I'd like to fill the background of my app with animated clouds. I did some research and stumbled upon the perlin noise algorithm which seems to be fitting. However even in the first test it was extremely expensive to generate a 512x512 (2D) cloud map. I tried simplex noise but it didn't fix it.
According to http://freespace.virgin.net/hugo.elias/models/m_clouds.htm generating clouds is done by adding some perlin/simplex noise maps together. Impossible to do it on a iPhone in my app: I need fluid graphics (my optimistic expectation is 60 FPS on an A4).
So my question: Is there a lighter algorithm to generate animated clouds that does not make my frame rate drop too much?
Thanks in advance!
Paul
Unless all you're doing is generating clouds you'll definitely want them precomputed. Perlin noise can make for nice 2d animations by traversing a set of 3d data, but you could just scroll a 2d image of some noise or a fractal like is generated by the diamond-square algorithm. Either way, you should probably precompute it.
If you want some more variation, I would experiment with putting a noise filter over the precomputed clouds.
Pre-generate the clouds and create 2d sprites using core animation or otherwise. You can then animate these around cheaply. You may not get 60 fps, but you should get close depending on how complex movement you want or what other animations are going on at the time. Either way, it's going to be faster than generating clouds yourself.
Related
I'm currently in the process of coding a procedural terrain generator for a game. For that purpose, I divide my world into chunks of equal size and generate them one by one as the player strolls along. So far, nothing special.
Now, I specifically don't want the world to be persistent, i.e. if a chunk gets unloaded (maybe because the player moved too far away) and later loaded again, it should not be the same as before.
From my understanding, implicit approaches like treating 3D Simplex Noise as a density function input for Marching Cubes don't suit my problem. That is because I would need to reseed the generator to obtain different return values for the same point in space, leading to discontinuities along chunk borders.
I also looked into Midpoint Displacement / Diamond-Square. By seeding each chunk's heightmap with values from the borders of adjacent chunks and randomizing the chunk corners that don't have any other chunks nearby, I was able to generate a tileable terrain that exhibits the desired behavior. Still, the results look rather dull. Specifically, since this method relies on heightmaps, it lacks overhangs and the like. Moreover, even with the corner randomization, terrain features tend to be confined to small areas, i.e. there are no multiple-chunk hills or similar landmarks.
Now I was wondering if there are other approaches to this that I haven't heard of/thought about yet. Any help is highly appreciated! :)
Cheers!
Post process!
After you do the heightmaps, run back through adding features.
This is how Minecraft does it to get the various caverns and cliff overhangs.
I have several images of the pugmark with lots of irrevelant background region. I cannot do intensity based algorithms to seperate background from the foreground.
I have tried several methods. one of them is detecting object in Homogeneous Intensity image
but this is not working with rough texture images like
http://img803.imageshack.us/img803/4654/p1030076b.jpg
http://imageshack.us/a/img802/5982/cub1.jpg
http://imageshack.us/a/img42/6530/cub2.jpg
Their could be three possible methods :
1) if i can reduce the roughness factor of the image and obtain the more smoother texture i.e more flat surface.
2) if i could detect the pugmark like shape in these images by defining rough pugmark shape in the database and then removing the background to obtain image like http://i.imgur.com/W0MFYmQ.png
3) if i could detect the regions with depth and separating them from the background based on difference in their depths.
please tell if any of these methods would work and if yes then how to implement them.
I have a hunch that this problem could benefit from using polynomial texture maps.
See here: http://www.hpl.hp.com/research/ptm/
You might want to consider top-down information in the process. See, for example, this work.
Looks like you're close enough from the pugmark, so I think that you should be able to detect pugmarks using Viola Jones algorithm. Maybe a PCA-like algorithm such as Eigenface would work too, even if you're not trying to recognize a particular pugmark it still can be used to tell whether or not there is a pugmark in the image.
Have you tried edge detection on your image ? I guess it should be possible to finetune Canny edge detector thresholds in order to get rid of the noise (if it's not good enough, low pass filter your image first), then do shape recognition on what remains (you would then be in the field of geometric feature learning and structural matching) Viola Jones and possibly PCA-like algorithm would be my first try though.
I'm developing an image warping iOS app with OpenGL ES 2.0.
I have a good grasp on the setup, the pipeline, etc., and am now moving along to the math.
Since my experience with image warping is nil, I'm reaching out for some algorithm suggestions.
Currently, I'm setting the initial vertices at points in a grid type fashion, which equally divide the image into squares. Then, I place an additional vertex in the middle of each of those squares. When I draw the indices, each square contains four triangles in the shape of an X. See the image below:
After playing with photoshop a little, I noticed adobe uses a slightly more complicated algorithm for their puppet warp, but a much more simplified algorithm for their standard warp. What do you think is best for me to apply here / personal preference?
Secondly, when I move a vertex, I'd like to apply a weighted transformation to all the other vertices to smooth out the edges (instead of what I have below, where only the selected vertex is transformed). What sort of algorithm should I apply here?
As each vertex is processed independently by the vertex shader, it is not easy to have vertexes influence each other's positions. However, because there are not that many vertexes it should be fine to do the work on the CPU and dynamically update your vertex attributes per frame.
Since what you are looking for is for your surface to act like a rubber sheet as parts of it are pulled, how about going ahead and implementing a dynamic simulation of a rubber sheet? There are plenty of good articles on cloth simulation in full 3D such as Jeff Lander's. Your application could be a simplification of these techniques. I have previously implemented a simulation like this in 3D. I required a force attracting my generated vertexes to their original grid locations. You could have a similar force attracting vertexes to the pixels at which they are generated before the simulation is begun. This would make them spring back to their default state when left alone and would progressively reduce the influence of your dragging at more distant vertexes.
I have a computer vision set up with two cameras. One of this cameras is a time of flight camera. It gives me the depth of the scene at every pixel. The other camera is standard camera giving me a colour image of the scene.
We would like to use the depth information to remove some areas from the colour image. We plan on object, person and hand tracking in the colour image and want to remove far away background pixel with the help of the time of flight camera. It is not sure yet if the cameras can be aligned in a parallel set up.
We could use OpenCv or Matlab for the calculations.
I read a lot about rectification, Epipolargeometry etc but I still have problems to see the steps I have to take to calculate the correspondence for every pixel.
What approach would you use, which functions can be used. In which steps would you divide the problem? Is there a tutorial or sample code available somewhere?
Update We plan on doing an automatic calibration using known markers placed in the scene
If you want robust correspondences, you should consider SIFT. There are several implementations in MATLAB - I use the Vedaldi-Fulkerson VL Feat library.
If you really need fast performance (and I think you don't), you should think about using OpenCV's SURF detector.
If you have any other questions, do ask. This other answer of mine might be useful.
PS: By correspondences, I'm assuming you want to find the coordinates of a projection of the same 3D point on both your images - i.e. the coordinates (i,j) of a pixel u_A in Image A and u_B in Image B which is a projection of the same point in 3D.
I have two images. In one of the images, my eye is in the center position and in the other image, it is in the left. How do I find out whether my eye is in the left or the right?
I am using MATLAB. Are there any functions for this?
A simple solution is to try to detect the iris using circular Hough Transform.
You can find a lot materials out there. To name a few, these two fileexchange submissions:
Hough Transform for circle
detection
Circle Detection via Standard Hough
Transform
This sounds like Eye tracking implemented in MATLAB which is a fairly popular research topic.
If you want a more detailed answer, please answer the following questions:
Do you know the coordinates of your eye in the first image?
What kind of motion is there between the two images? Rotation/translation/scaling/...?
Do you want this to be real-time?
What is the resolution of the images?
Are there going to be more eyes in the image apart from yours?
If you are willing to select the eye in one image you can use template matching to find it in others (for example you can mark it in the first frame of a video and then find it in all other frames).
Look at the normxcor2 function in matlab:
http://www.nd.edu/~hpcc/solaris8_usr_local/src/matlab6.1/help/toolbox/images/normxcorr2.html
This technique is robust to constant illumination change, but will fail if the appearance of the eye changes significantly between the image you took the template from and the image you are searching in.
If you are going to search for the eye in a lot of frames (for example, eye tracking from a webcam) then you should look at stronger techniques such as the Kalman Filter or the Particle Filter (aka Condensation Filter in computer vision)
By using Color Distance Maps, the skin and non skin area can be differentiated and thus the non skin area contains the iris. From the iris, the whole eye could be detected. Hope it works.
You should also have a look at Eye Ball Detection in MATLAB , they have detected eyes first and then detected the EyeBall.