I am implementing a keypoint detection algorithm to recognize biomedical landmarks on images. I only have one type of landmark to detect. But in a single image, 1-10 of these landmarks can be present. I'm wondering what's the best way to organize the ground truth to maximize learning.
I considered creating 10 landmark coordinates per image and associate them with flags that are either 0 (not present) or 1 (present). But this doesn't seem ideal. Since the multiple landmarks in a single picture are actually the same type of biomedical element, the neural network shouldn't be trying to learn them as separate entities.
Any suggestions?
One landmark that can appear everywhere sounds like a typical CNN problem. Your CNN filters should learn which features make up the landmark, but they don't care where it appears. That would be the responsibility of the next layers. Hence, for training the CNN layers you can use a monochrome image as the target: 1 is "landmark at this pixel", 0 if not.
The next layers are basically processing the CNN-detected features. To train those, your ground truth should be basically the desired outcome. Do you just need a binary output (count>0)? A somewhat accurate estimate of the count? Coordinates? Orientation? NN's don't care that much what they learn, so just give it in training what it should produce in inference.
I'm working on a model to identify bodies of water in satellite imagery. I've modified this example a bit to use a set of ~600 images I've labeled, and it's working quite well for true positives - it produces an accurate mask for imagery tiles with water in it. However, it produces some false-positives as well, generating masks for tiles that have no water in them whatsoever - tiles containing fields, buildings or parking lots, for instance. I'm not sure how to provide this sort of negative feedback to the model - adding false-positive images to the training set with an empty mask is having no effect, and I tried a training set made up of only false-positives, which just produces random noise, making me think that empty masks have no effect on this particular network.
I also tried training a binary classification network from a couple of examples I found to classify tiles as water/notwater. It doesn't seem to be working with a good enough accuracy to use a first-pass filter, with about 5k images per class. I used OSM label-maker for this, and the image sets aren't perfect - there are some water tiles in the non-water set and vice-versa, but even the training set isn't getting good accuracy (~.85 at best).
Is there a way to provide negative feedback to the binary image segmentation model? Should I use a larger training set? I'm kinda stuck here without an ability to provide negative feedback, and would appreciate any pointers on how to handle this.
Thanks!
I'm attempting to write a short mini-program in Python that plays around with force-based algorithms for graph drawing.
I'm trying to minimize the number of times lines intersect. Wikipedia suggests giving the lines an electrical charge so that they repel each other. I asked my physics teacher how I might simulate this, and she mentioned using calculus with Coulomb's Law, but I'm uncertain how to start.
Could somebody give me a hint on how I could do this? (Or alternatively, another way to tweak a force-based graph drawing algorithm to minimize the number of times the lines cross?) I'm just looking for a hint; no source code please.
In case anybody's interested, my source code and a youtube vid I made about it.
You need to explicitly include a term in your cost function that minimizes the number of edge crossings. For example, for every pair of edges that cross, you incur a fixed penalty or, if the edges are weighted, you incur a penalty that is the product of the two weights.
I'm working on an application that needs to key out the background from an image taken by a webcam in front of a green screen. I figured this would be a very common task, but to my surprise i'm having trouble finding code samples for anything more advanced than a simple color-threshold and those do not quite cut it quality wise.
I've found a few pdf-papers, but I'm having trouble translating these rather high concepts into actual implementations, I'd much rather look at some code.
Focus here is on quality, having a second or more of processing time is not a problem.
I will be using actionscript 3 (and possibly pixel bender) to implement this, but I'll happily look at other languages aswell.
If you have any good samples doing this, the whole process or parts, please do post them!
If you have no high requirement for chromakey quality, maybe this(http://www.quasimondo.com/archives/000615.php) will be okay.
But for high quality video( i.e, 720*576 video ), it is not easy job.
I have spent a lot time to research high quality chromakey algorithm, finally I have figured out one algorithm(implemented with c/c++) for real time video,which can be used in none-linear video editing system as plugin or standalone application.
I put some demo static images to show at voicethread.com websites, everyone can comment with text, or voice there.
http://voicethread.com/share/801789/
Does anyone have any good references for equations which can be implemented relatively easily for how to compute the transfer of angular momentum between two rigid bodies?
I've been searching for this sort of thing for a while, and I haven't found any particularly comprehensible explanations of the problem.
To be precise, the question comes about as this; two rigid bodies are moving on a frictionless (well, nearly) surface; think of it as air hockey. The two rigid bodies come into contact, and then move away. Now, without considering angular momentum, the equations are relatively simple; the problem becomes, what happens with the transfer of angular momentum between the bodies?
As an example, assume the two bodies have no angular momentum whatsoever; they're not rotating. When they interact at an oblique angle (vector of travel does not align with the line of their centers of mass), obviously a certain amount of their momentum gets transferred into angular momentum (i.e. they each get a certain amount of spin), but how much and what are the equations for such?
This can probably be solved by using a many-body rigid system to calculate, but I want to get a much more optimized calculation going, so I can calculate this stuff in real-time. Does anyone have any ideas on the equations, or pointers to open-source implementations of these calculations for inclusion in a project? To be precise, I need this to be a rather well-optimized calculation, because of the number of interactions that need to be simulated within a single "tick" of the simulation.
Edit: Okay, it looks like there's not a lot of precise information about this topic out there. And I find the "Physics for Programmers" type of books to be a bit too... dumbed down to really get; I don't want code implementation of an algorithm; I want to figure out (or at least have sketched out for me) the algorithm. Only in that way can I properly optimize it for my needs. Does anyone have any mathematic references on this sort of topic?
If you're interested in rotating non-spherical bodies then http://www.myphysicslab.com/collision.html shows how to do it. The asymmetry of the bodies means that the normal contact force during the collision can create a torque about their respective CGs, and thus cause the bodies to start spinning.
In the case of a billiard ball or air hockey puck, things are a bit more subtle. Since the body is spherical/circular, the normal force is always right through the CG, so there's no torque. However, the normal force is not the only force. There's also a friction force that is tangential to the contact normal which will create a torque about the CG. The magnitude of the friction force is proportional to the normal force and the coefficient of friction, and opposite the direction of relative motion. Its direction is opposing the relative motion of the objects at their contact point.
Well, my favorite physics book is Halliday and Resnick. I never ever feel like that book is dumbing down anything for me (the dumb is inside the skull, not on the page...).
If you set up a thought problem, you can start to get a feeling for how this would play out.
Imagine that your two rigid air hockey pucks are frictionless on the bottom but have a maximal coefficient of friction around the edges. Clearly, if the two pucks head towards each other with identical kinetic energy, they will collide perfectly elastically and head back in opposite directions.
However, if their centers are offset by 2*radius - epsilon, they'll just barely touch at one point on the perimeter. If they had an incredibly high coefficient of friction around the edge, you can imagine that all of their energy would be transferred into rotation. There would have to be a separation after the impact, of course, or they'd immediately stop their own rotations as they stuck together.
So, if you're just looking for something plausible and interesting looking (ala game physics), I'd say that you could normalize the coefficient of friction to account for the tiny contact area between the two bodies (pick something that looks interesting) and use the sin of the angle between the path of the bodies and the impact point. Straight on, you'd get a bounce, 45 degrees would give you bounce and spin, 90 degrees offset would give you maximal spin and least bounce.
Obviously, none of the above is an accurate simulation. It should be a simple enough framework to cause interesting behaviors to happen, though.
EDIT: Okay, I came up with another interesting example that is perhaps more telling.
Imagine a single disk (as above) moving towards a motionless, rigid, near one-dimensional pin tip that provides the previous high friction but low stickiness. If the disk passes at a distance that it just kisses the edge, you can imagine that a fraction of its linear energy will be converted to rotational energy.
However, one thing you know for certain is that there is a maximum rotational energy after this touch: the disk cannot end up spinning at such a speed that it's outer edge is moving at a speed higher than the original linear speed. So, if the disk was moving at one meter per second, it can't end up in a situation where its outer edge is moving at more than one meter per second.
So, now that we have a long essay, there are a few straightforward concepts that should aid intuition:
The sine of the angle of the impact will affect the resulting rotation.
The linear energy will determine the maximum possible rotational energy.
A single parameter can simulate the relevant coefficients of friction to the point of being interesting to look at in simulation.
You should have a look at Physics for Game Developers - it's hard to go wrong with an O'Reilly book.
Unless you have an excellent reason for reinventing the wheel,
I'd suggest taking a good look at the source code of some open source physics engines, like Open Dynamics Engine or Bullet. Efficient algorithms in this area are an artform, and the best implementations no doubt are found in the wild, in throroughly peer-reviewed projects like these.
Please have a look at this references!
If you want to go really into Mecanics, this is the way to go, and its the correct and mathematically proper way!
Glocker Ch., Set-Valued Force Laws: Dynamics of Non-Smooth Systems. Lecture Notes in Applied Mechanics 1, Springer Verlag, Berlin, Heidelberg 2001, 222 pages. PDF (Contents, 149 kB)
Pfeiffer F., Glocker Ch., Multibody Dynamics with Unilateral Contacts. JohnWiley & Sons, New York 1996, 317 pages. PDF (Contents, 398 kB)
Glocker Ch., Dynamik von Starrkörpersystemen mit Reibung und Stößen. VDI-Fortschrittberichte Mechanik/Bruchmechanik, Reihe 18, Nr. 182, VDI-Verlag, Düsseldorf, 1995, 220 pages. PDF (4094 kB)