I have a binary image in a matrix that I obtained by some image processing. When I say Binary Image I mean it has zeros and ones indicating complete black and complete white pixels. The image is mostly white and has some black spots. I now want to expand this black spots by some factor. How can I do it?
Have you tried dilate?
imdilate(1-binary_image, SE)
Where SE is the structural element.
Related
I have an image that is essentially a text document (black and white) but due to anti-aliasing/undersampling applied during scanning, the image contains a lot of color, light tone pixels and is thus saved as a full-color image i.e: takes a lot of space.
My goal is to be able to detect Black and White image candidates in order to convert them from full color to B&W which dramatically reduces their size.
Is there a way to detect such anti-aliased/undersampled images? Doing color pixel analysis doesn't help because the colored pixels end up being close in amount to the black pixels... Essentially I want to be able to detect that the colored pixels come from anti-aliasing/undersampling a black & white image and not from a picture type image.
Here is an example image:
As you can see there are many more colors than just black. However this image is a good candidate for Black & White / Greyscale conversion instead of full color. How can I detect such images? Please note that in this example the colors tend to be on the grey side but there are many cases where they are cyan or brown etc.
I think it is a valid question. I don't have 50 reputation to post a comment so I will post this as an answer.
Basically, in a black and white anti-aliased image the various grey colors are opacity differences of the black color. If we observe those pixels they will be like these listed below. So, if the operation is a color manipulation then apply the same opacity picked up from those grey pixels to the new color.
rgba(0,0,0,0.6)
rgba(0,0,0,0.9)
rgba(0,0,0,0.5)
rgba(0,0,0,0.9)
rgba(0,0,0,0.6)
rgba(0,0,0,0.1)
rgba(0,0,0,0.5)
In my opinion, the pixels other than grey, in this example image, cyan and brown as it appears can be safely ignored because they seemed like not part of the original text. If there were a few more example images of non grey pixels would have been good. But if we cannot ignore them just need to get the pixel opacity and apply the same color manipulation. In other words we treat them as black pixels.
I am working with RGB images that contain a single object against a monochrome background.
My goal is to isolate the object in the image and resize the image to contain only the object.
I have successfully been able to detect the object by converting the image to a binary image using an appropriate threshold. Then, in order to isolate the object in the original RGB image I use the binary image as a mask with the original RGB image.
maskedImage = bsxfun(#times,originalimage, cast(binaryimage,class(originalimage)));
This leaves me with a image only containing the object surrounded by a black background. This is due to the fact that the binary image mask I used contained the object in white pixels and the background in black pixels and since possess intensity values of 0 the masking process converted all pixels that didn't belong to the object to black pixels. I've attached an example below.
I would now like to draw a bounding box around the object and resize the image to the size of the bounding box, so that I can get rid as much of the surrounding black pixels as possible. Is there any way of doing this? Any help would be appreciated.
Given the segmented image, you want to crop out all of the black pixels and provide the closest bounding box that fully encapsulates the object. That's very simple.
You already have a binary mask that determines what is an object and what's background. You simply need to find the minimum spanning bounding box. You can find the top-left and bottom right corner by obtaining all of the pixel locations that are non-zero in the mask, and finding the minimum and maximum row and column coordinates. You'd then just use these to crop out the segmented image.
As such:
%// Find all non-zero locations in the mask
[row,col] = find(binaryImage);
%// Find the top left corner of the mask
topLeftRow = min(row);
topLeftCol = min(col);
%// Find the bottom right corner of the mask
bottomRightRow = max(row);
bottomRightCol = max(col);
%// Extract the object
extracted = maskedImage(topLeftRow:bottomRightRow, topLeftCol:bottomRightCol, :);
The words of the day are Bounding boxes !
If you want the minimum-area rectangle to crop only the nonzero values, you want the bounding box of your region, then set your phasers to stun and you're all set !
See this Matlab help forum question for more implementation details in Matlab.
I have a mortar image and it has some blackish pixels on the inside edges.
I need to make those blackish pixels colored same as the gray mortar color.
Look at the beginning of the image I have pointed out some blackish pixels on the edges
one (time consuming) solution is to switch colors to grey of every pixel that passes the following condition:
pixel is part of a component that is connected to a mortar but is not part of the mortar rectangle, this can be achieved with modifications to "Connected-component labeling" algorithm.
one way to do this modification is:
scan the image one time with Connected-component labeling algorithm (with threshold to contain grey and black colors pixels).
discard every component that doesnt contain a mortar (in your case this can be achieved with a demand of minimum pixels in a component).
scan the image a second time, each fixel that is on a line with an end and is black, switch color to grey.
this can be done in number of ways, one way is to search the line ends and then changes colors on all the line (you can flag the line ends in Connected-component labeling algorithm to save time).
hope this helps.
I have the original gray scale image below which I dilated and eroded in order to get the binary image. I have two questions:
1) How can I remove the shadows / extra noise (Examples of what I am referring to are circled red part of the images)?
2) In the binary image, the black singular beads are larger than they should be and the white ones are smaller. Other than eroding and dilating, is there a way to normalize their sizes? (see circled blue for clarification).
I've done some work with regionprops, but has not yielded what I need.
you can try use bwareaopen to remove small objects from binary image.
is there any difference between grey scale image and binary image?
Yes, the one is grayscale, e.g. gray scales from 0.255, the binary imange is binary, that means black(0) or white(1).
EDIT: Convert grayscale to binary. Directly converting color images (like RGB) to binary is not that easy, because you have to handle every color channel within the image seperatly.
Converting to binary is done using a ceratin threshold. E.g. you can say, all pixels with gray > 125 will become white, the others black.
There are several thresholding algorithm out there, but maybe the most common is Otsu. You can find it here Thresholding by Otsu
Yes, but I'm not sure what this has to do with C++ or programming. A binary image could be an image where pixels are only either red or blue.
Binary Image: http://en.wikipedia.org/wiki/Binary_image
Grayscale Image: http://en.wikipedia.org/wiki/Grayscale
A binary image has only two values for each pixel, 0 and 1 corresponding to black and white (or vice versa). A gray scale image has a certain number (probably 8) bits of information per pixel, hence, 256 possible grey values.
Of course, a grey scale image has a binary representation, but the smallest size of information is not a bit, so we don't call it a binary image.
edit Assuming you want to convert in Matlab, use im2bw. If you're not using Matlab, the idea of binarization is explained on that page as well. It's not difficult to port, it boils down to comparing every pixel to a threshold value.
Black and White image contains only two levels.
Gray image represent by black and white shades or combination of levels for e.g.
8 bit gray image means total 2^8 levels form black to white 0 = black and 255 is White.
Yes,
A binary image is one that consists of pixels that can have one of exactly two colors, usually black and white. It is also called bi-level or two-level.
A gray scale image is a kind of black and white or gray monochrome are composed exclusively of shades of gray.
Yes
Binary Image
A binary image is a black and white image where each pixel value is either 0 or 1. The value 0 represents background or black and the value 1 represents foreground or white.
Grayscale Image
A grayscale image is a black and white image with various shades of gray. The pixel value of a grayscale image is represented as an 8 bit signed integer. i.e values between 0 and 255. The pixel value of the grayscale image represents the brightness of the pixel. The value 0 is black, the value 255 is white, and the values in between makeup different shades of gray.