I want to detect shape and then describe it (somehow) to compare it with server data.
So the first question is, is it possible to detect shape like blob with ARKit?
To be more specific, let's describe my usecase generally.
I want to scan image by phone, get the specific shape, send it on server, compare two images on server (server image is the real one, scanned image would be very similar) and then send back some data. I am not asking about server side, the only question about server side is what should I compare - images using OpenCV, some mathematical description of both images and try to find similarity, etc.).
If the question is hard to understand, let's split it on two easy questions:
1) How to scan 2D object by iPhone and save it (trim the specific shape from its background when object is black and background white).
2) Describe scanned object for comparision with almost the same object.
ARKit has no use here.
You will probably need a lot of CoreImage (for fixing perspective distortion and binarization) and OpenCV logic.
Perhaps Vision can help you a little bit with getting ROI from the entire frame, especially if the waveform image is located in some kind of rectangle.
Perhaps you can train a custom ML model that will recognize specific waveforms or waveforms in general to use with Vision.
In any case, it is not a trivial task.
Related
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.
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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 am trying to detect exact silhouette of human body in this dataset using background subtraction. After doing some thresholding I was getting split blobs so I looked at this tutorial by Steve but now I am getting blob other that human body as shown below
So here is the original
After Subtracting it from background, background was considered as the first frame of the video, so after subtracting it from orignal image I get the following image
so I did basic thresholding and I get the following image, which is split from further areas
and using Steve's method I get this
But this contains a lot of area which is not a part of human body, any suggestion if somehow or using edges I can get good blob of human body.
EDIT
As #lennon310 asked me to upload color image so here it is
and as #NKN asked me to upload edge information of the same image so here it is
Instead of literally subtracting the background, try using the vision.ForegroundDetector object, which is part of the Computer Vision System Toolbox. It implements the mixture-of-gaussians adaptive background modeling, and it may give you a cleaner segmentation.
Having said that, it is very unlikely that you will get the "exact" silhouette. Some error is inevitable.
In your result image, you have tow types of black regions. one is moving and the other is stationary.
So when you you want to fill the human body, you have to choose only the moving region, for this purpose, I suggest to segment your image by adding optical flow technique to know where the moving regions are.
This is an interesting tutorial doing what you need to do:
http://docs.opencv.org/trunk/doc/py_tutorials/py_video/py_lucas_kanade/py_lucas_kanade.html
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 working on car parking system project. For that, I would like to detect the presence of a car.
Can anybody tell me how I can accomplish this using MATLAB?
Also, what is the algorithm for detecting a car?
There's a whole world of methods for object detection in images. You need to learn a little bit about image processing to solve this problem. I suggest you read about template matching or more generally about Object recognition. Specifically for car detection, if you know they will be seen at a certain angle (head on, for example) i'd try Viola-Jones detection which is implemented in OpenCV as haar-based feature cascade detection. Although OpenCV is not a matlab library, you can probably find something in matlab's image processing toolboxes that does a similar job (or interface into OpenCV)
Background subtraction would be a simple place to start.
In a nutshell:
Can capture an image of your empty parking lot. This is your reference image.
Compare the current image of your parking lot with the reference image. The parts that are different will be of interest.
Problems:
You need to keep updating your reference image to stay current with the conditions (e.g. day, night, cloudy, raining). Sometimes this may not be possible, because your reference image needs to have no cars in it for the approach to work.
Moving things in the background (like trees shaking in the wind) will come up as false positives
Have you considered using 3D/stereoscopic imaging in addition to using 'normal' images? If yes you could open up a whole new world of methods and intelligent tricks to remove objects based upon their distance to the camera. Then, any object that is a certain, fixed distance from the camera (e.g. your background) is easily removable and you can just process the new parts of the image (e.g. cars).
If this interests you I can supply you with an algorithm I have developed to detect animals in a livestock pen, which is a similar concept.