thnks my friend travesti and jigolo

mirc mırc eski mirc script indir irc komutları mirc indir kameralı mirc sohbet mirc indir mırc indir mirc mırc mirc yükle mirc download islami sohbet dini sohbet islami site islami chat kelebek kelebek script kelebekscript kelebek.gen.tr kelebek.com kameralı mirc indir kameralı mirc kameralı sohbet chat chat yap chat sohbet chatsohbet çet çet sohbet çet odası sohbet kanalları izmir sohbet kanalları sohbet odaları aşk sohbet odaları chat odaları soru cevap sevgili sevgili bul arkadaş arkadaş ara arkadaş bul arkadaşlık bedava sohbet arkadaşlık sitesi arkadaşlık siteleri partner erkek arkadaş bayan arkadaş oto araba mp3 astroloji zoydak nedir cep telefonları gazete marifetname bedava domain ücretsiz domain bayii parça kontör bayiliği bayii online kontör

regarding matlab source codes

Hello, nice site :)

Hi Jorge,
My final project was already completed two years ago.
I finally understand it, although not all of it. But it was working well during the representation.

Thank you very much for your help :) I appreciate it.

Hi, Iwan
You've basically have to understand the Bayes Law:
p(x|z) = [p(z|x).p(x)]/p(z) (see http://en.wikipedia.org/wiki/Bayes'_theorem )

You don't measure p(x|z) (posterior density), cause it's hard to obtain. So, you resort to the above expression, since terms in the left hand side of the expression are easier to measure/estimate. You do have to estimate the p(z|x) (conditional density) and there are several methods for doing so.
To fully grasp the theory involved, read the tutorial:
A Tutorial on Particle Filters for On-line Non-linear/Non-Gaussian Bayesian Tracking (2001) - PDF: http://citeseer.ist.psu.edu/504843.html

Find the entire source code in C here:
http://www.robots.ox.ac.uk/~misard/condensation.html

Regards

65bfc46cb2f3d7e15c5ac0a1cf032473

Hi, everyone. I urgently need help about this algorithm.
I'm taking this Condensation Algorithm as my Final Project in visual tracking. I'm having difficulties in understanding the differences between p(x|z) and p(z|x). When and how should I measure each of these? I need p(z|x) to calculate the weights, am I right?
Thanks

In the discussion of the dynamical model (sect. 5.2), the authors mention that it would be possible to choose sensible defaults for A x and B, but more satisfactory to estimate them from input data. I would like to know how much more satisfactory, and whether from an implementation standpoint, choosing 'sensible' defaults could mean avoiding the need for input data of 'typical motions', which presumably requires operator knowledge - because it seems that many applications would benefit from as little bootstrapping as possible.

The prior distribution of x is the assumed distribution of x without any observed evidence. This is in contrast to the posterior distribution of x, p(x|z) in the notation of the paper, which is the distribution of x after observing evidence z.

I don't have that much mathematical/theoretical background, so the answer may seem very obvious/trivial to some. I keep seeing "prior" p(x), but what exactly is a prior and what does it represent?

It seems that the authors view the Condensation algorithm as an improvement over Kalman filter contour-tracker, but in section 7, there are times when it seems that the Kalman tracker is used first to obtain training data, which is then used to get the shape and motion models used in the Condensation algorithm. Is there some other way to obtain the shape and motion models?

In Figure 13, it took 4 fields to recover that tracking failure. Can we get any idea how long it will take to track the correct object or it just gauranteed to recover for a very short time. And in Figure5, at diffuse step, how to split the elements from t-1 to t?

This method doesn't address initialization of the object model. It's assumed to be known in advance.

I won't go into much detail tomorrow about the specific parameterization that they use, but for their examples they use B-splines. However, rather than allowing arbitrary changes to the splines they restrict the shape-space to a small subspace of possible changes. The purpose of this is to disallow unrealistic changes such as a rigid box turning into a sphere. One way to determine a good subspace is to use PCA.

p(x) is assumed to be known before the algorithm starts, so clutter doesn't really affect it. What clutter affects is accurate feature extraction. With lots of clutter there will be lots of detected features that don't belong to the object being tracked and in this case the tracker might not know which features to follow or not. Having a multi-modal probability distribution allows the tracker to follow both the object and false features caused by clutter. If a Gaussian distribution is used, the tracker will often lose track of the object because the false features will cause the Gaussian to move away from the object.

As for the observation independence, I think what they mean is that the conditional observations p(z_i|x_i) are independent.

Sorry this isn't more clear, hopefully this clarifies things

I've noticed that for all their given examples, a hand-drawn template was provided for initialization. Is this absolutely necesary for the tracking to work or is it just a convenience?

Also, how much variablity is allowed in the curves from frame to frame? I've noticed some warping of the curves in the movie example with the hyperactive kid dancing (http://www.robots.ox.ac.uk/~misard/images/dancemv.mpg), and it seems like scaling is dealt with quite nicely also.

Sunny.

btw more examples abound @ (http://www.robots.ox.ac.uk/~misard/condensation.html)

I'm glad someone is presenting this paper, because I have never been able to understand anything other than the equations in Sections 1-3 of this paper. In general, the descriptions in words of the equations are almost meaningless to me, and I'm glad someone will be presenting these, Here are a couple of the more specific questions I have from these sections:

What is the relationship between assuming a p(x) is Gaussian and clutter? I could see that there would be a relationship between p(x|z) or p(z|x) being Gaussian and clutter, but not the prior p(x).

To me, "observations z_t are assumed to be independent, both mutually and wrt the dynamical process" is not implied by the corresponding equation (equation 2). It should never be the case that z_t and z_{t-1} are independent in a tracking application.

The author mentioned that the tracker must be supplied a set of sample values to initilise, so I guess it cannot handle very well when the camera "lose sight", which can be considered starting a new sequence. BTW, as the task is difficult even to human, it would be really impressive if this algorithm can do.

Also, it is interesting to know how far the algorithm can go using other cues rather than just contour.

You're probably right. The algorithm would search for something that looks like a leaf and since there's a bush full of leaves it would probably find another leaf.

Maybe I should redefine "lose sight of the leaf". If the camera were to turn away for a while (the leaf is not visible) and then return to its original position, I have a feeling that one of the outlier leaves would probably be picked up instead.

The CONDENSATION algorithm handles occlusion fairly well because it combines image features with a stored model of the object being tracked. When measured features in the image become unreliable (as in occlusion), the model compensates. If occlusion persists for a long time, then probably the algorithm would fail however.

The camouflage section is neat. Despite the fact that many of the leaves are potential outliers, the Condensation algorithm performs rather well. However, it does make me wonder what would happen if the camera were to ever lose site of the leaf and then try to reacquire it. I can imagine that this might cause some problems.