my problem is how to match one image to a set of images and to display the matched images. I am using SURF feature for feature extraction.
If you have the Computer Vision System Toolbox, take a look at the following examples:
Object Detection In A Cluttered Scene Using Point Feature Matching
Image Search using Point Features
Related
I am stuck at a current project:
I have an input picture showing the ground with some shapes on it. I have to find a specific shape with a given template.
I have to use distance transformation into skeletonization. My question now is: How can I compare two skeletons? As far as I noticed and have been told, the most methods from the Image Processing Toolbox to match templates don't work, since they are not scale-invariant and rotation invariant.
Also some skeletons are really showing the shapes, others are just one or two short lines, with which I couldn't identify the shapes, if I didn't know what they should be.
I've used edge detection, and region growing on the input so there are only interessting shapes left.
On the template I used distance transformation and skeletonization.
Really looking forward to some tips.
Greetings :)
You could look into convolutions?
Basically move your template over your image and see if there is a match, and where.
The max value of your array [x,y] is the location of your object in the image.
Matlab has a built-in 2D convolution function for this
I'm trying to figure out a good way to programmatically generate contours describing a 2D surface, from a 3D STEP model. Application is generating NC code for a laser-cutting program from a 3D model.
Note: it's easy enough to do this in a wide variety of CAD systems. I am writing software that needs to do it automatically.
For example, this (a STEP model):
Needs to become this (a vector file, like an SVG or a DXF):
Perhaps the most obvious way of tackling the problem is to parse the STEP model and run some kind of algorithm to detect planes and select the largest as the cut surface, then generate the contour. Not a simple task!
I've also considered using a pre-existing SDK to render the model using an orthographic camera, capture a high-res image, and then operating on it to generate the appropriate contours. This method would work, but it will be CPU-heavy, and its accuracy will be limited to the pixel resolution of the rendered image - not ideal.
This is perhaps a long shot, but does anyone have thoughts about this? Cheers!
I would use a CAD library to load the STEP file (not a CAD API), look for the planar face with the higher number of edge curves in the face loop and transpose them on the XY plane. Afterward, finding 2D geometry min/max for centering etc. would be pretty easy.
Depending on the programming language you are using I would search for "CAD control" or "CAD component" on Google combining it with "STEP import".
I'm trying to make an object recognition program using a k-NN classifier. I've got a bunch of images for the training part of the classifier and a bunch of images to recognize. Those images are in grayscale and there's an object per image. The problem is that there's only the edge of the object (not filled), so I don't think using regionprops(img,'centroid') will work properly for what I understand...
So how can I get their center of mass?
xenoclast's answer should be quite clear, just to add something extra.
As you are done creating the binary image from the grayscale image of yours using im2bw; if the edge of your the object is a the boundary that covers the object fully, you may use regionprops(bw,'centroid') directly without going through imfill.
The first step would be to binarise the image with im2bw. Then you can use imfill(img, 'holes') to turn it from an outline into a filled solid. After that regionprops will work as expected.
I am trying to use the vision.cascadeObjectDetector (MATLAB) to detect heads from a CCTV footage(and not faces). Till now, I have tried the following:
Used vision.cascadeObjectDetector to detect faces.
trained it to detect a sign (an example shown in read me file)
created a folder with positive & negative training HEAD images & same for testing
tried to create a .mat file using trainingImageLabeler (although it gave me an error when trying to pass it as param to the object detector).
Can someone try to put me in the right track, to start detecting heads using Viola-Jones Haar features please?
Recently, I have to do a project of multi view 3D scanning within this 2 weeks and I searched through all the books, journals and websites for 3D reconstruction including Mathworks examples and so on. I written a coding to track matched points between two images and reconstruct them into 3D plot. However, despite of using detectSURFFeatures() and extractFeatures() functions, still some of the object points are not tracked. How can I reconstruct them also in my 3D model?
What you are looking for is called "dense reconstruction". The best way to do this is with calibrated cameras. Then you can rectify the images, compute disparity for every pixel (in theory), and then get 3D world coordinates for every pixel. Please check out this Stereo Calibration and Scene Reconstruction example.
The tracking approach you are using is fine but will only get sparse correspondences. The idea is that you would use the best of these to try to determine the difference in camera orientation between the two images. You can then use the camera orientation to get better matches and ultimately to produce a dense match which you can use to produce a depth image.
Tracking every point in an image from frame to frame is hard (its called scene flow) and you won't achieve it by identifying individual features (such as SURF, ORB, Freak, SIFT etc.) because these features are by definition 'special' in that they can be clearly identified between images.
If you have access to the Computer Vision Toolbox of Matlab you could use their matching functions.
You can start for example by checking out this article about disparity and the related matlab functions.
In addition you can read about different matching techniques such as block matching, semi-global block matching and global optimization procedures. Just to name a few keywords. But be aware that the topic of stereo matching is huge one.