I am having trouble understanding the rawintden measurement in imageJ. It outputs numbers as large as 300,000, but pixel intensity is only measured on a scale of 0-255. Why is this scale so much larger and what does the calculation represent?
Thank you for your help!!
The value raw integrated density (RawIntDen) is the sum of all pixel values in the ROI (region of interest). Dividing this value by the number of pixels in the ROI gives the Mean. Since it is the sum of several pixels, its value is usually larger than the bit-depth of the image.
There is another measurement called IntDen which is the Area multiplied by the Mean. In an uncalibrated image, RawIntDen and IntDen are equal.
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Can anyone explain me how can I use the equation of bilateral filter in an image ,each coefficient in equation how can use it on an array and how the filter leave the edge without smoothing ? can anyone help me, please?and why(Ip - Iq) multiply in Iq
The filter computes a weighted sum of the pixel intensities. A normalization factor is always required in a weighted average, so that a constant signal keeps the same value.
The space factor makes sure that the filter value is influenced by nearby pixels only, with a smooth decrease of the weighting. The range factor makes sure that the filter value is influenced by pixels with close gray value only, with a smooth decrease of the weighting.
The idea behind this filter is to average the pixels belonging to the same homogeneous region as the center pixel, as if performing a local segmentation. The average performs smoothing/noise reduction, while restricting to the same region avoids spoling the edges by mixing with pixels of a very different intensity.
Nearest-neighbor is a commonly-used "filtering" technique for scaling pixel art while showing individual pixels. However, it doesn't work well for scaling with non-integral factors. I had an idea for a modification that works well for non-integral factors significantly larger than the original size.
Nearest-neighbor: For each output pixel, sample the original image at one location.
Linear: For each output pixel, construct a gradient between the two input pixels, and sample the gradient.
Instead, I want to calculate which portion of the original image would map to the output pixel rectangle, then calculate the average color within that region by blending the input pixels according to their coverage of the mapped rectangle.
This algorithm would produce the same results as supersampling with an infinite number of samples. It is not the same as linear filtering, as it does not produce gradients, only blended pixels on the input-pixel boundaries of the output image.
A better description of the algorithm is at this link: What is the best image downscaling algorithm (quality-wise)? . Note that the URL mentions downscaling, which could potentially have more than four pixels per output pixel. Upscaling has a maximum of four input pixels per output pixel processed, though.
Now is there any image editor or utility that supports weighted-average scaling?
I have 2 2D depth/height maps of equal dimension (256 x 256). Each pixel/cell of the depth map image, contains a float value. There are some pixels that have no information so are set to nan currently. The percentage of non nan cells can vary from ~ 20% to 80%. The depth maps are taken of the same area through point sampling an underlying common surface.
The idea is that the images represent a partial, yet overlapping, sampling of an underlying surface. And I need to align these images to create a combined sampled representation of the surface. If done blindly then the combined images have discontinuities especially in the z dimension (the float value).
What would be a fast method of aligning the 2 images? Translation in the x and y direction should be minimal only a few pixels (~ 0 to 10 pixels). But the float values of one image may need to be adjusted to align the images better. So minimizing the difference between the 2 images is the goal.
Thnx for any advice.
If your images are lacunar, one way is the exhaustive computation of a matching score in the window of overlap, ruling out the voids. FFT convolution will not apply. (Workload = overlap area * X-range * Y-range).
If both images differ in noise only, use the SAD matching score. If they also differ by the reference zero, subtract the average height before comparing.
You can achieve some acceleration by using an image pyramid, but you'll need to handle the voids.
Other approach could be to fill in the gaps by some interpolation method that somehow ensures that the interpolated values are compatible between both images.
How can I calculate the entropy of individual pixels? The function entropy of matlab calculates the entropy of an image. I want to calculate the entropy of every pixel
As I pointed out in my comment, it is meaningless to ask the entropy of a single pixel.
Having said that, you might want to look at entropyfilt - measuring the entropy of a region around a pixel.
I am comparing RGB images of small colored granules spilled randomly on a white backdrop. My current method involves importing the image into Matlab, converting to a binary image, setting a threshold and forcing all pixels above it to white. Next, I am calculating the percentage of the pixels that are black. In comparing the images to one another, the measurement of % black pixels is great; however, it does not take into account how well the granules are dispersed. Although the % black from two different images may be identical, the images may be far from being alike. For example, assume I have two images to compare. Both show a % black pixels of 15%. In one picture, the black pixels are randomly distributed throughout the image. In the other, a clump of black pixels are in one corner and are very sparse in the rest of the image.
What can I use in Matlab to numerically quantify how "spread out" the black pixels are for the purpose of comparing the two images?
I haven't been able to wrap my brain around this one yet, and need some help. Your thoughts/answers are most appreciated.
Found an answer to a very similar problem -> https://stats.stackexchange.com/a/13274
Basically, you would use the average distance from a central point to every black pixel as a measure of dispersion.
My idea is based upon the mean free path ()used in ideal gad theory / thermodynamics)
First, you must separate your foreground objects, using something like bwconncomp.
The mean free path is calculated by the mean distance between the centers of your regions. So for n regions, you take all n/2*(n-1) pairs, calculate all the distances and average them. If the mean distance is big, your particles are well spread. If it is small, your objects are close together.
You may want to multiply the resulting mean by n and divide it by the edge length to get a dimensionless number. (Independent of your image size and independent of the number of particles)