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.
Related
I'v been trying to do quadrangle detection and localization for weeks, my goal is to have a robust way of getting the 4 points of an quadrangle(rectangle), so I can apply projective transform to an Image then attach it to the source image. I have try the classic opencv contour method, and also using hough transform to find lines then calculate intersections, those two methods is unusable when apply it to real life images.
So I turn to CNN for help, but currently i haven't find any one try to use CNN to solve this simple problem.
My first attempt is to use state-of-art object detection and localization methods to get quadrangle's bounding box so i can narrow the search of 4 points, then use image processing & computer vision methods to further the search for 4 points. but after trying YOLOv2 and Faster-RCNN, the prediction accuracy is not ideal.
So I'm wondering if there is any idea i can do this end to end, training and feedforward all using a single neural network. it also must be able to deal with occlusion reasonably well.
Currently my idea is to remove the fc-layers and make a huge activation map that has the same width and height as the first input layer(eg. 448x448) then optimize the 4 most highly activated areas, using argmax to get the position. but this method only works for one quadrangle it doesn't work well with corner occlusions as well.
I'll be appreciated if anyone can provide any suggestions. Thanks a lot!
You are absolutely right about the first methods you mentioned. Hough transform like methods are old and not useful for images in the wild. And of course, computer vision field turned its face to object detection and recognition with rise of deep learning.
However, there is a very nice discussion came up recently.
Have we forgotten about Geometry in Computer Vision?
My suggestion would be contour detection and then apply Hough transform(use state of the art) methods to detect rectangles you want, about the occlusion, you can set parameters for Hough transform to be more forgiving for missing edge pixels with parameters.
You can for example check most recent contour detection methods as in recent CVPR paper.
At image i need find "table" - simple rectangle.
Problem is with edge recognition, because potencial photos will be "dark".
I tried edge - sobel, canny, log, .... - recognition and after that Hough transformation and line finding. But this algorithms are not enough for this task.
Something what can help me:
- it is rectangle!, only in perspective view (something like fitting perspective rectangle?)
- that object MUST cover atleast for example 90% of photo (i know i need looking near photo edges)
- that rectangle have fast same color (for example wood dining table)
- i need find atleast "only" 4 corners..(but yes, better will be find the edges of that table)
I know how for example sobel, canny or log algorithms works and Hough as well. And naturally those algorithms fail at dark or non-contrast images. But is there some another method for example based at "fitting"?
Images showing photo i can get (you see it would be dark) and what i need find:
and this is really "nice" picture (without noise). I tested it on more noise pictures and the result was..simply horrible..
Result of this picture with actual algorithm log (with another ones it looks same):
I know image and edge recognition is not simple challenge but are there some new better methods or something like that what i can try to use?
In one of posts in here i found LSD algorithm. It seems very nice descripted and it seems it is recognizing really nice straight lines as well. Do you think it would be better to use it insted of the canny or sobel detection?
Another solution will be corner detection, on my sample images it works better but it recognize too much points and there will problem with time..i will need to connect all the points and "find" the table..
Another solution:
I thought about point to point mapping. That i will have some "virtual" table and try to map that table above with that "virtual" table (simple 2d square in painting :] )..But i think point to point mapping will give me big errors or it will not working.
Does someone have any advice what algorithm use to?
I tried recognize edges in FIJI and then put the edge detected image in matlab, but with hough it works bad as well..:/..
What do you think it would be best to use? In short i need find some algorithm working on non contrast, dark images.
I'd try some modified snakes algorithm:
you parameterize your rectangle with 4 points and initialize them somewhere in the image corners. Then you move the points towards image features using some optimization algorithm (e.g. gradient descent, simulated annealing, etc.).
The image features could be a combination of edge features (e.g. sobel directly or sobel of some gaussian filtered image) to be evaluated on the lines between those four points and corner features to be evaluated at those 4 points.
Additionally you can penalize unlikely rectangles (maybe depending on the angles between the points or on the distance to the image boundary).
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.
Maybe I'm asking this too soon in my research, but I'd better know if this is possible sooner than later.
Imagine I have the following square printed on a paper on top of a table:
The table is brown, so it does not match with any of the colors in the square. Is there a way for me, from a common iPhone camera (non-stereo view), to figure out the distance and angle from which Im looking at the square in the table?
In the end what I'm looking for is being able to draw a 3D square on top of this one using the camera image, but I'm not sure if I am going to be able to figure out the distance and position of the object in space using only a 2D image. Any hints are well appreciated.
Short answer: http://weblog.bocoup.com/javascript-augmented-reality
Big answer:
First posterize, Then vectorize, With the vectors in your power you may need to do some math tricks to define, based on the vectors position, the perspective and then the camera position.
Maybe this help:
www.pixastic.com/lib/docs/actions/posterize/
github.com/selead/cl-vectorizer
vectormagic.com/home
autotrace.sourceforge.net
www.scipy.org/PyLab
raphaeljs.com/
technabob.com/blog/2007/12/29/video-games-get-vectorized/
superuser.com/questions/88415/is-there-an-open-source-alternative-to-vector-magic
Oughta be possible. Scan the image for the red/blue/yellow pattern, then do edge detection to figure out how warped the squares are (they'll be parallelograms in anything but straight-on view). Distance would depend on the camera's zoom setting and scan resolution. But basically you'd count how many pixels are visible in each of the squares, run that past the camera's specs and you should be able to determine a rough distance.
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.