If I have understand well, 3D 360 photos are created from a panorama photo, so I guess it should be possible to create a 3D photo (non 360) from a normal photo. But how? I did not find anything in Google! Any idea of what should I search??
So far, if nothing available (I don't think so), I'll try to duplicate the same photo in each eye. One of the pictures a little bit moved to the right, and the other one moved a little bit to the left. But I think the distortion algorithm is much more complicated.
Note: I'm also receiving answers here: https://plus.google.com/u/0/115463690952639951338/posts/4KdqFcqUTT9
I am in no way certain of this, but my intuition on how 3D 360 images are created in GoogleVR is this:
As you take a panorama image, it actually takes a series of images. As you turn the phone around, the perspective changes slightly with each image, not only by angle, but also offset (except in the unlikely event you spin the phone around its own axis). When it stitches together the final image, it creates one image for each eye, picking suitable images from the series so that it creates a 3D effect when viewed together. The same "area" of the image for each eye comes from a different source image.
You can't do anything similar with a single image. It's the multitude of images produced, each with a different perspective coming from the turning of the phone, that enables the algorithm to create a 3D image.
2D lacks a dimension hence cannot be converted to 3D just like that, but there are clever ways for example Google Pixel even though doesn't have 2 camera can make it seem like the image is 3D by applying some Machine learning algorithm that create the effect of perspective and depth by selective blurring.
3d photos can't be taken by normal but you can take 360 photos with normal camera ..... There are many apps via which you can do this ..... Also there are many algorithms to do it programmatically
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I want to implement an application, that is able to recognize pictures from camera input. I don't mean classification of objects, but rather detecting the exact single image from given set of images. So if I for example have an album with 500 pictures, then if I point a camera to one of them, then application will be able to tell it's filename. Most of tutorials I find about CoreML is strictly for image classification (recognizing class of object) and not about recognizing exact image name in camera. This needs to work from different angles as well, and all I can have for training the network is this album with many different pictures (single picture for single object). Can this be somehow achieved? I can't use ARKit Image Tracking, because there will be about 500 of these images, and I need to find at least a list of similar ones first with CoreML / Vision.
I am not sure, but I guess perceptual hashing might be able to help you.
It works in a way that it makes some fingerprint from the reference images, and for a given image, it extracts the fingerprints as well, and then you can find the most similar fingerprints.
in this way, even if the new image is not 100% as the image in the dataset, you still can detect it.
It is actually not very hard to implement. but if you would like, i think phash library is a good one to use.
I am trying to do a number of things via MATLAB but I am getting a bit lost with what techniques to use. My ultimate goal is to extract various measurements from a users fingerprint presentation, e.g. how far the finger over/undershoots, the co-ordinates of where the finger enters, the angle of the finger.
In my current setup, I have a web camera recording footage of a top down view of the presentation which I then take the video file and break down into individual frames. https://www.dropbox.com/s/zhvo1vs2615wr29/004.bmp?dl=0
What I am trying to work on at the moment is using ROI based image processing to create a binary mask around the edges of the scanner. I'm using the imbw function to get a binarised image and getting this as a result. https://www.dropbox.com/s/1re7a3hl90pggyl/mASK.bmp?dl=0
What I could use is some guidance on where to go from here. I want to be able to take measurements from the defined ROI to work out various metrics e.g. how far a certain point is from the ROI so I must have some sort of border for the scanner edges. From my experience in image processing so far, this has been hard to clearly define. I would like to get a clearer image where the finger is outlined and defined and the background (i.e. the scanner light/blocks) are removed.
Any help would be appreciated.
Thanks
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I'm working on ways of collecting farm aerial images (images collected from a helicopter in a perpendicular fashion) that I'd want to stitch them together to build the whole photo of the area that's being covered and then I wanted to run analytics.
I'm assuming the images will come with [latitudue, longitude] coordinates, to help me determine the spots to place the images.
To understand the issues with this technology, I tried manually stitching pictures taken from my phone of some sample area in my back yard. I experienced that the edges don't usually look the same because they are being seen by the camera from different sides or angles. I guess this is a distortion in image that could potentially be fixed by ortho-rectification (not completely sure).
I quickly created the following picture to help explain my problem.
My question to you:
What are the algorithms/techniques used to do ortho-rectification?
What tools would best suit my needs: opencv, or processing or matlab or any other tool that could easily help in rectification of images and creating a mosaic photo?
What other issues should be considered in doing aerial imagery mosaic-ing and analytics?
Thank you!
Image stitching usually assumes that the camera center is fixed across all photos, and uses homographies to transform the images so that they seem continuous. When the fixed camera center assumption is not strictly valid, artifacts/distortions may appear due to the 3D of the scene. If the camera center moved by a small distance compared to the relief of the scene, "seamless image blending" techniques may be sufficient to blur out the distortions.
In more extreme cases, ortho-rectification is required. Ortho-rectification (Wikipedia entry) is the task of transforming an image observed from a given perspective camera into an orthographic (Wikipedia entry) and usually vertical point of view. The orthographic property is interesting because it makes the stitching of several images much easier. The following picture from Wikipedia is particularly clear (left is an orthographic or directional projection, right is a perspective or central projection):
The task of ortho-rectification usually requires having a 3D model of the scene, in order to map appropriately intensities observed by the perspective camera to their location with respect to the orthographic camera. In the context of aerial/satellite images, Digital Elevation Models (DEM) are often used for that purpose, but generally have the serious drawback of not including man-made structures (only Earth relief). The NASA provides freely the DEM acquired by the SRTM missions (DEM link).
Another approach, if you have two images acquired at different positions, you could try to do a 3D reconstruction using one of the stereo matching technique, and then to generate the ortho-rectified image by mapping the two images as seen by a third orthographic and vertical camera.
OpenCV has several interesting function for that purpose (e.g. stereo reconstruction, image mapping functions, etc) and might be more appropriate for intensive usage. Matlab probably has interesting functions as well, and might be more appropriate for quick tests.
First, rectification is some kind of warping, but not the one that you need. Regular rectification is used in stereo to ensure that matching points lie on the same row - not your case. Ortho-rectification warps perspective projection into orthographic - again not your case. Not only you lack a 3D model for this warping to calculate but also you dont need it since your perspective distortions are negligible and you already have image pretty close to ortho ( that is when the size of the objects is small compared to the viewing distance perspective effects are small).
You problems in aligning two images stem from small camera rotations between shots. To start fixing the problem you need to ensure that your images actually overlap by say 30%. To read about this see chapter 9 of this book.
What you need is to review a regular image stitching techniques that use homography to map two images. Note that doing so assumes that images are essentially flat. To find homography you can first manually select 4 points in one image and 4 matching points in another image and run openCV function findHomography(). Note that overlap is required to find the matches (in your picture there is no overlap). warpPerspective() can warp images for you after homogrphy is found.
"What are the algorithms/techniques used to do ortho-rectification?
If you want a good overview of the techniques, then the book "Multiple View Geometry in Computer Vision" by Hartley & Zisserman might be a good place to start: http://www.robots.ox.ac.uk:5000/~vgg/hzbook/
Andrew Zisserman also has some tutorials available here at www.robots.ox.ac.uk/~az/tutorials/ which might be more accessible/make it easier for you to find the particular technique you want to use.
"What tools would best suit my needs: opencv, or processing or matlab or any other tool that could easily help in rectification of images and creating a mosaic photo?"
OpenCV has a fair few number of tools available - take a look at Images stitching for starters. There's also a lot available for correcting distortion. However, it doesn't have to be the tool you use, there are others!
I have a database of images of one person who is using his hands to show various words and phrases in sign language. The background is white and the only thing changing is the shape of the person's hands and their locations. Now in my gui in matlab, I want the user to be able to choose another image from the same person that was taken at another time doing a sign but wearing the same clothes and then the program will have to compare this against the images in the database and show the most similar. Obviously I can't do pixel by pixel comparison as the images were taken by a hand held mobile camera and slight movement has been inevitable so I should try and locate the hands in the images and compare their shapes. I have no idea how to go about this? I have to say I am new to image processing toolbox in matlab.
Your help is much appreciated
I am doing a phD in computer vision, and I can tell you that it is an unsolved problem. (even in your simple framewrok, with white background)
If you are interested, you might read some works about it ar MIT:
http://people.csail.mit.edu/rywang/handtracking/
or at Oxford:
http://www.robots.ox.ac.uk/~vgg/research/sign_language/index.html
http://www.robots.ox.ac.uk/~vgg/research/hands/index.html
I disagree with you. Such a project can achieve results quickly.
This becomes a problem as soon as the project has to deal with "real life".
Using a single camera, and a completely known background; Opencv provides a simple way to extract hand shape in a image (in about 20 lines of code). You will find plenty of source on the web (have a look at calcbackproj).
After that, what you will have to do is to play with shape, and search for characteristic points.
Begin with some simple signs (example : a circle and a V). How would you recognize one from the other?
There are thousands of papers on sign language; just read the older one to simple ideas flowing :)
I am building an application on night vision but i don't find any useful algorithm which I can apply on the dark images to make it clear. Anyone please suggest me some good algorithm.
Thanks in advance
With the size of the iphone lens and sensor, you are going to have a lot of noise no matter what you do. I would practice manipulating the image in Photoshop first, and you'll probably find that it is useful to select a white point out of a sample of the brighter pixels in the image and to use a curve. You'll probably also need to run an anti-noise filter and smoother. Edge detection or condensation may allow you to bold some areas of the image. As for specific algorithms to perform each of these filters there are a lot of Computer Science books and lists on the subject. Here is one list:
http://www.efg2.com/Lab/Library/ImageProcessing/Algorithms.htm
Many OpenGL implementations can be found if you find a standard name for an algorithm you need.
Real (useful) night vision typically uses an infrared light and an infrared-tuned camera. I think you're out of luck.
Of course using the iPhone 4's camera light could be considered "night vision" ...
Your real problem is the camera and not the algorithm.
You can apply algorithm to clarify images, but it won't make from dark to real like by magic ^^
But if you want to try some algorithms you should take a look at OpenCV (http://opencv.willowgarage.com/wiki/) there is some port like here http://ildan.blogspot.com/2008/07/creating-universal-static-opencv.html
I suppose there are two ways to refine the dark image. first is active which use infrared and other is passive which manipulates the pixel of the image....
The images will be noisy, but you can always try scaling up the pixel values (all of the components in RGB or just the luminance of HSV, either linear or applying some sort of curve, either globally or local to just the darker areas) and saturating them, and/or using a contrast edge enhancement filter algorithm.
If the camera and subject matter are sufficiently motionless (tripod, etc.) you could try summing each pixel over several image captures. Or you could do what some HDR apps do, and try aligning images before pixel processing across time.
I haven't seen any documentation on whether the iPhone's camera sensor has a wider wavelength gamut than the human eye.
I suggest conducting a simple test before trying to actually implement this:
Save a photo made in a dark room.
Open in GIMP (or a similar application).
Apply "Stretch HSV" algorithm (or equivalent).
Check if the resulting image quality is good enough.
This should give you an idea as to whether your camera is good enough to try it.