bomb to give you an insight into what we can gain from other spaces like features spaces of frequency spaces out what evident beat that the transport transport all in which the difference between high level foetuses load of the features
The major difference anybody But During Nobody Are basically from high level features without losing any of the image information you can reconstruct the texture As just a different kind of and for Oliver if you use a the future your extracting some characteristics some measurements on the structure and the usually impossible to reconstruct real regional image the origin of structure from low level feature of the basic difference between 2 days we want to go a little bit into into into shapes we want to see I'm FVB highly can put all fingers on an object that opera in images and the shape of a best description of a description of what was actually is in the image and them that we will work a little bit on offer much resolution of the House and then go to the real shape a features of was of different ways of preparing off the commanding the image and detecting the basic at the city and at just that may make actually out the visual impression of the of the show
But 1st we won't stop eluded about my 2 resolution and the and the and the problem that we see the last time was that even full of the smell that functions that we useful follow offload image frequency for the intensity and the Distribution found the transformations can become are the obdurately complex wrapped up and we see the with the transfer on which is debt 7 different different ways that will used among wavelet 2 children and and 4 debut with the and even that was a high demand for the patient systems and and if we have not sell a bigger base for all features that this is getting even more complex and we have a lot of information the image on 1 hand we have a lot of of variables given by the face vector or given by the different frequencies in the assignment around the world waves and a slow in this Linear equation is definitely a very expensive think so that what we do with all we or any did go into some algorithms like The The Fastest Fourier Transform that makes it a little bit easier to calculate the actual Linear Systems
With the recent and then we have something similar which is called The Last which from who would of notice In and out on to show you a Hall this works to because the of this is basically a on the road of the foundation of multi resolution and and that is that you want after topics 0 1 1 classical ways of dealing with high demand computations that he will from the experience of very off name in the UK The and and and before the Bill was passed with the terms for a new can actually compute the result of Philip Linear patients system Linear time and only basically do if you change between 2 steps you Convert the image found into reduced resolution to get smaller images was last resolution and you start a 2nd thought and that is what you lost during the reduced their the reductions the icons Represent images the ways by 2 possible the basic features And the details And this is where we get in the party of the are so called the basic features are so visible in below and Load a resident of reserves of representation are used to see the big edges is to see the dominant colours But if you draw a deeper into the image you find that this is not actually thorough of the same colour But the cover changes slightly and these changes Could be Stalled in a different I'm part of the image or different from representation of the game and was these 2 images you connect the reconstruct the original image By the adding the details To the stadium pop up there And this is basically but was called mighty resolution and losers so you have a picture in many resolution and for each resolution That is smaller than the origin resolution All the details that could be used to reconstruct fioritura resolution 2 but in a different So you get 1 found that just shows that the basic get another file that he can for reconstruction if you want to Popular
This particular itself where you do is you consider the image in different resolution and the stigma of being a justice the intensity signalled its you go through the image and your a called the intensity of each left I do well you can Start with a single picks A single Pixo has just a single intensity The violent at more details have to say what actually It was a single Pixo it will 6 picks and the Distribution off the brightness all colours in this week so I was like that end something you Which goes to want the construction If I'd do that several times to ironed out at the original image up and this is the basic ideas so the representation of the image in certain blokes in certain resolution It is just time By averaging The intensity information And keep the difference is what he used for every may be an example will make it more clear
What you basically do what they do You taking The big picture That might 16 Picks up In the 1st step will always take the pixels Every which they intensity value and stole them as a single pigs Oka It led to a couple of times with another 1 pigs Press The something I've to nobody is of what happened here What do loos by the average told the origin of the new look like Kent and his commit just under by differences surpassed on it
They've rested image Became and said Became minus 1 is just low resolution where the fixes have been than the average and at some point we arrive at the great though the worst revolution resolution about just a single pigs up And the single Pixar Pixo shows me the average colour Note that in round a when we were talking about about the foetus for colour Everage colour was a necessary and a meaningful which in a way so even This image tells us something About the it gives us a way to distinguish between image because if the the images were totally different to stop with the also there are also that that the average colours are different but she's not is this this way around is not as true as the other way around If the average colour of to images different than the origin of images must have been before It was that at different can still come down to the same average So we have the examples of and a mixed being being half red and how blue and an image being totally lilac by their the same ever sculpted but still that different images that if the UK which would be green no way of getting the same as basically the and back But still we have to go back from The singer image tool reconstructed so we still have to ask about what's happening he had we go back and that is basically the intensity of pixels off a obtained from the coast How does the back transformation work and family
Basically The intensity of the peaks in the low grade the means of a set of corresponding pixels in the high resolution great Foca and Whisker 1 step by high the number of pixels So Apple with half the number of pixels to pixels become what they and if to pixels become 1 When storing the inflammation Contained in the 2 Pixo can basically be done Howell But the IAAF to pixels intensity I'm intensity Jake and a goal to 1 Pixo which as intensity Yes There I've just days divided by 2 basically a pin just the average What do after struggle is our so 1 to have this step But It Yes on the Exactly the difference between night and day because they find their what the mood and the and the difference was restricted of the Put my fingers on what they she were if the difference 0-pc there must have been exactly the same colour on the spot if the difference to the mass of been ride 1 low on higher folk and so this is actually want to do not I'm Starring the difference He UK and and this would make so that happens basically is ideal from 200 300 200 to 150 200 from 150 to to run to a 100 for the year so I'm was kind of like the boiling it down in
The party is under way and in the word of a pet this allways were to cope from Saudi 100 can I read and 100 200 simple down to 150 200 size template downed and the vertical accents and and from 150 200 assembly down to of 150 100 up so now it exactly equipped of the size of the original image of this basically is hot vertically and half horizontal and this is bygone and as you can see it is getting closer and cost of the not see out the The the actual the actual objects and more at some point but you just see very close representation of the future now for each Pixo in the offer to be made
There was a corresponding pixel In below resolution rusted this to write to By repeated averaging and If I'm the intent city of a pixel at some point and for each takes we have what is left of the rich part class the different that we use to kind of like simply don't and and this is the tale information that is needed to reconstruct the intensity of the actual picture of the actual picture and if where case that the way in resolution in terms of resolution from the Oriental pictured with left to educate times the differences and then we find the arrived at the origin of picked So this is last less decomposition though if we only look at that The 1st part it very lousy We can do last things with the further cost which is interesting but we need the detailed information to kind of like time so and 1 of the ways that was basically already hinted at averaging and different 6 or what we do but we take each to fix also entrance from them into the Duke of Kent
Easy Then we take the next 2 Pixar Owais horizontally and the and The ship Take them to a mix of but what do we have to do basically in if I'd duty on the fight and the mind To have an average of 7 And after saying how with the different from each other they give them again Well basically the 1st 1 of the 2nd 1 is 7 last to buy keep the to 4 9 7 plus 2 If I'd do 11 9 and want to arrive at an average of 7 what will be other number be Of 55 of the idea the same for the aid and the fit again for the 2nd number by recalled the different with respect to the average which this well and 6 makes minus to talk and reclad the mind to what his eye have to think what we were was the the 1st number Well have the 6 as an average of the flow as 1 of the pubs and the other part has to be a again I've to decide for even want it works either way just take want you can stand the difference was respectively 1st ball was offered to 2nd doesn't matter but it has to be the same way as obviously The 5 foot benign and now because what basically it you were waste put together 2 pixels to fail 1 average picks up here And the information that you need This depending on the average obviously And on the difference was respected either of the number numbers it doesn't matter if you choose the 1st number of number talking playground and stay with a study confusing if you Dhirendra need BBC any number Unite Benidorm work that occurred immediately say well defined 7 this the average and the and the night is 1 of the numbers and and the and the other members of the have to do this solution Which is easy But Basically what a candle but but you have to decide at 1 point but I don't want the demand which take but for you want and you can see now is that we keep the every just And we keep The differences A camp It's the same amount of information It is now So what we saving Numbers you aged numbers to you Copes act And 8 numbers here For And replaced 16 numbers by 2 times the number yet What do we say it welcomed the group yes to you do get a preview of the if you just skip the details and the and use this yes we can do something with a silly of to stall same out of date and only Tricky P Yes there will be a while P The moral of the tale is that it Yet that was the basically ideological a high demand of a solo we can we can see things and in the end of a long resolution version of that I'd interesting phosphorite and this actually doing but still as that they have arguement about the storage Yes but yes the exactly if we start The differences with respect to the average its from old numbers you and most of them will actually be Zero because they got pixels and in some image you relentless if you guys have the Orange Fletcher now like these big so that will do well in in the same colour differences hero will be very off in the same difference and available was not right and was indifferent and happens 1 black picks up the difference This is the interest in part of this is where the actual compression comes from many of these are 0 But and this is kind of like the but there with storey 1 at some point you arrive the single Pixo P update said on Friday the average intensity of this image of it looks like for or Kent Everybody understood why I'm doing Get
So what I'm doing In signal-processing that has actually something that is the even even nicer to do then the averaging and and and simply what you could do is to say I'm hypothesise load of the year a signal that this year we I you and what you do with with a high cost and low cost of as the hype after the with only egullet high frequencies low part of with only figure The load Africa it for this basic the what happens with the help of those would that of the man and is that what happened was the hype part of the and and the hypothesis allways basically out the baby with the of high all of them And the low cost this the baby wave of law that the low cost information contains a says the and inflammation and the average us the high possibly fulmination contains the detailing fulmination the difference care But
I'd have been expecting which details low cost of the average end of cost you could use possible Applications off the field as again polls horizontally investigate began to due to a high cost US you can to a high cost to the followed by a lot of the you could do a lot less than the full of high cost the that you could do to low cost to the UK and so this is basically should be the You because of the cost of and his the result of the high cost of the falls subsequent reconstruction off the made this the details from each and you look at the low pasta the image of the city and edges for the average colours for whatever you are interested and budget
It basically looks like that itself for doing it is you take some image You down downtempo at Along the white exes with high cost of soap and a low cost but can't hypothesis and give you the details of the information that was changed The differences And as said love them on the road the of the White part Of this image can only where something happens there is a difference And these are the average this alone a care I've done it along the road to horizontal access X excesses Knowledge to would along the road collect so again by can do it a 2nd low-cost to Which was still give me a small a match Quetta the size of the original image and Wright and still containing many of the details because it's 2 times sampled the average and they get full possible this off Getting all the different These are the differences In the vertical reconstruction these are the differences In the horizontal reconstruction and these of the differences in both becomes a path to Times iPod Right This basically what I'm getting if I'd put together and these full images what do again The are original image Reconstruct perfect reconstruct I'm not throwing away any banned these sale images are in terms of resolution Exactly the original image It's just that Some of them are almost life That's very small differences very many 0 And that can seem like and get a good impression of a preview as he said some of the originally image to look a case What I'm saying this basic the worried do in in my resolution and and if we are doing it it looks like that of this allways and a double height and these although the difference at so the total number of each pixels in the status is saying that the Dior into the size of the image does not change it
At Paul than earlier doing all 4 combinations yes Again And what I basically doing it is you doing that white sell you go from the original image for 4 times the and 1 That will possible to be image for Load details sorry for for for basic feature of the new take this and again to the tools and the different combinations of the future then now with and even smaller and I could do that until you left with a single picks up and the common good and bad that Inglewood detailed information to the origin of fixed Icann schmooze reconstructed and image A Case this is basically a mighty resolution and a really care Osama full either It's actually so easy and a would be global food
Well during a so audio basically do we now was the I'm with the feature victory is used says the expected value and the standard of the Asian off the base of coefficient at each resolution to for example a few streets stage resolution you get 20 diamonds will feature of every because for every single 1 of these images is safe the spectacle you and the standard aviation so to numbers have turned off the field times to is 20 year 20 dimensional feature And this actually gives you a very good impression of the image that actually allows for very good Comparison of different images of I guess But The Because you need to be expected value and the standard of each exist to measure this is what the fact that uses to numbers your installed the expected value of which is a kind of like the average Colorado and that is that the average intensity but just having the average intensity is but to 0 2 0 2 2 mistake so you could have a lot of images having the same expected value but the and they might be different in the 2nd division yesterday might also be different askewness and and whatever it may be you now like so you could at moments to to distinguish between but the basic idea is the same as as we had with the greyscale images of all the texture mapping intellectus taking taking out statistical a moment to describe the actual image and this is where we do was the 1st to to statistical moment expected values stand defeat which we could add some help if we wanted to but to Aichi OK and a 20 dimensional feature does is is a very what the very easy to hand If which we would have 30 damaged but that it would take skewness would have plenty damage to the UK and the can Getting more more good that she brings to or 2nd public far too late shape based feature on the it it's really down to earth we want to see what in the picture we want to see all take we want to see shapes tables and chairs and you faces and everything and we have to find a way to describe it
Describes the shape using the picture information but not representing by picture information but by representing a something else we're with injury Thinking about its not so easy So I'm never less we have to think of a way out represent them because of the state system in which it really contribute significantly to the semi if we seek to images of caps 1 can be Brown when can be why we still notices simonetta to because the image contains and if we see the image of a tree or colour could be approached trio beach 3 0 0 3 0 1 of maybe the colours may slightly change But still we get the impression that similar and the impression is very off transport by the shape of things because it's just 3 shaped you know who you are Riesch and seeing that shaped everybody would agree yet to treat a and a one off Draw a camel something like that any more The fate last time with but that's the way it at that I'm So basically of also if you have shaped you may have died semantic information that this is the tree she Give you an idea of what the the author depicted in the show yes it could be a bunch of Colton The iPad on the state of its Tree shape but it's not treat but still something walks like a duck and talks like that it might well be the year that so is something shaped like lemon it might be time are now 1 of main and them
If we look at some typical shapes we find that the things come in and very many files for all these are immediately recognisable PM may be that not but the families are almost immediately recognisably chess though images utterly different that different coloured they differ in in Indian in texture firm here this woman texture you don't Or can they differ in Why actually in the shape of a basic shade For with seems to be there Club Kent And this is probably what makes us believe that such
And if you have a combination of simple shaped features of something from some round shaped some Triangular-Shaped-Cloud babies squid as and you end up other features before its of the colour of old text you could get a retrieval so for example if you around object in and out injured and red yellow which picture that might be Some said Or some of and with this kind of like Round Part and the rest of orange yellows order and that it might not best might be orange or the beach very well but I'm means it all we have and bomb
Identity fraud he was was 1 of the Ivy and to of looking for for cross shaped and of things There were quite well you know what you see that most of the match action to contain crosses and and even if they have enough like this the exact captain here or they split almost they will still be recognised as crosses and the something that can be done and that the fundamental problem of course is how do we recognise the shape of things to do and it was easy to 2 0 to construct something like added with the chasing a like is out the Apollo wasted on and he is the public lean to and these are the 3rd at the feet of the church for where they can do that easy for him on a flight some of the command the image and find out what is the the same but the computer to do that and in some automatic weight and of course as well as early as ideal semantic where mapping and it was below that suggested the shape of the shed
She has a certain shape because of has function And it doesn't make sense if you care upside down because he said all of that has to have a chat but the police true that from the shape of something you camp can actually OMX of the a computer wanted it is what was semantics behind thing and is not true that we have to have described the shape again with somebody to evict a with some load the features features and that the engine once we are able to describe shapes based on this descriptions we have to find simulator to measure To compel between different show is a table shape really different from a chest shape What are the sum error tease where it is the difference of a computer that if of this question the fundamental problem that we have to deal with the during next part of the and the 1st topic we will have it is segmentation a segmentations is really fundamental because not where the shapes that we know what our in the pictures all social on a picture of a topless in which is a Sunday on everybody can see that when it was time
While it's behind the drought It's not shown in the picture side don't have anything some shape Seat We knowledge that the picture does not show it it's all cluded It happened very off would only interesting shapes anyway Amin should you I'd should you help the look shaped They have about you Do we have felt you obviously large shaped Ways Something which she Which driving recalled where the interesting shapes But other the states that would make the picture seems symbolise to some of picture a pink So I'm during the consider all the different shapes to consider only be held on May Be only those in the 4th round of this is probably more of the image that it is a sundown image all than it is now image That problems were facing about what to do with them on the way to consider segmentation can be announced a problem because the the reprimand shade and the image and those not
Group The brush shaped We need The move Where and in review This unit The brash shaped Interesting concept the how to make out the images Because lots of the price and they are clued each other and they have tried in the UK and how to see what popular and 2 words the brought out of the city and and Brooke or mulch Where does this leave and Difficult the the same year remained the and and know what it is like them some rock full Meanchey but What of the shape of this kind of rope pupils into a new and more complex for we were brought up in the hype to make out her still yet of visual impression from its is that this is something in terms of shape and we can definitely see something The stop usual except 3 Blog It's all happening simply just for a complicated images and the Dome
The 3rd said 1 part is really all part of the state a book and sugar recording has been found and no with some minor with some usually of round the Sahaba livestock to adjust Take the knowledge and say yes but this is definitely a around some and if somebody look for for some always look for something round and to adjust to Stop this part Offers some so anybody can look for images whether some is how long The difficult part segmented of Kent
And by this segmentation as is 1 of the problems that the multimedia community is working on for 8 in all like and and an automatic SembCorp segmentation image segment of still kind of the Holy Grail of of computer vision and during off but maybe it has to be done automatically but but could be done semiautomatic that the kind of like it suggestions awful shapes that and images and just accept them a decline in the number of assistance is not and has been in in the early version of much media retrieval but it was it was not done automatically but there were some semiautomatic tools to forget that the use unit brought into the picture and the cost of the loss of some of us thought so for example was idea MPs cubic which is part of the beach to database the image extended but there is no way of doing automatic It's the way it is so idea that this this prototype thing you here and there you really have to go and an end to a new year low of tools like you probably no from from of British all PPL something like that of the of the last World of books kind of like that at just do something you had some some automatic of semiautomatic things with the kind of luck still some of the for most of the same colour some like to do that but they see the what you have to do this kind of have to draw the French
The adventure of again and you ended up with the different of and shapes and this for recorded the system as the Dutch
That time has said 1 of the of ways of doing it was kind of like I'm so called that feeling so if you had the early distinguishable regions in that nosy process rather things of of the same colour which could do with you could you could just Moskvy image and they all want a what the this this shape and and then fillet all the pixels was sustained colour and you would you would end up was a decent and the state could because of the image that either a it and someone on what from of surfaces and assume as you have something like here where the with a shape some of the broken it will immediately Run out and and this is no longer moonshake This is kind of like you know what I would seem more like that So there are made said that to the difficult and them many research project in multimedia retrieve that have been working on the top deck of the book unanimity just trying to figure out how to talk to a seat at the ground images here for example tigerish and but would see this as a time of not enough and that of enough and that sitting there for every image of the web and and like Annotating of the shapes the current shaped by hand I'm not some solutions somehow usually algorithms on the work 1 on special cases so if you have a special collection for example the of built on movies and the idea was to recognise people shape in in in movies and if you have something like that the and there is usually a way of doing segmentation automatically all at least Samuel with that of her behind the decision but it is fuel working on the Web or like general images which in possible it as he may be somebody things of level best difficult
So you to the segmentations problems actually from the commercial that basis shaped features Profile you remove So the prototype on the 1st set but I had them and the representation and compressed not shake used to enter the world of the segmentation problem made them unusable so they were removed from for 4 4 idea So the between 2 weeks and there Fouracre into media coverage and also for the IN for mixed that all decided to remove them There is an interesting point haul to do with the segmentation out to do the kind of like the PM the shape feature so we would cover them the lecture maybe some of the things that segmentation where or you a selection of images is of specific kind so you can have segmentation automatically and don't want to you to come to not know how to represent shaped features competitive that you should be at for that imprint put you can do is to find a film as the better perimeter of something the shape of a sail of something
The So the actual segmentations Ohio 3 get the old liable to get the sort of thing
You can have some nearest sell if you area has with similar brightness colour and texture of the meat may be long to the same of the of A reasonable if you have differences in brightness from 1 area to another them might be edge between the and edges over what is a good candidate for being part of Suez opened the visible caught at detection Of course You can fill spaces was more ethological operate doesn't just Mostyn would go into the later just see how I'm like wedded with a lot of here to see how far off some space extends to You may Sigmund the outlined as closed so you find the edges and put them together too close to and that might be the shape It also comes approximate it was some sort of political of the blind in early just interpolation and that is a large number of other procedures to to halt the rise and after more perfectly but they don't have complement each of the depending on called on what collection you have you might use that 1 on the other
But the 1st 1 is basically group show we are sure we do so in a small but not much Yes exactly so I'm Wrestling as basically 1 of the basic ideas The shape of the area That have the same right value Belong to the same cheques And but for wrestling icon distinguish different area was was different brightness was different intensity UK and some area that youngest wrestled and some very falls under stress and but it is Mostyn not images you will find that the of the ground images are light From them the background Well at least the different even if the dock all their black than the background life so photographing something in front of the similar came background and means not usually what he would do so are a certain stress old connect to separate between regions and what I do is basically you say that area that matically related like this Trulia celebrity value and can be separated from the surrounding space not like the
It is actually the best none in OMX and automatic men affect trading so quality-control of part to try to find out something as an old not under computervision knows lot of these wrestling some of these wrestling algorithms and and we will just have a couple of what happens basically to take the brightest inflammation of the image And then you say OK output in the 1st told the Stop the bright area so like you And these are the black area like the screws over a And by just figuring out where the correct wrestled is icons Separated account from for ground and then icons
Do the shape segmentation Well I'm these ways is just take some wrestled up and this is the 1st of everything begun about 200 in brightness is background everything be For something and the case of fine images for easy to do but he's Eugene with great value images that show range of different colours and where was the difference is actually up their unnoticeable but you could free put your thing on whether this is a light at image as of Waldouck images level and the fixing the wrestled with all be not very good idea so we go for a flexible threshold that depends on the right about his to rhythm and tries to kind of like the idea what part of the histogram belonged to the full ground and belong to the back I'm usually the if you take his over his move that somehow all but you make sure you keep the peak to keep the masses of the distribution of and then you go looking for them for for the for
I'm 1 of the earlier algorithm that algorithms by Redland coloured which basically tried to divide the great values to Premier and a two part of any Calculate the expectation values of the great values in the Left and the Right so he separated in the middle and do you calculation of expectation value and not the new vessel should be the average between the 2 expected The show is the 1st told the Everett and recalculated expectation value The idea is that the expectation values in both part of the image Move to with 1 of the peaks Of the of the of the system and that once it reaches a peach that stabilise it doesn't move any more so the average dozens of any more than that basic when the algorithm finished up and the Saudi basically do is the biggest digram here Thinking the England You just had into 2 pubs become pupae expect Asian value of this part and of this part of the here and now The for granted but more new over her under the GNU recalculate The dilemma tel He look the loop look An cuticular In the middle of expectation values he recalculate expectations of UK easy to do said 1 is triangle algorithm some also about the same time which Connors's from where you should do it as you don't have a peak in the system gram Depending on whether the image is also for ground 0 Bakr of small images would have to be background peak Of the 4 Grand object have grumpy opened and wanting to do it is by a long queue connect the highest peak With the minimum In the distribution of Kent And the and iPad Pressault where there is a maximum perpendicular distance So basically out 1 to get at the foot of the peak And this is The where take the the threshold up care We connect the highest peak in the scramble side brightness value we maximised the distance to the connecting lines and distress sold is a minimum of 4 weeks shift of some comfort to the cost for so allow for some much look Also possibilities to kind of be termed of 1 of the 1st on the eyes of that is and iterative way of finding threshold this is just doing the solution for the perpendicular up Chris The he's right education example also and this is very often don't actually this wrestling is in medical segmentations if you have some of grammes for for example and want to find out something about of the baby's all can sell all different opens a you do every imaging Toulouse that kind of colour them you to the fact felt like which showed some of the stress early and using the extra his digram you you can find a way this facility so this is the interesting cost care and this is kind 1 of the background part can be removed from the image and and the but and the in what what actually happened was that was what should be can
That set up the 1st thing that also and a new set of rules and that the the value 1st on on different image area as we just as not just a 1 background and 1 3rd round of the image but there might be more Dixon the image the land together and I'll just but the images into different types and do settling on average
Can also because the bid and the image collection 1 of the order of nature of the images that would you get best and if you have the time and the best of reasons while the possibilities of especially
Doing doing Khaled images of so advise look at this in a two year segmentation works very well and I find all the flower beds segmented that they are kind of like you see here that these parts that are not really belonging to a sold out changes and colour you couldn't you couldn't do that with a lot but with toting you can't and found that that kind of had a very good segmentation that it out on the other hand as since he here with whatever it is I'm 1 of the things I'm the different types of talk about the light Politeia are broken down into different world order from public the animalness that they are more The all the other 1 is the body that you probably would like something on the amount that the SAS Chase and not really having all the different exotic and the lid of a tricky for the 5th try and sometimes for about them Aga Questions wrestling But Lamno
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Get so that style along with the 2nd part of the front following was 1 thing but the eyes said that you could not only take that the area of the same colour but I could also figure out what the benches the limited of these areas and this is usually done in in X detection so I don't look at the area was a look at the limits of the law
And the beat the idea is that once you have closed around in some area and this is the shape of the and if you can constructed in such a way that semantically consistent objects us around by the the 1st perfect than you have read the silhouette of some of the and and what would you what basically do is you look at the bright function usually bid of what we already did when we were trying to figure out what Texas on so when we were figuring out what taxes are and how they can be considered but we looked at some the direct melody of part of the tumour mesh enough so we walked through the image in some direct and then so well here that it is light years it is likely that the stock had lied like is that the intensity function runs like lied and and its Dawkins than slide in the stock again no like that and the PM Looking at the intensity function as a real valued function you know you can order with look at the the derivatives and street where are extreme points of the function of all this point although he correspondents basically to this point of their because this is where of the documents and You came from light and you go back to life again finding this extreme points to maximise minimum in the function of you do that But I do like I didn't School in 0 1 over discussing the characteristics of of Look at the 1st derivative and look at all places where the derivative 0 because once you have the maximum minimum It's a point where the derivatives Sell This 0 And as soon as you move along with her The derivative gets nonfuel up Kent An interesting enough That different kinds of kerfs sold for example The because going like that A P yes For example This is 1 of them Also here you do have a point but the derivatives 0 but it's not a maximum them It just tell of Kent and the and how do you cheque whether to settle whether they are a real maximum momentum The book The the only ones That's right exactly city look at the 1st derivative It has the following after this point the same kind of kind of increased its either decreasing on both sides what increasing Mumbles but it doesn't change its sign What you need is something that really changes Negative to positive or the other way around for the next and the and the 1st 1 but they are 1st function is the gradient if you think Two-Dimensional in terms of the function of this is the 1st derivative and the 2nd 1 of the last of Britain's 2nd derivative did describing the increase and the cream of the 1st of of the gradient
So what we are basically looking for we are looking for the gradient at some point And the problem was that all this is that it's a stepped function And I'd say it's not a continuous function because the Pixo has a certain intensity value and the neighbouring takes has a different intensity value and not that of his No no kind of like the way how they hold of the the by the transplanted of called for the kind of movie to into each other and but what you can do it you can't kind of like trying to estimated the differential functions from the point of you you consider the point the pixels cities and samples of function and then you kind of Interpolate the actual function for example for a transformation doesn't like that Then you just estimated causal for function for each picks from the immediate enable so you don't look at the function of the and image that once you have a picture You just say where I'm interested and the gradient of the pixel here in the middle of solo do is basically look at all the neighbouring pixels And just consider differences With respect to the pigs and then eye concede that the intensity got all down and this is all I'm interested and I'm not interested in the real function and and holidays in the UK and US interested is more or less and if by some some some intensity when going for the image in in any direct goals from from from brightness to adopt and up again to brightness then I'll know this is 1 of the point of I'm looking for an extra can see that Looking at the street pixels because of the year the intensity of 35 year to 0 in the eyes of some of the and only to reconstruct and if you do would like that are 1 of the 2 is that the so called gold said that it expected the estimated gradient just by looking at the the differences in the neighbourhood of usually the neighbourhood is a little bit bigger than just 1 takes Latino because you might have some some some voice that will give you a lot of holes positive but if you look at big enable you can really see by averaging opposite the right inside left side pixels of each Pixels full that you have to determine the gradient time you get a very good impression that this is an edge minimum local of a pet This was about as much faster than reconstructing the actual functioned based on the simple points but obviously there are some sold this is the way that we are up there
Some of the gradient based method 3 Calculate the magnitude of the gradient each point From And just show high gradient Because of a change in colour Area of the same colour showed no gradient at all just let going on 1 of that and then we use algorithm to separated the edges from the regions so it would be like that and we go through the image we find for example of the gradient here is not changing But suit as we get something The gradient has of the increase Oka And then This pointillist eating face Then it doesn't change much for the next time until you suddenly it goes into the background so we have a strong decrease the UK and and after that the change We require these 2 points and the 2 point of basically a point over here look by taking all these points from the match And fast thresholding the point was high gradient from the point with a low grade we will get quite except for UK Now that was a little bit suggested for because I'm using a by an array of kind of mine repayments here for a while about the EDS's where it's kind of like changing very little gradually into some of the more tricky is
So the actual advantage is that it is that it's amazing a simple to do it that way but the song as you have not assume as he slow gradient the something some area emerges into some of it does not work So if you merging Qantas over Qantas it does for because you don't have this Singlepoint where the gradient although some changes Sadly level we do what we look at the 2nd derivatives the and and look at it really crosses 0 was the derivative the 1st derivative the gradient really positive before 0 and negative after or negative before positive after If so what it went through some ballet all was amount If models It has changed somehow so the 0 crossing is really a test that shows you whether there was an extreme point a minimum all Maxim or not talk and and that this can be used and not the images way do have protected edges because of what see in a noisy amid even if you have time to go into the direction and have Excelsior even if you have a small changes of the intensity and so you have a five year any of the 3 year many of the earlier movie and the other by and what you can't see out its it it's not a big enough like change in what I do it very slow change even if you come to from 100 all something like that here The river is so change you can see that it's diminishing here And That's kind of taking up here So there must be 0 crossing Because people don't here and of the UK a This a basic idea But I saw what happened the idea is basically that if you have a blurred ApJ this you edged high intensity after words low-intensity before then agreed you will assess the seat and the go from here but you so you need 10 pixels to recover The differences in and in the level of function if you look at the the of the gradient The Brady in this kind of slowly of the gradient him gradient slowly rising as a function goes up At some point but the rope this is the interesting but this is where it changes start to full again so it that this is not the high his point in change that he would notice and that kind of like going down to get up and going up before And going down again and some point because of the the of and if you look at the at the last him filing below last year and is positive when the gradient goes up It is negative limit gradient those dump So it has to do the 0 crossing some Where does 0 crossing The maximum of gradient change And but this is where the action And though you have to be full An interval where the ECJ actually happened By looking at the gradient and the Boston 0 crossing you can put your point exactly where they should be UK and a new kind of like the gnawing the noise that is on both sides of But Yes but I'll like if Yes The Exactly Exactly so out 1 thing it is to really make sure that it is Gradient change that actually to a maximum and not just what changes some Holland and stays on the level of could also happened in which it is not an act United it's just a different different shift of shapes and it's just no like you have slightly different colours and the off because there is a shadow of what it means to be human but this rather slow and and even read changes goes to something totally different after which This is a sign that it probably is an act and you perfect I write the edge could be rather rather large and that the 2 2 2 2 0 2 0 a represents the state have to decide which the ex actually is account you can out of it for a really big modules for for the for the edges of the losses will be very difficult to compel between different shapes because of the dispute M'baye but some some white mountains you kebab between the UK that the real shape the real come to of the silhouette of the same could be within the mountain somewhere makes it very so deciding full behind has changed in the Grade II and it is this year its stake I'd like granted that but I'm in the best we can do
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Multimedia
Subtraktion
Bit
Merkmalsraum
Wellenlehre
Multimedia
Wärmeübergang
Transformation <Mathematik>
Textur-Mapping
Raum-Zeit
Computeranimation
Datenhaltung
Übergang
Spezialrechner
Deskriptive Statistik
Textur-Mapping
Zufallszahlen
Schwebung
Mustersprache
Information Retrieval
Kontrast <Statistik>
Datenstruktur
Einflussgröße
Bildauflösung
Shape <Informatik>
Pixel
Datenhaltung
Datenmodell
Übergang
Frequenz
Objekt <Kategorie>
Last
Messprozess
Information
Charakteristisches Polynom
Distributionstheorie
Bit
Wellenlehre
Wärmeübergang
Transformation <Mathematik>
Nichtlinearer Operator
Rechenbuch
Analysis
Computeranimation
Systemprogrammierung
Spezialrechner
Variable
Algorithmus
Schwellwertverfahren
Gleichungssystem
Bildauflösung
Wavelet
Lineares Funktional
Mathematische Morphologie
Vektorgraphik
Bildauflösung
Lineare Gleichung
Vektorraum
Physikalisches System
Frequenz
Linearisierung
Fourier-Transformation
Wavelet
Information
Pixel
Shape <Informatik>
Resultante
Distributionstheorie
Lineare Abbildung
Subtraktion
Mereologie
Wavelet-Transformation
Selbstrepräsentation
Mathematisierung
Computerunterstütztes Verfahren
Information
Term
Analysis
Computeranimation
Überlagerung <Mathematik>
Spezialrechner
Multiplikation
Koeffizient
Spieltheorie
Wavelet
Bildauflösung
Konstruktor <Informatik>
Transformation <Mathematik>
Bildauflösung
Physikalisches System
Bildschirmsymbol
Elektronische Publikation
Ordnungsreduktion
Linearisierung
Last
Mereologie
Selbstrepräsentation
Kantenfärbung
Information
p-Block
Pixel
Bitmap-Graphik
Mittelwert
Subtraktion
Pixel
Punkt
Prozess <Informatik>
Familie <Mathematik>
Bildauflösung
Einfache Genauigkeit
Unrundheit
Transformation <Mathematik>
Analysis
Computeranimation
Spezialrechner
Mittelwert
Rotationsfläche
Kantenfärbung
p-Block
Pixel
Bildauflösung
Mittelwert
Multifunktion
Subtraktion
Pixel
Punkt
Template
Selbstrepräsentation
Ruhmasse
Zahlenbereich
Analysis
Computeranimation
Gradient
Persönliche Identifikationsnummer
Objekt <Kategorie>
Arithmetisches Mittel
Spezialrechner
Funktion <Mathematik>
Menge
Wort <Informatik>
Pixel
Bildauflösung
Bitmap-Graphik
Mittelwert
Subtraktion
Punkt
Klasse <Mathematik>
Gruppenkeim
Versionsverwaltung
Zahlenbereich
Information
Term
Analysis
Computeranimation
Spezialrechner
Mittelwert
Gruppe <Mathematik>
Speicher <Informatik>
Quellencodierung
Bildauflösung
Beobachtungsstudie
Prinzip der gleichmäßigen Beschränktheit
Multifunktion
Pixel
Einfache Genauigkeit
Datenfluss
Mereologie
Information
Pixel
Fitnessfunktion
Mittelwert
Resultante
Subtraktion
Gruppe <Mathematik>
Wellenlehre
Wavelet-Analyse
Kartesische Koordinaten
Digitalfilter
Frequenz
Frequenz
Gesetz <Physik>
Statistische Hypothese
Computeranimation
Spezialrechner
Komponente <Software>
Datenfeld
Mittelwert
Last
Mereologie
Kantenfärbung
Information
Figurierte Zahl
Hilfesystem
Metropolitan area network
Videospiel
Subtraktion
Pixel
Matching <Graphentheorie>
Schaltnetz
Bildauflösung
Zahlenbereich
Information
Term
Analysis
Statistische Hypothese
Computeranimation
Last
Mittelwert
Rechter Winkel
Mereologie
Information
Pixel
Bildauflösung
Subtraktion
Momentenproblem
Zahlenbereich
Textur-Mapping
Information
Ähnlichkeitsgeometrie
Analysis
Division
Computeranimation
Netzwerktopologie
Spezialrechner
Textur-Mapping
Koeffizient
Standardabweichung
Information Retrieval
Stochastische Abhängigkeit
Hilfesystem
Wavelet
Bildauflösung
Autorisierung
Shape <Informatik>
Schiefe Wahrscheinlichkeitsverteilung
Güte der Anpassung
Bildauflösung
Ähnlichkeitsgeometrie
Paarvergleich
Physikalisches System
Visuelles System
Kontrast <Statistik>
Kugelkappe
Rhombus <Mathematik>
Datenfeld
Koeffizient
Nichtunterscheidbarkeit
Information
Kantenfärbung
Shape <Informatik>
Standardabweichung
Tabelle <Informatik>
Aggregatzustand
Information Retrieval
Shape <Informatik>
Rundung
Schaltnetz
Familie <Mathematik>
Unrundheit
Textur-Mapping
Elektronische Publikation
Ellipse
Computeranimation
Spezialrechner
Textur-Mapping
Computerschach
Trapezoid
Mereologie
Information Retrieval
Dreieck
Quadratzahl
Ordnung <Mathematik>
Shape <Informatik>
Fundamentalsatz der Algebra
Lineares Funktional
Subtraktion
Shape <Informatik>
Gewichtete Summe
Gewicht <Mathematik>
Matching <Graphentheorie>
Gruppenoperation
Computer
Textur-Mapping
Computeranimation
Formale Semantik
Mapping <Computergraphik>
Spezialrechner
Deskriptive Statistik
Last
Diskrete Simulation
Mereologie
Nichtunterscheidbarkeit
Information Retrieval
Shape <Informatik>
Fehlermeldung
Tabelle <Informatik>
Shape <Informatik>
Web log
Gruppenkeim
Unrundheit
Textur-Mapping
Term
Computeranimation
Spezialrechner
Fundamentalsatz der Algebra
Einheit <Mathematik>
Wort <Informatik>
Genetischer Algorithmus
Aggregatzustand
Shape <Informatik>
Information Retrieval
Multimedia
Shape <Informatik>
Einfügungsdämpfung
Mereologie
Multimedia
Datenhaltung
Versionsverwaltung
Zahlenbereich
Unrundheit
Computer
Computeranimation
Spezialrechner
Datensatz
Einheit <Mathematik>
Mereologie
Hypermedia
Information Retrieval
Notepad-Computer
Versionsverwaltung
Maschinelles Sehen
Prototyping
Aggregatzustand
Inklusion <Mathematik>
Multimedia
Shape <Informatik>
Prozess <Physik>
Pixel
Flächentheorie
Physikalisches System
Abenteuerspiel
Dialekt
Computeranimation
Übergang
Entscheidungstheorie
Spezialrechner
Bildschirmmaske
Benutzerbeteiligung
Online-Katalog
Ein-Ausgabe
DoS-Attacke
Hilfesystem
Projektive Ebene
Shape <Informatik>
Prototyping
Aggregatzustand
Shape <Informatik>
Punkt
Multimedia
Selbstrepräsentation
Profil <Aerodynamik>
Orakel <Informatik>
Umfang
Computeranimation
Datenhaltung
Spezialrechner
Menge
Trennschärfe <Statistik>
Hypermedia
Basisvektor
Information Retrieval
Shape <Informatik>
Prototyping
Mathematische Morphologie
Subtraktion
Shape <Informatik>
Spline
Umfang
Zeitdilatation
Zahlenbereich
Ähnlichkeitsgeometrie
Nichtlinearer Operator
Textur-Mapping
Quick-Sort
Raum-Zeit
Algorithmische Programmiersprache
Computeranimation
Textur-Mapping
Font
Polygon
Flächeninhalt
Interpolation
Mereologie
Objekt <Kategorie>
Trennungsaxiom
Videospiel
Subtraktion
Shape <Informatik>
Computervision
Gruppenkeim
Bildschirmsymbol
Ähnlichkeitsgeometrie
Dialekt
Raum-Zeit
Computeranimation
Spannweite <Stochastik>
Spezialrechner
Flächeninhalt
Rechter Winkel
Mereologie
Nichtunterscheidbarkeit
Schwellwertverfahren
Normalspannung
Funktion <Mathematik>
Mittelwert
Distributionstheorie
Subtraktion
Extrempunkt
Gruppenoperation
Extrempunkt
Rechenbuch
Computeranimation
Übergang
Histogramm
Spezialrechner
Loop
Spannweite <Stochastik>
Erwartungswert
Algorithmus
Mittelwert
Schwellwertverfahren
Warteschlange
Abstand
Gerade
Verschiebungsoperator
Prinzip der gleichmäßigen Beschränktheit
Algorithmus
Binärcode
Shape <Informatik>
Schwellwertverfahren
Division
Ruhmasse
Physikalisches System
Rechnen
Objekt <Kategorie>
Gefangenendilemma
Histogramm
Rechter Winkel
Erwartungswert
Mereologie
Dreieck
Kantenfärbung
Normalspannung
Algorithmus
Subtraktion
Natürliche Zahl
Mathematisierung
Schlussregel
Unrundheit
Computeranimation
Spezialrechner
Menge
Flächeninhalt
Mittelwert
Datentyp
Mereologie
Flächeninhalt
Schwellwertverfahren
Datenfluss
Ordnung <Mathematik>
Objekt <Kategorie>
Impuls
Subtraktion
Punkt
Extrempunkt
Mathematisierung
Derivation <Algebra>
Gradient
Nichtlinearer Operator
Extrempunkt
Term
Gesetz <Physik>
Computeranimation
Gradient
Richtung
Eins
Spezialrechner
Reelle Zahl
Schwebung
Vorzeichen <Mathematik>
Inverser Limes
Kontrollstruktur
Schwellwertverfahren
Dimension 2
Videospiel
Lineares Funktional
Multifunktion
Shape <Informatik>
Sichtbarkeitsverfahren
Inverser Limes
Objekt <Kategorie>
Rechenschieber
Flächeninhalt
Komplex <Algebra>
Mereologie
Derivation <Algebra>
Helmholtz-Zerlegung
Polygonnetz
Größenordnung
Stetig differenzierbare Funktion
Nachbarschaft <Mathematik>
Subtraktion
Bit
Punkt
Ortsoperator
Extrempunkt
Mathematisierung
Gradient
Transformation <Mathematik>
Rechenbuch
Computeranimation
Data Mining
Gradient
Richtung
Nachbarschaft <Mathematik>
Algorithmus
Reelle Zahl
Gruppe <Mathematik>
Schätzung
Uniforme Struktur
Stichprobenumfang
Schwellwertverfahren
Punkt
Diskrete Untergruppe
Algorithmus
Lineares Funktional
Suite <Programmpaket>
Pixel
Matching <Graphentheorie>
Transformation <Mathematik>
Stellenring
Digitalfilter
Dialekt
Stetige Abbildung
Funktion <Mathematik>
Interpolation
Flächeninhalt
Rechter Winkel
Größenordnung
Pixel
Stetig differenzierbare Funktion
Größenordnung
Einfügungsdämpfung
Subtraktion
Punkt
Extrempunkt
Mathematisierung
Geräusch
Derivation <Algebra>
Gradient
Extrempunkt
Term
Computeranimation
Gradient
Übergang
Richtung
Spezialrechner
Informationsmodellierung
Negative Zahl
Vorzeichen <Mathematik>
Abschattung
Inverser Limes
Punkt
Schreib-Lese-Kopf
Verschiebungsoperator
Ortsoperator
Softwaretest
Lineares Funktional
Shape <Informatik>
Pixel
Snake <Bildverarbeitung>
Digitalfilter
Flächeninhalt
Derivation <Algebra>
Wort <Informatik>
Speicherabzug
Größenordnung
Kantenfärbung
Laplace-Operator
Aggregatzustand
Punkt
Bereichsschätzung
Gruppe <Mathematik>
Mathematisierung
Server
Paarvergleich
Geräusch
Gradient
Algorithmische Programmiersprache
Computeranimation
Gradient
Sichtbarkeitsverfahren
Spezialrechner
Lineares Funktional
Sichtenkonzept
Maßstab
Fächer <Mathematik>
Gruppe <Mathematik>
Datentyp
Gruppenoperation
Zahlenbereich
Digitalfilter
Integraloperator
Computeranimation
Lineares Funktional
Gerichteter Graph
Elektronische Publikation
Einheit <Mathematik>
Zellularer Automat
Transformation <Mathematik>
Information
Extrempunkt
Radius
Computeranimation
Schreib-Lese-Kopf
Lineares Funktional
Schwellwertverfahren
Web Site
Oval
VIC 20
Gleitendes Mittel
Quick-Sort
Computeranimation
Gradient
Rechenschieber
Demoszene <Programmierung>
Projektive Ebene
Touchscreen
Schreib-Lese-Kopf
Schwellwertverfahren
Lochstreifen
Punkt
Spieltheorie
Zustand
Simulation
Computeranimation
Serviceorientierte Architektur
Metropolitan area network
Homepage
Gradient
Differential
Schwellwertverfahren
Gewichtete Summe
Sichtenkonzept
Total <Mathematik>
Hypermedia
Mereologie
Trägheitsmoment
Kombinatorische Gruppentheorie
Computeranimation
Punkt
Extrempunkt
Wasserdampftafel
Flächentheorie
Transformation <Mathematik>
Extrempunkt
Computeranimation
Gradient
Spezialrechner
Maßstab
Flächentheorie
Reverse Engineering
Abstand
Integraloperator
Shape <Informatik>
Oval
Pixel
Transformation <Mathematik>
Digitalfilter
Flächeninhalt
Last
Einheit <Mathematik>
Mereologie
Information
Zeitzone
Subtraktion
Punkt
Wasserdampftafel
Mathematisierung
Ablöseblase
Sprachsynthese
Transformation <Mathematik>
Gradient
Extrempunkt
Computeranimation
Gradient
Übergang
Bildsegmentierung
Spieltheorie
DoS-Attacke
Inverser Limes
Gerade
Einflussgröße
Umwandlungsenthalpie
Trennungsaxiom
Lineares Funktional
Pixel
Matching <Graphentheorie>
Transformation <Mathematik>
Flächeninhalt
Krümmung
Punkt
Randwert
Mathematisierung
Snake <Bildverarbeitung>
Ablöseblase
Abgeschlossene Menge
IRIS-T
Gradient
Transformation <Mathematik>
Extrempunkt
Whiteboard
Computeranimation
Internetworking
Gradient
Spezialrechner
Stetige Abbildung
Iteration
Datentyp
Leistung <Physik>
Kreisfläche
Physikalischer Effekt
Snake <Bildverarbeitung>
Systemaufruf
Algorithmische Programmiersprache
Dialekt
Randwert
Energiedichte
Druckverlauf
Flächeninhalt
Rechter Winkel
Mereologie
Energiedichte
Information
Subtraktion
Einfügungsdämpfung
Bit
Punkt
Mathematisierung
Snake <Bildverarbeitung>
IRIS-T
Kolmogorov-Komplexität
Term
Komplex <Algebra>
Whiteboard
Computeranimation
Internetworking
Gradient
Unterring
Abschattung
Kurvenanpassung
Maßerweiterung
Metropolitan area network
Leistung <Physik>
Schreib-Lese-Kopf
Shape <Informatik>
Kreisfläche
Matching <Graphentheorie>
Fuzzy-Logik
Krümmung
Snake <Bildverarbeitung>
Güte der Anpassung
Systemaufruf
Quick-Sort
Arithmetisches Mittel
Summengleichung
Energiedichte
Druckverlauf
Flächeninhalt
Subtraktion
Element <Mathematik>
Flächentheorie
Geräusch
Nichtlinearer Operator
Computeranimation
Nachbarschaft <Mathematik>
Geräusch
Flächentheorie
Uniforme Struktur
Zeiger <Informatik>
Operations Research
Binärcode
Nichtlinearer Operator
Shape <Informatik>
Befehl <Informatik>
Mathematische Morphologie
Präprozessor
Quick-Sort
Datenstruktur
Flächeninhalt
Mereologie
Ordnung <Mathematik>
Programmierumgebung
Mustererkennung
Pixel
Aggregatzustand
Shape <Informatik>
Objekt <Kategorie>
Prozess <Physik>
Element <Mathematik>
Zeitdilatation
Mathematisierung
Soundverarbeitung
Nichtlinearer Operator
Code
Whiteboard
Computeranimation
Nachbarschaft <Mathematik>
Open Source
Spezialrechner
Spieltheorie
Symmetrie
Flächentheorie
Uniforme Struktur
Kontrollstruktur
Flächeninhalt
Soundverarbeitung
Mathematische Morphologie
Pixel
Element <Gruppentheorie>
Systemaufruf
Objekt <Kategorie>
Flächeninhalt
Rechter Winkel
Pixel
Aggregatzustand
Prozess <Physik>
Element <Mathematik>
Zeitdilatation
Mathematisierung
Klasse <Mathematik>
Ablöseblase
Soundverarbeitung
Automatische Handlungsplanung
Zellularer Automat
Computer
Nichtlinearer Operator
Statistische Hypothese
Code
Computeranimation
Richtung
Nachbarschaft <Mathematik>
Spezialrechner
Open Source
Multiplikation
Spieltheorie
Datenstruktur
Auswahlaxiom
Soundverarbeitung
Mathematische Morphologie
Pixel
Element <Gruppentheorie>
Programmierumgebung
Dichte <Physik>
Rechter Winkel
Pixel
Objekt <Kategorie>
Einfach zusammenhängender Raum
Autorisierung
Nichtlinearer Operator
Mathematische Morphologie
Zeitdilatation
Versionsverwaltung
Nichtlinearer Operator
Kontextbezogenes System
Quick-Sort
Computeranimation
Homepage
Demoszene <Programmierung>
Leck
Negative Zahl
Flächeninhalt
Offene Menge
Prozess <Informatik>
Eliminationsverfahren
Mathematische Morphologie
Sichtenkonzept
Punkt
Reihe
Bildverarbeitung
Nichtlinearer Operator
Computeranimation
Bildschirmmaske
Minimalgrad
Flächeninhalt
Ordnung <Mathematik>
Gleichmäßige Konvergenz
Kontrolltheorie
Metropolitan area network
Konturfindung
Retrievalsprache
Programmiergerät
Decodierung
Momentenproblem
Baum <Mathematik>
Kombinatorische Gruppentheorie
Nichtlinearer Operator
Analysis
Computeranimation
Eins
Deskriptive Statistik
Gruppe <Mathematik>
Momentenproblem
Vererbungshierarchie
Schwellwertverfahren
Optimierung
Kette <Mathematik>
Schwellwertverfahren
Mathematische Morphologie
Nummerung
Visuelles System
Dialekt
Algorithmische Programmiersprache
Invariante
Histogramm
Verkettung <Informatik>
Information
Ordnung <Mathematik>
Shape <Informatik>
Vorlesung/Konferenz

Metadaten

Formale Metadaten

Titel Multiresolution Analysis, Form based Features, Thresholding, Edge Detection, Morphological Operators (28.04.2011)
Serientitel Multimedia Databases
Teil 4
Anzahl der Teile 14
Autor Balke, Wolf-Tilo
Mitwirkende Homoceanu, Silviu
Lizenz CC-Namensnennung - keine kommerzielle Nutzung 3.0 Deutschland:
Sie dürfen das Werk bzw. den Inhalt zu jedem legalen und nicht-kommerziellen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen.
DOI 10.5446/343
Herausgeber Technische Universität Braunschweig, Institut für Informationssysteme
Erscheinungsjahr 2011
Sprache Englisch
Produzent Technische Universität Braunschweig
Institut für Informationssysteme
Balke, Wolf-Tilo
Produktionsjahr 2011
Produktionsort Braunschweig

Inhaltliche Metadaten

Fachgebiet Informatik
Abstract In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features - Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees

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