In MeshLab, you can use the quality mapper to maps some qualities (values) on your mesh points to specific colors. The QualityMapperDialog offers an equalizer function that has three bars and affects the histogram of the quality. What is this effect, and what how does the three bars adjustment affect it?
Meshlab calls "quality" to a scalar value you can attach to each vertex or face of
the mesh. Examples of some qualities can be:
Area of each triangle.
ShapeFactor of each triangle (how it differs from an equilateral triangle).
Euclidean distance from each vertex to a given point.
Geodesic distance from each vertex to a given point.
Distance from each vertex to nearest border.
In general, any measure you can compute by vertex/face. For example, you can create a vertex quality using the formula of Relative Luminance (0.2126*R + 0.7152*G + 0.0722*B)
You can see if your mesh has some quality if the info panel shows the flags "VQ" (Vertex Quality) or "FQ" (Face Quality). To represent all those quality values meshlab offers you several options:
An histogram, available in the option Render->Show Quality Histogram
Contour lines, available in the option Render->Show Quality Contour
A mapping from scalar quality values to RGB values in vertex/faces.
For example, this is the Pythagoras mesh with "Distance to border" quality represented both as histogram and RGB values on vertex.
The Edit->Quality Mapper dialog allows the user to control the RGB color assigned to each scalar value. There is two panels there:
The "Transfer Function" panel lets you choose the color (R,G,B values) to each value inside your quality window of interest.
The "Equalizer" panel lets you to define that quality window of interest. The three bars allows you to define the lower and upper quality values in which you are interested, and also the main value of interest inside that interval in which you want to focus.
Here is the default window of interest, which map all the quality values to the complete RGB ramp.
Here we will use a window of interest for the qualities, so your ramp will map only the interval (25.43 ..45.57)
Here we use the same window of interest, but will focus on values around quality 43.00. Half of your ramp will be under value 43 and half of the ramp will be above 43. It may be easier to understand if you look at the "Gamma correction" graph... the input values are adapted to follow that red curve, that deform the qualities to adapt to a curve where 24.43 -> 0%, 25.57->100% and 43.00 -> 50%.
Related
I am looking for a workflow that would clean (and possibly straighten) old and badly scanned images of musical scores (like the one below).
I've tried to use denoise, hough filters, imagemagick geometry filters, and I am struggling to identify the series of filters that remove the scanner noise/bias.
Just some quick ideas:
Remove grayscale noise: Do a low pass filter (darks), since the music is darker than a lot of the noise. Remaining noise is mostly vertical lines.
Rotate image: Sum grayscale values for each column of the image. You'll get a vector with the total pixel lightness in that column. Use gradient descent or search on the rotation of the image (within some bounds like +/-15deg rotation) to maximize the variance of that vector. Idea here is that the vertical noise lines indicate vertical alignment, and so we want the columns of the image to align with these noise lines (= maximized variance).
Remove vertical line noise: After rotation, take median value of each column. The greater the distance (squared difference) a pixel is from that median darkness, the more confident we are it is its true color (e.g. a pure white or black pixel when vertical noise was gray). Since noise is non-white, you could try blending this distance by the whiteness of the median for an alternative confidence metric. Ideally, I think here you'd train some 7x7x2 convolution filter (2 channels being pixel value and distance from median) to estimate true value of the pixel. That would be the most minimal machine learning approach, not using some full-fledged NN. However, given your lack of training data, we'll have to come up with our own heuristic for what the true pixel value is. You likely will need to play around with it, but here's what I think might work:
Set some threshold of confidence; above that threshold we take the value as is. Below the threshold, set to white (the binary expected pixel value for the entire page).
For all values below threshold, take the max confidence value within a +/-2 pixels L1 distance (e.g. 5x5 convolution) as that pixel's value. Seems like features are separated by at least 2 pixels, but for lower resolutions that window size may need to be adjusted. Since white pixels may end up being more confident overall, you could experiment with prioritizing darker pixels (increase their confidence somehow).
Clamp the image contrast and maybe run another low pass filter.
I would like to know the difference between contrast stretching and histogram equalization.
I have tried both using OpenCV and observed the results, but I still have not understood the main differences between the two techniques. Insights would be of much needed help.
Lets Define Contrast first,
Contrast is a measure of the “range” of an image; i.e. how spread its intensities are. It has many formal definitions one famous is Michelson’s:
He says contrast = ( Imax - Imin )/( Imax + I min )
Contrast is strongly tied to an image’s overall visual quality.
Ideally, we’d like images to use the entire range of values available
to them.
Contrast Stretching and Histogram Equalisation have the same goal: making the images to use entire range of values available to them.
But they use different techniques.
Contrast Stretching works like mapping
it maps minimum intensity in the image to the minimum value in the range( 84 ==> 0 in the example above )
With the same way, it maps maximum intensity in the image to the maximum value in the range( 153 ==> 255 in the example above )
This is why Contrast Stretching is un-reliable, if there exist only two pixels have 0 and 255 intensity, it is totally useless.
However a better approach is Histogram Equalisation which uses probability distribution. You can learn the steps here
I came across the following points after some reading.
Contrast stretching is all about increasing the difference between the maximum intensity value in an image and the minimum one. All the rest of the intensity values are spread out between this range.
Histogram equalization is about modifying the intensity values of all the pixels in the image such that the histogram is "flattened" (in reality, the histogram can't be exactly flattened, there would be some peaks and some valleys, but that's a practical problem).
In contrast stretching, there exists a one-to-one relationship of the intensity values between the source image and the target image i.e., the original image can be restored from the contrast-stretched image.
However, once histogram equalization is performed, there is no way of getting back the original image.
In Histogram equalization, you want to flatten the histogram into a uniform distribution.
In contrast stretching, you manipulate the entire range of intensity values. Like what you do in Normalization.
Contrast stretching is a linear normalization that stretches an arbitrary interval of the intensities of an image and fits the interval to an another arbitrary interval (usually the target interval is the possible minimum and maximum of the image, like 0 and 255).
Histogram equalization is a nonlinear normalization that stretches the area of histogram with high abundance intensities and compresses the area with low abundance intensities.
I think that contrast stretching broadens the histogram of the image intensity levels, so the intensity around the range of input may be mapped to the full intensity range.
Histogram equalization, on the other hand, maps all of the pixels to the full range according to the cumulative distribution function or probability.
Contrast is the difference between maximum and minimum pixel intensity.
Both methods are used to enhance contrast, more precisely, adjusting image intensities to enhance contrast.
During histogram equalization the overall shape of the histogram
changes, whereas in contrast stretching the overall shape of
histogram remains same.
If a program displays a pixel at X,Y on a display with resolution A, can I precisely predict at what coordinates the same pixel will display at resolution B?
MORE INFORMATION
The 2 display resolutions are:
A-->1366 x 768
B-->1600 x 900
Dividing the max resolutions in each direction yields:
X-direction scaling factor = 1600/1366 = 1.171303075
Y-direction scaling factor = 900/768 = 1.171875
Say for example that the only red pixel on display A occurs at pixel (1,1). If I merely scale up using these factors, then on display B, that red pixel will be displayed at pixel (1.171303075, 1.171875). I'm not sure how to interpret that, as I'm used to thinking of pixels as integer values. It might help if I knew the exact geometry of pixel coordinates/placement on a screen. e.g., do pixel coordinates (1,1) mean that the center of the pixel is at (1,1)? Or a particular corner of the pixel is at (1,1)? I'm sure diagrams would assist in visualizing this--if anyone can post a link to helpful resources, I'd appreciate it. And finally, I may be approaching this all wrong.
Thanks in advance.
I think, your problem is related to the field of scaling/resampling images. Bitmap-, or raster images are digital photographs, so they are the most common form to represent natural images that are rich in detail. The term bitmap refers to how a given pattern (bits in a pixel) maps to a specific color. A bitmap images take the form of an array, where the value of each element, called a pixel picture element, correspond to the color of that region of the image.
Sampling
When measuring the value for a pixel, one takes the average color of an area around the location of the pixel. A simplistic model is sampling a square, and a more accurate measurement is to calculate a weighted Gaussian average. When perceiving a bitmap image the human eye should blend the pixel values together, recreating an illusion of the continuous image it represents.
Raster dimensions
The number of horizontal and vertical samples in the pixel grid is called raster dimensions, it is specified as width x height.
Resolution
Resolution is a measurement of sampling density, resolution of bitmap images give a relationship between pixel dimensions and physical dimensions. The most often used measurement is ppi, pixels per inch.
Scaling / Resampling
Image scaling is the name of the process when we need to create an image with different dimensions from what we have. A different name for scaling is resampling. When resampling algorithms try to reconstruct the original continuous image and create a new sample grid. There are two kind of scaling: up and down.
Scaling image down
The process of reducing the raster dimensions is called decimation, this can be done by averaging the values of source pixels contributing to each output pixel.
Scaling image up
When we increase the image size we actually want to create sample points between the original sample points in the original raster, this is done by interpolation the values in the sample grid, effectively guessing the values of the unknown pixels. This interpolation can be done by nearest-neighbor interpolation, bilinear interpolation, bicubic interpolation, etc. But the scaled up/down image must be also represented over discrete grid.
The simple question is - is there any difference between gl.LINEAR_MIPMAP_NEAREST and gl.NEAREST_MIPMAP_LINEAR? I've used the first, with bad results (see below) and found the second on the web. Interestingly, both are defined (in Chrome), and I wonder what their difference is.
The real question is - If I have a texture atlas with transparency (containing glyphs), can I use mipmapping? When zooming to small sizes, the glyphs flicker, which I want to eliminate by mipmapping.
But when I turn on mipmapping (only changing the TEXTURE_MIN_FILTER from LINEAR to LINEAR_MIPMAP_NEAREST, and calling generateMipmap() afterwards), the transparency is completely gone and the entire texture turns black.
I understand that mipmapping may cause bleeding of the black ink into the transparent area, but not fill the entire texture at all mipmap levels (including the original size).
What scrap of knowledge do I miss?
From the docs
GL_NEAREST
Returns the value of the texture element that is nearest (in Manhattan distance) to the center of the pixel being textured.
GL_LINEAR
Returns the weighted average of the four texture elements that are closest to the center of the pixel being textured.
GL_NEAREST_MIPMAP_NEAREST
Chooses the mipmap that most closely matches the size of the pixel being textured and uses the GL_NEAREST criterion (the texture element nearest to the center of the pixel) to produce a texture value.
GL_LINEAR_MIPMAP_NEAREST
Chooses the mipmap that most closely matches the size of the pixel being textured and uses the GL_LINEAR criterion (a weighted average of the four texture elements that are closest to the center of the pixel) to produce a texture value.
GL_NEAREST_MIPMAP_LINEAR
Chooses the two mipmaps that most closely match the size of the pixel being textured and uses the GL_NEAREST criterion (the texture element nearest to the center of the pixel) to produce a texture value from each mipmap. The final texture value is a weighted average of those two values.
GL_LINEAR_MIPMAP_LINEAR
Chooses the two mipmaps that most closely match the size of the pixel being textured and uses the GL_LINEAR criterion (a weighted average of the four texture elements that are closest to the center of the pixel) to produce a texture value from each mipmap. The final texture value is a weighted average of those two values.
As for why your stuff turns black have you checked the JavaScript console for errors? The most likely reason is your texture is not a power of 2 in both dimensions. If that's the case, trying to use mips by switching from gl.LINEAR to gl.LINEAR_MIPMAP_NEAREST will not work because in WebGL mips are not supported textures that are not a power of 2 in both dimensions.
When we look at a photo of a group of trees, we are able to identify that the photo is predominantly green and brown, or for a picture of the sea we are able to identify that it is mostly blue.
Does anyone know of an algorithm that can be used to detect the prominent color or colours in a photo?
I can envisage a 3D clustering algorithm in RGB space or something similar. I was wondering if someone knows of an existing technique.
Convert the image from RGB to a color space with brightness and saturation separated (HSL/HSV)
http://en.wikipedia.org/wiki/HSL_and_HSV
Then find the dominating values for the hue component of each pixel. Make a histogram for the hue values of each pixel and analyze in which angle region the peaks fall in. A large peak in the quadrant between 180 and 270 degrees means there is a large portion of blue in the image, for example.
There can be several difficulties in determining one dominant color. Pathological example: an image whose left half is blue and right half is red. Also, the hue will not deal very well with grayscales obviously. So a chessboard image with 50% white and 50% black will suffer from two problems: the hue is arbitrary for a black/white image, and there are two colors that are exactly 50% of the image.
It sounds like you want to start by computing an image histogram or color histogram of the image. The predominant color(s) will be related to the peak(s) in the histogram.
You might want to change the image from RGB to indexed, then you could use a regular histogram and detect the pics (Matlab does this with rgb2ind(), as you probably already know), and then the problem would be reduced to your regular "finding peaks in an array".
Then
n = hist(Y,nbins) bins the elements in vector Y into 10 equally spaced containers and returns the number of elements in each container as a row vector.
Those values in n will give you how many elements in each bin. Then it's just a matter of fiddling with the number of bins to make them wide enough, and with how many elements in each would make you count said bin as a predominant color, then taking the bins that contain those many elements, calculating the index that corresponds with their middle, and converting it to RGB again.
Whatever you're using for your processing probably has similar functions to those
Average all pixels in the image.
Remove all pixels that are farther away from the average color than standard deviation.
GOTO 1 with remaining pixels until arbitrarily few are left (1 or maybe 1%).
You might also want to pre-process the image, for example apply high-pass filter (removing only very low frequencies) to even out lighting in the photo — http://en.wikipedia.org/wiki/Checker_shadow_illusion