I am asking for a step-by-step process with the appropriate plugins (I have been attempting with multipoint and landmark correspondence). Please include images in answer if possible.
I want to overlay two scientific images
The images are not oriented the same due to distortion of the second image from collection at a 45o angle and the object was also at a different orientation (flipped horizontally and slightly rotated)
In Adobe Photoshop I transformed the distorted image to overlay with the
first image by eyeballing the match as you can see below but I am having
difficulty using ImageJ to perform this overlay. I have been told that my
eyeballing method in Adobe Photoshop will not be sufficient for my methods
section of my manuscript and that I must use a scientific program such as
ImageJ.
I tried to follow instructions from the ImageJ forum for Multipoint and Landmark Correspondence but it does not overlay the two images or transform the second image to match the first. Rather, it distorts a portion of the second image and appears to crop the rest out.
Related
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
I want to merge an image to another image in one shape. Example:
1- People image
2- Shape Image:
So how to do draw that. I already implement for merging but it's not fill to that shape.
It's possible to do this using the masking functions in the Quartz 2D framework. It's a little bit more involved than using the higher level image functions of UI Kit, but Quartz 2D gives you a lot more power to do cool graphics techniques.
The relevant Apple Developer guide to this can be found here: https://developer.apple.com/library/mac/documentation/GraphicsImaging/Conceptual/drawingwithquartz2d/dq_images/dq_images.html
For this example, you'd want to create a mask shape for the inside part of the shape image. There are two ways you can do this. One way is to use image editing software to create a second mask image, with the same size as your shape image, with pure black in the area where you want the people image to appear, and white where you don't want to appear. In this example, that would be the area inside the blue shape. It is important to not crop this image, or else they won't match up exactly.
The other way to create the masking image would be to do that dynamically based on the shape image, and honestly, this is the way I would do it. This would mean that you're including fewer images in your app, and if you made any changes to the shape image, you wouldn't have to recreate the mask image as well. You could do this by making a small change to the way your shape image is formatted. You would need to use a format that allows transparency - png is preferred - so that there is alpha transparency in the part of the image outside of the shape, which is white in your JPEG image. Make sure the section in the center of the image is white (really, any color that is NOT USED in the wanted part of the shape image would work, but I'll say white for this example) and that you don't have parts of it that aren't pure white after image compression.
You will then use Quartz to select the area that's white, and create a mask from that. This technique is a bit more involved, but what you need can be found in the document I linked to above. Because of this, you might start with a static masking image, and then convert to the more involved technique after you've got the code to make the first technique work.
When you have your masking image, you would create the mask itself with the function CGImageMaskCreate(::::::::). You can then apply the mask to the people image using the function CGImageCreateWithMask(::), which will give you an image with the person's portrait, with the correct shape cropped from the center.
Finally, you would display this in your app by placing the masked people image on top of the shape image, and voila, you'll have what you're looking for.
Also, keep in mind, when using the Quartz 2D framework, you'll have to make sure you release images when they are no longer needed, or else you could have memory leaks.
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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 an image that contains an upward-facing arrow in its left-hand side. I would like to be able to detect that particular shape in that orientation, and if it's present, capture just that portion of the image. If the arrow faces downward, I want it to be ignored.
I've looked into using SURF descriptors and OpenCV to detect and match a shape like this, but there are licensing issues around the SURF algorithms. What alternative method(s) could I use to detect this shape in this particular orientation within an image on iOS?
A simple template matching can solve this. Build a template (or crop from an existing image) an 'upwards arrow.' Then use Normalized Cross Correlation to see where all the 'upwards arrows' are present in your test image. Since you are only looking for 'upwards arrows' on left-side of image, limit your Normalized Cross Correlation search in that region. Here is a good tutorial: http://www.mathworks.com/products/demos/image/cross_correlation/imreg.html