Recently I saw demonstration of image distortion algorithm that preserves some objects in image. For example changing weight/height ratio but preserving people faces in image to look realistically. The problem is i can't find the reference to that demonstration neither I know the name of such transformations.
Can anybody point me to a references to such algorithms?
Perhaps you are referring to liquid rescale?
Seam carving is the current favourite. Liquid Rescale is an implementation of it.
Related
I would like to ask a question I already asked on the OpenCV board but did not get an answer to: http://answers.opencv.org/question/189206/questions-about-the-fundamental-matrix-and-homographies/.
After learning about the fundamental matrix I have the following question that I could not answer by googling. The fundamental matrix is a more general case of the homography as it is independent of scene's structure. So I was wondering if it could be used for image stitching instead of a homography. But all papers I found only use homographies. So I reread the material about the properties of the fundamental matrix and now I am wondering:
Is it not possible to use the fundamental matrix for stitching because of its rank deficiency and the fact that it does only relate points in Image 1 to lines (epipolar lines) in Image 2?
Another question I have regarding homographies: All papers I read about image stitching use homographies for rotational panoramas. What if I want to create a panorama based only on translation between images? Can I use the homography as well? The answers provided by a google search vary quite a lot.
Kind regards and thanks for your help!
Conundraah
About using fundamental matrix for stitching.
It actually depends on how you want to stitch the image together.
The problem is even if you get the fundamental matrix, when you stitch images together, you will only need homography matrix to do the transformation of images. So what is the point of using fundamental matrix. Unless you figure out how to handle the different distance on the same image.
In the case of panorama images, the assumption is that the scene structure is far enough to be seen as planar, so comparatively the translation could be ignored. If that is not the case, translation could be considered.
I am currently reading over Yasutaka Furukawa et al.'s Paper "Accurate, Dense, and Robust Multi-View Stereopsis" (PDF available here), where they describe an MVS-algorithm for reconstructing a 3D point-cloud from images.
I do understand the concepts and the main steps, but there is one detail that I am struggling with. This may be because I am not an English native speaker, so maybe a small hint would be enough.
On page 4 of the linked source, in chapter 3.2 "Expansion", there is the definition of "n-adjacent" patches:
|(c(p)−c(p'))·n(p)|+|(c(p)−c(p'))·n(p')| < 2ρ_2
My question is about ρ_2, that is described as in the following:
[...] ρ_2 is determined automatically as the distance at the depth of the
midpoint of c(p) and c(p') corresponding to an image displacement of β1 pixels
in R(p).
I do not understand what "distance" in this context should be, and I do not understand the stated correspondence to the image displacement.
I know that this is a very specific question, but since this paper is somewhat popular I hoped, that there is somebody, that can help me.
Alright, I think I do get it now.
It just means, that ρ_2 is the distance you have to move in a plane, located as far away from the camera (depth) as the midpoint of c(p) and c(p'), so that you get a displacement of β1 pixels in the image showing the scene.
I want to make an effective illumination compensation on iris images and I want this compensation to be based on color i.e. illumination compensation using color rather than texture. I corrected my images for various mechanical errors but I want a simple algorithm to compensate the illumination based on color. Any ideas?
Try subtracting a low-pass copy of the same image?
What you are interested in is white balancing (i.e. achieving color constancy). One of the simplest algorithms is the Gray-World algorithm and I would try that one first because it's very easy to implement (even though it's not very precise).
You also might want to try some Retinex based algorithms. If so, visit this site: http://www.fer.unizg.hr/ipg/resources/color_constancy/
It contains C++ implementations of several Retinex-based color constancy algorithms.
I have a set of Points in 3D space.
The image below is an example:
I would like to turn these points into a surface. I just know the X,Y and Z values of the points.
For example, check out the image below, which shows a mesh of a human face generated from points in 3D space.
i googled so much but, what i found is some images and explaination
but no one has explained with practical aspect and practical example.
is there any good or best algorithms which help me to solve this problem.
Please....
Thaks...........
You want to do a Delaunay-Triangulation. See example application here: http://www.geometrylab.de/VoroGlide/.
Please suggest any template matching algorithms, which are independent of size and rotation.
(any source codes as examples if possible please)
EDIT １:
Actually I understand how the algorithm works, we can resize template and rotate it. It is computationally expensive, but we can use image pyramids. But the real problem for me now is when the picture is made at some angle to object, so that only a perspective transform can correct the image. I mean that even if we rotate image or scale it, we will not get a good match if the object in image is perspectively transformed. Of course it is possible to try to generate many templates at different perspective, but I think it is very bad idea.
EDIT ２：
One more problem when using template matching based on shape matching.
What if image doesn't have many sharp edges? For example a plate or dish?
EDIT ３：
I've also heard about camera callibration for object detection. What is the algorithm used for that purpose? I don't understand how it can be used for template matching.
I don't think there is an efficient template matching algorithm that is affine-invariant (rotation+scale+translation).
You can make template matching somewhat robust to scale+rotation by using a distance transform (see Chamfering style methods). You should probably also look at SIFT and MSER to get a sense of how the research area has been shaped the past decade. But these are not template matching algorithms.
Check out this recent 2013 paper on efficient affine template matching: "Fast-Match". http://www.eng.tau.ac.il/~simonk/FastMatch/
Matlab code is available on that website. Basic idea is to exhaustively search the affine space, but do it in the sparsest way possible based on how smooth the image is. Has a formal approximation guarantee, although it won't always find the absolute best answer.