I am working on a lane detection project. Figure 1 shows one of the frames of the dashboard recording.
I have taken the following steps:
Removed the noise.
Detected the edges using Canny Edge Detector.
Masked out the top half of the image.
Applied a close morpholody to make continuous lines.
Applied a skeleton morphology to get the thinnest lines.
Performed a Hough transform on binary mask to obtain Hough matrix.
Identified the peaks in Hough matrix.
Extracted lines from the binary mask using houghlines function.
Calculated the line and marker positions from the previous step.
Visualized the line ([165,1,28]) and marker ([255,157,11]) on Figure 1.
But, as it can be seen, the white dashed lanes on the RHS are not detected. Where do you think the issue is and do you have any suggestions to fix it? Thanks.
I have tried to threshold the image by creating a binary mask using the histogram of the image and obtained better results but that method required some parameter tuning, which I am trying to avoid as this code (eventually) will be tested on a continuous video instead of a single frame.
The Matlab implementation of houghlines accepts the arguments 'MinLength' and 'FillGap'.
'MinLength'
By playing around (=decreasing) with the 'MinLength' Parameter you should be able to make houghlines() accept also the short line on the right side. As your Image looks quite clean, you can even try to reduce it to a very small value such that even the very small part in the lower right corner can be detected as a line.
'FillGap'
Next increase 'FillGap' to allow larger gaps between two line sgments. This might help you, produce one single line, for both right segments.
Hope this helps.
Related
i need some help with a corner detection.
I printed a checkerboard and created an image of this checkerboard with a webcam. The problem is that the webcam has a low resolution, therefore it do not find all corners. So i enhanced the number of searched corner. Now it finds all corner but different one for the same Corner.
All Points are stored in a matrix therefore i don't know which element depends to which point.
(I can not use the checkerboard function because the fuction is not available in my Matlab Version)
I am currently using the matlab function corner.
My Question:
Is it possible to search the Extrema of all the point clouds to get 1 Point for each Corner? Or has sb an idea what i could do ? --> Please see the attached photo
Thanks for your help!
Looking at the image my guess is that the false positives of the corner detection are caused by compression artifacts introduced by the lossy compression algorithm used by your webcam's image acquisition software. You can clearly spot ringing artifacts around the edges of the checkerboard fields.
You could try two different things:
Check in your webcam's acquisition software whether you can disable the compression or change to a lossless compression
Working with the image you already have, you could try to alleviate the impact of the compression by binarising the image using a simple thresholding operation (which in the case of a checkerboard would not even mean loosing information since the image is intrinsically binary).
In case you want to go for option 2) I would suggest to do the following steps. Let's assume the variable storing your image is called img
look at the distribution of grey values using e.g. the imhist function like so: imhist(img)
Ideally you would see a clean bimodal distribution with no overlap. Choose an intensity value I in the middle of the two peaks
Then simply binarize by assigning img(img<I) = 0; img(img>I) = 255 (assuming img is of type uint8).
Then run the corner algorithm again and see if the outliers have disappeared
I am trying to obtain edges from image using canny detector on a 16 gray level image (see image).
Although I am getting the boundaries, some regions present their boundaries disconnected (see image).
Anyone knows how can get the complete boundaries?
In my experience, the Canny edge detection algorithm commonly leaves these kinds of artifacts when it's used to process low-resolution images. As odd as it sounds, you can often achieve much better results by simply increasing the size of the image (for example, using bilinear interpolation). I'm not sure what tools you're using, but in OpenCV, this can be done using a simple resize function. By increasing the width and height by a factor of 4, I can get the following boundaries:
Of course, if you look closely, you'll still see a few small gaps. One possible solution to address that it to use morphological transformations. For example, if I use a morphological close operation on the above image, this will help connect the gaps:
It's worth noting that since your edge image appears to be black edges on white background, you will probably need to use a morphological open operation instead.
Also, note that the low threshold of the Canny operation can also have some influence on addressing the kinds of gaps you're seeing under some circumstances. Before trying more computationally expensive approaches, it's worth trying to lower the low threshold to see if you can pull in the missing edges.
Try use a sharpen filter before the canny contour detector to accentuate the edges.
At image i need find "table" - simple rectangle.
Problem is with edge recognition, because potencial photos will be "dark".
I tried edge - sobel, canny, log, .... - recognition and after that Hough transformation and line finding. But this algorithms are not enough for this task.
Something what can help me:
- it is rectangle!, only in perspective view (something like fitting perspective rectangle?)
- that object MUST cover atleast for example 90% of photo (i know i need looking near photo edges)
- that rectangle have fast same color (for example wood dining table)
- i need find atleast "only" 4 corners..(but yes, better will be find the edges of that table)
I know how for example sobel, canny or log algorithms works and Hough as well. And naturally those algorithms fail at dark or non-contrast images. But is there some another method for example based at "fitting"?
Images showing photo i can get (you see it would be dark) and what i need find:
and this is really "nice" picture (without noise). I tested it on more noise pictures and the result was..simply horrible..
Result of this picture with actual algorithm log (with another ones it looks same):
I know image and edge recognition is not simple challenge but are there some new better methods or something like that what i can try to use?
In one of posts in here i found LSD algorithm. It seems very nice descripted and it seems it is recognizing really nice straight lines as well. Do you think it would be better to use it insted of the canny or sobel detection?
Another solution will be corner detection, on my sample images it works better but it recognize too much points and there will problem with time..i will need to connect all the points and "find" the table..
Another solution:
I thought about point to point mapping. That i will have some "virtual" table and try to map that table above with that "virtual" table (simple 2d square in painting :] )..But i think point to point mapping will give me big errors or it will not working.
Does someone have any advice what algorithm use to?
I tried recognize edges in FIJI and then put the edge detected image in matlab, but with hough it works bad as well..:/..
What do you think it would be best to use? In short i need find some algorithm working on non contrast, dark images.
I'd try some modified snakes algorithm:
you parameterize your rectangle with 4 points and initialize them somewhere in the image corners. Then you move the points towards image features using some optimization algorithm (e.g. gradient descent, simulated annealing, etc.).
The image features could be a combination of edge features (e.g. sobel directly or sobel of some gaussian filtered image) to be evaluated on the lines between those four points and corner features to be evaluated at those 4 points.
Additionally you can penalize unlikely rectangles (maybe depending on the angles between the points or on the distance to the image boundary).
I have a stack of images with a bar close to the center. As the stack progresses the bar pivots around one end and the entire stack contains images with the bar rotated at many different angles up to 45 degrees above or below horizontal.
As shown here:
I'm looking for a way to rotate the bar and/or entire image and align everything horizontally before I do my other processing. Ideally this would be done in Matlab / imageJ / ImageMagick. I'm currently trying to work out a method using first Canny edge detection, followed by a Hough transform, followed by an image rotation, but I'm hoping this is a specific case of a more general problem which has already been solved.
If you have the image processing toolbox you can use regionprops with the 'Orientation' property to find the angle.
http://www.mathworks.com/help/images/ref/regionprops.html#bqkf8ji
The problem you are solving is known as image registration or image alignment.
-The first thing you need to due is to treshold the image, so you end up with a black and white image. This will simplify the process.
-Then you need to calculate the mass center of the imgaes and then translate them to match each others centers.
Then you need to rotate the images to matcheach other. This could be done using the principal axis measure. The principal axis will give you the two axis that explain most of the variance in the population. Which will basically give you a vector showing which way your bar is pointing. Then all you need to due is rotate the bars in the same direction.
-After the principal axis transformation you can try rotating the pictues a little bit more in each direction to try and optimise the rotation.
All the way through your translation and rotation you need a measure for showing you how good a fit your tranformation is. This measure can be many thing. If the picture is black and white a simple subtraction of the pictures is enough. Otherwise you can use measures like mutual information.
...you can also look at procrustes analysis see this link for a matlab function http://www.google.dk/search?q=gpa+image+analysis&oq=gpa+image+analysis&sugexp=chrome,mod=9&sourceid=chrome&ie=UTF-8#hl=da&tbo=d&sclient=psy-ab&q=matlab+procrustes+analysis&oq=matlab+proanalysis&gs_l=serp.3.1.0i7i30l4.5399.5883.2.9481.3.3.0.0.0.0.105.253.2j1.3.0...0.0...1c.1.5UpjL3-8aC0&pbx=1&bav=on.2,or.r_gc.r_pw.r_qf.&bvm=bv.1355534169,d.Yms&fp=afcd637d8ae07bde&bpcl=40096503&biw=1600&bih=767
You might want to look into the SIFT transform.
You should take as your image the rectangle that represents a worst case guess for your bar and determine the rotation matrix for that.
See http://www.vlfeat.org/overview/sift.html
Use the StackReg plugin of ImageJ. I'm not 100% sure but I think it already comes installed with FIJI (FIJI Is Just ImageJ).
EDIT: I think I have misread your question. That is not a stack of images you are trying to fix, right? In that case, a simple approach (probably not the most efficient but definetly works), is the following algorithm:
threshold the image (seems easy, your background is always white)
get a long horizontal line as a structuring element and dilate the image with it
rotate the structuring element and keep dilating image, measuring the size of the dilation.
the angle that maximizes it, is the rotation angle you'll need to fix your image.
There are several approaches to this problem as suggested by other answers. One approach possibly similar to what you are already trying, is to use Hough transform. Hough transform is good at detecting line orientations. Combining this with morphological processing and image rotation after detecting the angle you can create a system that corrects for angular variations. The basic steps would be
Use Morphological operations to make the bar a single line blob.
Use Hough transform on this image.
Find the maximum in the transform output and use that to find orientation angle.
Use the angle to fix original image.
A full example which comes with Computer Vision System Toolbox for this method. See
http://www.mathworks.com/help/vision/examples/rotation-correction-1.html
you can try givens or householder transform, I prefer givens.
it require an angle, using cos(angle) and sin(angle) to make the givens matrix.
Can anyone guide me how the function 'hough transform' works in matlab?? The problem is that i have an image containing two straight rectangles and one rectangle is tilted at some angle. According to me after applying hough transform; i should get a line structure of 1X6 but i am getting a structure of 1x14. Can anyone help me? I have also uploaded the images:
You can't restrict Hough Transform to give a structure of 1x6.It doesn't produce stabile results.It also doesn't work well when looking further ahead on curved roads. I should not acquire 1x6 structure from each frame.Instead, I should take all returned line segments and use some logic to determine the lane markings.
First of all, your image actually looks slightly blurred. I don't know if it actually is , but if so, you need to run an edge detection algorithm, so your hough transform does not detect the blurred part of the line.
Second of all, you need to reduce the number of detected lines, simply by taking out any lines which does not have enough points going through it. This can be done by thresholding the H variable in [H,t,r]=hough(image).
Additional Sources:
http://en.wikipedia.org/wiki/Hough_transform
http://www.mathworks.com/help/toolbox/images/ref/hough.html