I want to split many images inside one image.
My method :
1- Quantize image using pyramid segmentation.
2- Extract contour from image.
3- Accumulate horizontal & vertical edges.
4- Compute the intersection of the horizontal & vertical lines.
What is your suggestion for this problem?
Please refer to the here to see sample images.
I assume the number and size of the images is variable (if they aren't, then you can easily cut at known distances)
Since the joint between different images is going to generate contrasts, you can use canny plus standard line detection. You can find a good tutorial here . Also check documentation of HoughLines & HoughLinesP.
After detecting the lines, you can discard all the non-horizontal and non-vertical ones. Then, you can find positions and distances between horizontal and vertical lines in order to compute the sub-images boundaries.
Related
I am working on image registration between LWIR & RGB images. I am able to extract the edges from both images.
RGB_Edges, LWIR_Edges
Now, I want to match the edges of these images to calculate homography.
I tried to match each edge of RGB with LWIR image separately using template matching (OpenCV) but it didn't worked.
Therefore, can anyone please suggest some methods to mach the edges/structures from both images that can be helpful to compute homography?
I will really appreciate any suggestion/help.
Thanks.
These two images are already fairly well aligned.
Due to the large thickness and irregularity of the edges, I doubt you can do much better.
If you have the option of operator supervision, point at corresponding points in the two images (four pairs are enough for an homography).
For an automated approach, you can try to thin the strokes then to find (approximate) line segments in both images. For a certain number of segments in one image, find the segment which is (approximately) parallel, close and facing with a significant overlap in the other. You can expect that these segments are in correspondence.
Next, you can you can obtain corresponding points by forming the intersections between some segments in each image (take segments that are close but as perpendicular as possible).
As this procedure will suffer from outliers, model fitting by RANSAC is probably a good option.
I am trying to create a panorama and I am stuck on the part where I have two separate warped images in two cv::Mat's and now I need to align them and create one single cv::Mat. I also need to average the pixel color value where the pixels in the images overlap to do elementary blending. Is there a built in function in opencv that can do this for me? I have been following the Basic Stitching Pipeline. I'm not sure how I can align and blend the images. I looked up a solution that does feature matching between the images and then we get the homography and just use the translation vector to align the images. Is this what I should be using?
Here are the warped images:
Image 1:
Image 1:
Generating a panaroma from a set of images is usually done using homographies. The reason for this is explained very well here.
You can refer to the code given by Eduardo here. It is also based on feature matching though.
You are right, you need to start with finding descriptors for features in the image (Brief descriptor might be a good idea) and then do feature matching. Once you have the correspondences, you will use those correspondences to estimate the homography. The homography will help you warp one of the image with respect to the other. Post this, you can simply blend them together (by simply add the two images, or taking the maximum value of the at each pixel between the two images)
I have two binary images of hand which are almost same.How should I compare them to know whether they represent almost same shape or not.I have tried finding euclidean distance between two images but its not giving correct answer if the image is slightly changed or moved to left or right or slight decrease in size.I have also tried HOG descriptors in opencv still I am unable to get correct answer if I compare more than one image.What is the best way to compare two binary images based on shape or any feature to know nearly matching images not considering the size of the image.Links to images are http://postimg.org/image/w20tuuzmv/ and http://postimg.org/image/jndr4br9x/
I think that Generalized Hough transform might be a good solution for you. Here is a tutorial about it.
Alternatively uou can try to cut hand from one image (just use contour bounding rect) and than use it as a template and search for it in second image using template matching technique - here you can read more about. When you will find point with highest correlation value, you need to decide whether it is big enough - you need to find threshold on your own.
Are the images just rotated, translated and scaled? If so you could compute the principal components of the images using PCA, then rotate the images so that the first component is in a certain direction (e.g. always vertical) you could then compute the centroids of the images and translate them to be always in the same position (e.g. center of the image), to use always the same scale you could resize the images so that the sum of the distances between each white pixel with the centroid is the same in both images. Now it's easy to compare the images for example score = np.sum(A==B)
I have two images which I know represent the exact same object. In the picture below, they are referred as Reference and Match.
The image Match can undergo the following transformations compared to Reference:
The object may have changed its appearance locally by addition(e.g. dirt or lettering added to the side) or omission (side mirror has been taken out).
Stretched or reduced in size horizontally only (it is not resized in vertical direction)
Portions of Reference image are not present in Match (shaded in red in Reference Image).
Question: How can the regions which have "changed" in the ways mentioned above be identified ?
Idea#1: Dynamic Time Warping seems like a good candidate once the beginning and end of Match image (numbered 1 and 3 in the image) are aligned with corresponding columns in Reference Image, but I am not sure how to proceed.
Idea#2: Match SIFT features across images. The tessellation produced by feature point locations breaks up the image into non-uniform tiles. Use feature correspondences across images to determine which tiles to match across images. Use a similarity measure to figure out any changes.
You might want to consider an iterative registration algorithm. Basically you want to perform optimization to find the parameters of the transform, in your case horizontal scaling and horizontal translation. Once you optimize the parameters you will have the transformation between the two images, transform one to match the other, and can then use a subtraction to identify the regions with differences.
For registration take a look at the ITK library.
You can probably do a gradient decent optimization using mutual information as the metric. It has a number of different transforms that will capture translation and scaling. The code should run quickly on the sample images you show.
I have 55 000 image files (in both JPG and TIFF format) which are pictures from a book.
The structure of each page is this:
some text
--- (horizontal line) ---
a number
some text
--- (horizontal line) ---
another number
some text
There can be from zero to 4 horizontal lines on any given page.
I need to find what the number is, just below the horizontal line.
BUT, numbers strictly follow each other, starting at one on page one, so in order to find the number, I don't need to read it: I could just detect the presence of horizontal lines, which should be both easier and safer than trying to OCR the page to detect the numbers.
The algorithm would be, basically:
for each image
count horizontal lines
print image name, number of horizontal lines
next image
The question is: what would be the best image library/language to do the "count horizontal lines" part?
Probably the easiest way to detect your lines is using the Hough transform in OpenCV (which has wrappers for many languages).
The OpenCV Hough tranform will detect all lines in the image and return their angles and start/stop coordinates. You should only keep the ones whose angles are close to horizontal and of adequate length.
O'Reilly's Learning OpenCV explains in detail the function's input and output (p.156).
If you have good contrast, try running connected components and analyze the result. It can be an alternative to finding lines through Hough and cover the case when your structured elements are a bit curved or a line algorithm picks up the lines you don’t want it to pick up.
Connected components is a super fast, two raster scan algorithm and will give you a mask with all you connected elements in it marked with different labels and accounted for. You can discard anything short ( in terms of aspect ratio). Overall, this can be more general, faster but probably a bit more involved than running Hough transform. The Hough transform on the other hand will be more tolerable for contrast artifacts and even accidental gaps in lines.
OpenCV has the function findContours() that find components for you.
you might want to try John' Resig's OCR and Neural Nets in Javascript