I am trying to solve the problem of detecting partially or fully overlapping components in an image. The components can be text, barcodes, QR codes, rectangle boundaries etc.
See the images below for better understanding of the problem.
Below label/image doesn't have any overlappings:
Below label/image has many overlappings:
My first thoughts are finding the four corner coordinates of each individual component. Then the problem reduces to finding if any two components(rectangles) out of all the components(rectangles) overlap or not.
But I'm clueless how to find the coordinates of each individual component from the image. Also the above approach fails if we have fully overlapping component printed inside another component. Is there any better way to solve the problem?
Related
I am placing icons with a fixed diameter/radius on a line using d3.scaleTime. This works well except for the case in which dates are close to one another, leading to an unwanted overlap.
In that specific case, I would want the icons to "relax" and not touch.
My code rather complex, including animations etc. — so I drew the problem here:
These are my attempts:
I looked at d3-force for collision prevention, but I was not quite sure how to merge such an approach with an existing time scale. Could this be helpful? http://jsbin.com/gist/fee5ce57c3fc3e94c3332577d1415df4 However, it may occur that the icons then do not align on a horizontal straight line anymore, which is a disadvantage, because I do not want them to spread vertically.
I also thought about calculating overlaps and then manually adjusting the data so that the overlap does not occur. That, however seems a bit more complex because I would have to somehow recursively find the best position for every icon.
Could interpolation help me? I thought there must be something like "snap to grid", but then two icons could snap to the same position, couldn't they?
Which d3 concept makes most sense to solve this problem?
I have a problem finding defects at the edge of a circular object. It's hard to describe so I have a picture which may help a bit. I am trying to find the red marked areas, such as what is shown below:
I already tried matching with templates vision.TemplateMatcher(), but this only works well for the picture I made the template of.
I tried to match it with vision.CascadeObjectDetector() and I trained it with 150 images. I found only < 5% correct results with this.
I also tried matching with detectSURFFeatures() and then matchFeatures(), but this only works on quite similar defects (when the edges are not closed it fails).
Since the defects are close to the half of a circle, I tried to find it with imfindcircles(), but there I find so many possible results. When I take the one with the highest metric sometimes I get the right one but not even close to 30%.
Do any of you have an idea what I can try to find at least more than 50%?
If someone has an idea and wants to try something I added another picture.
Since I am new I can only add two pictures but if you need more I can provide more pictures.
Are you going to detect rough edges like that on smooth binary overlays as you provided before? For eg. are you making a program whose input consists of getting a black image with lots of circles with rough edges which its then supposed to detect? i.e. sudden rough discontinuities in a normally very smooth region.
If the above position is valid, then this may be solved via classical signal processing. My opinion, plot a graph of the intensity on a line between any two points outside and inside the circle. It should look like
.. continuous constant ... continuous constant .. continuous constant.. DISCONTINUOUS VARYING!! DISCONTINUOUS VARYING!! DISCONTINUOUS VARYING!! ... continuous constant .. continuous constant..
Write your own function to detect these discontinuities.
OR
Gradient: The rate of change of certain quantities w.r.t a distance measure.
Use the very famous Sobel (gradient) filter.
Use the X axis version of the filter, See result, if gives you something detectable use it, do same for Y axis version of filter.
In case you're wondering, if you're using Matlab then you just need to get a readily available and highly mentioned 3x3 matrix (seen almost everywhere on the internet ) and plug it into the imfilter function, or use the in-built implementation (edge(image,'sobel')) (if you have the required toolbox).
I'd like to program a detection of a rectangular sheet of paper which doesn't absolutely need to be perfectly straight on each side as I may take a picture of it "in the air" which means the single sides of the paper might get distorted a bit.
The app (iOs and android) CamScanner does this very very good and Im wondering how this might be implemented. First of all I thought of doing:
smoothing / noise reduction
Edge detection (canny etc) OR thresholding (global / adaptive)
Hough Transformation
Detecting lines (only vertically / horizontally allowed)
Calculate the intercept point of 4 found lines
But this gives me much problems with different types of images.
And I'm wondering if there's maybe a better approach in directly detecting a rectangular-like shape in an image and if so, if maybe camscanner does implement it like this as well!?
Here are some images taken in CamScanner.
These ones are detected quite nicely even though in a) the side is distorted (but the corner still gets shown in the overlay but doesnt really fit the corner of the white paper) and in b) the background is pretty close to the actual paper but it still gets recognized correctly:
It even gets the rotated pictures correctly:
And when Im inserting some testing errors, it fails but at least detects some of the contour, but always try to detect it as a rectangle:
And here it fails completely:
I suppose in the last three examples, if it would do hough transformation, it could have detected at least two of the four sides of the rectangle.
Any ideas and tips?
Thanks a lot in advance
OpenCV framework may help your problem. Also, you can look to this document for the Android platform.
The full source code is available on Github.
I am new to the JavaCV/OpenCV thing, so apologies in advance if I'm being a complete idiot...
I need to detect the "primary/main" face in an image (This image will for the most part be a "Profile picture"), face recognition is not required.
Due to the fact that the different haarcascade files each detect different faces and that the faces detected are sometimes not actually faces but arbitrary artifacts in the image, I need to decide which of the faces to use.
Assuming the faces detected are real faces, it makes sense to use the largest face because it is a profile pic.
The main problem I'm having is that the code detects (for some images) more that 1 face and the biggest face is actually not the persons face at all.
Here is an example from one of my tests where the code detected 2 faces, 1 being the real face and the other being the woman's bust, it just so happens that her bust is bigger than her face.
Face: java.awt.Rectangle[x=62,y=42,width=78,height=78] Area of 6084
Bust: java.awt.Rectangle[x=86,y=144,width=80,height=80] Area of 6400
So my question in short, if I have multiple detected faces, is there some sort of rating scale that I can use to determine which of the faces best matches what OpenCV sees as a face?
Unfortunately the face detection does not provide you such an option.
I guess your code looks something like this :
CvSeq faces = cvHaarDetectObjects(grayImage, cascade, ......);
So you get a CvSeq which is in fact nothing more than a pointer to the sequence of rectangles that delimits the faces. You get no more information about that.
Usually, I would say that the bust is under the head (even more, the head is above the rest of the body), except in some special cases ;).
You can simply utilize the Y position to discard the bust.
If other elements that are non body parts are detected as faces then you're doomed.
I have nothing useful to do and was playing with jigsaw puzzle like this:
alt text http://manual.gimp.org/nl/images/filters/examples/render-taj-jigsaw.jpg
and I was wondering if it'd be possible to make a program that assists me in putting it together.
Imagine that I have a small puzzle, like 4x3 pieces, but the little tabs and blanks are non-uniform - different pieces have these tabs in different height, of different shape, of different size. What I'd do is to take pictures of all of these pieces, let a program analyze them and store their attributes somewhere. Then, when I pick up a piece, I could ask the program to tell me which pieces should be its 'neighbours' - or if I have to fill in a blank, it'd tell me how does the wanted puzzle piece(s) look.
Unfortunately I've never did anything with image processing and pattern recognition, so I'd like to ask you for some pointers - how do I recognize a jigsaw piece (basically a square with tabs and holes) in a picture?
Then I'd probably need to rotate it so it's in the right position, scale to some proportion and then measure tab/blank on each side, and also each side's slope, if present.
I know that it would be too time consuming to scan/photograph 1000 pieces of puzzle and use it, this would be just a pet project where I'd learn something new.
Data acquisition
(This is known as Chroma Key, Blue Screen or Background Color method)
Find a well-lit room, with the least lighting variation across the room.
Find a color (hue) that is rarely used in the entire puzzle / picture.
Get a color paper that has that exactly same color.
Place as many puzzle pieces on the color paper as it'll fit.
You can categorize the puzzles into batches and use it as a computer hint later on.
Make sure the pieces do not overlap or touch each other.
Do not worry about orientation yet.
Take picture and download to computer.
Color calibration may be needed because the Chroma Key background may have upset the built-in color balance of the digital camera.
Acquisition data processing
Get some computer vision software
OpenCV, MATLAB, C++, Java, Python Imaging Library, etc.
Perform connected-component on the chroma key color on the image.
Ask for the contours of the holes of the connected component, which are the puzzle pieces.
Fix errors in the detected list.
Choose the indexing vocabulary (cf. Ira Baxter's post) and measure the pieces.
If the pieces are rectangular, find the corners first.
If the pieces are silghtly-off quadrilateral, the side lengths (measured corner to corner) is also a valuable signature.
Search for "Shape Context" on SO or Google or here.
Finally, get the color histogram of the piece, so that you can query pieces by color later.
To make them searchable, put them in a database, so that you can query pieces with any combinations of indexing vocabulary.
A step back to the problem itself. The problem of building a puzzle can be easy (P) or hard (NP), depending of whether the pieces fit only one neighbour, or many. If there is only one fit for each edge, then you just find, for each piece/side its neighbour and you're done (O(#pieces*#sides)). If some pieces allow multiple fits into different neighbours, then, in order to complete the whole puzzle, you may need backtracking (because you made a wrong choice and you get stuck).
However, the first problem to solve is how to represent pieces. If you want to represent arbitrary shapes, then you can probably use transparency or masks to represent which areas of a tile are actually part of the piece. If you use square shapes then the problem may be easier. In the latter case, you can consider the last row of pixels on each side of the square and match it with the most similar row of pixels that you find across all other pieces.
You can use the second approach to actually help you solve a real puzzle, despite the fact that you use square tiles. Real puzzles are normally built upon a NxM grid of pieces. When scanning the image from the box, you split it into the same NxM grid of square tiles, and get the system to solve that. The problem is then to visually map the actual squiggly piece that you hold in your hand with a tile inside the system (when they are small and uniformly coloured). But you get the same problem if you represent arbitrary shapes internally.