From 5a422aba704c375a307a902bafe658342e209906 Mon Sep 17 00:00:00 2001 From: scuri Date: Fri, 17 Oct 2008 06:10:15 +0000 Subject: First commit - moving from LuaForge to SourceForge --- html/en/proc_samples.html | 142 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 142 insertions(+) create mode 100644 html/en/proc_samples.html (limited to 'html/en/proc_samples.html') diff --git a/html/en/proc_samples.html b/html/en/proc_samples.html new file mode 100644 index 0000000..e51ff37 --- /dev/null +++ b/html/en/proc_samples.html @@ -0,0 +1,142 @@ + + + + + + +Processing Samples + + + + + +

Image Processing Samples

Fourier Transform

+ +

This is another command line application that process an image in the + Fourier Frequency Domain. In this domain the image is a map of the spatial + frequencies of the original image. It depends on the IM main library and on + the IM_FFTW library. The FFTW is a very fast Fourier transform, but is + contaminated by the GPL license, so everything must be also GPL. To use it in + a commercial application you must contact the MIT and pay for a commercial + license.

Se also + + Reference / Image Processing / Domain Transform Operations.

You can view the source code here: + proc_fourier.cpp

+ +

Hough Lines

+ +

The Hough transform can be used to detect lines in an image. But it results + are highly dependent on other operations done before and after the transform. + Here you can see a small pseudo code that ilustrates a possible sequence of + operations to detect lines using the hough transform.

First the canny operator will isolate the borders, the threshold will mark + the candidate pixels. After the transform the local maximum are isolated to + detect the line parameters of the lines that have many pixels from the + cadidate ones. The last operation will just draw the detected lines over the + original gray scale image.

+ +

imProcessCanny(in,out,stddev)
+imProcessHysteresisThreshold(in,out,low,high)
+
+imProcessHoughLines(in,out)
+imProcessLocalMaxThreshold(in,out,size,min)
+
+imProcessHoughLinesDraw(in1,in2,out)

+ +

Or a more complete sequence using another approach:

+ +

gray = imImageCreate(width, height, IM_GRAY, IM_BYTE);
+binary = imImageCreate(width, height, IM_BINARY, IM_BYTE);
+binary2 = imImageClone(binary);
+
+rhomax = sqrt(width*width +height*height)/2;
+hough_height=2*rhomax+1;
+hough = imImageCreate(180, hough_height, IM_GRAY, IM_INT);
+hough_binary = imImageCreate(180, hough_height, IM_BINARY, IM_BYTE);
+
+imConvertColorSpace(rgb, gray);
+
+// very important step, the quality of the detected lines are highly dependent on
+// the quality of the binary image
+// Using a simple threshold like in here maybe not a good solution for your image
+imProcessPercentThreshold(gray, binary, percent=50); 
+
+// eliminates unwanted objects, depending on the quality of the threshold
+// this step can be skiped
+imProcessBinMorphClose(binary, binary2, 3, 1); 
+imProcessPrune(binary2, binary, 4, size=100, 0);
+
+// Is there any holes in the objects?
+// Holes also have borders...
+imProcessFillHoles(binary, binary2, 4);
+
+// leave only the object borders
+imProcessPerimeterLine(binary2, binary);  
+
+// here you should have near only the lines you want to detect.
+// if there are more or less lines that you want redo the previous steps
+
+imProcessHoughLines(binary, hough);
+imProcessLocalMaxThreshold(hough, hough_binary, 7, 100);
+
+// this is optional, it will draw the results
+imProcessHoughLinesDraw(gray,hough_binary,draw_hough);

+ +

In the result of imProcessLocalMaxThreshold there will be several white + pixels. They represent the detected lines. Defining:

+ +

Y = a * X + b
+cos(theta) * X + sin(theta) * Y = rho
+
+where:
+  X = x - width/2
+  Y = y - height/2
+
+because the origin of the transform is in the center of the image

+ +

Each coordinate in the transform has values in the intervals:

+ +

theta = 0 .. 179  (horizontal coordinate of the hough space)
+rho = -rhomax .. rhomax    (vertical coordinate of the hough space, 
+                            vertically centered in the image)
+
+where:
+    rhomax = sqrt(width*width + height*height) /2            (width and height of the original image)

+ +

For each (xi, yi) point found in the result image:

+ +

theta = xi;
+rho = yi - rhomax;
+
+then:
+
+a = -cos(theta)/sin(theta);
+b = (rho + (width/2)*cos(theta) + (height/2)*sin(theta))/sin(theta);

+ +

The complex formula for "b" came from the fact that we have to shift the + result to the image origin at (0,0).

+ +

Image Analysis

+ +

The following pseudo code ilustrates the sequence of operations to measure + regions. This is also called Blob Analysis.

First the regions are isolated from background using a threshold. Then + regions too small or too large are eliminated and the holes are filled in this + example. After the regions are found we can start measuring properties of the + regions like area and perimeter.

+ +

imProcessSliceThreshold(in, out, level1, level2)
+imProcessPrune(in, out, connect, size1, size2)
+imProcessFillHoles(in, out, connect)
+imAnalyzeFindRegions(in, out, connect)
+imAnalyzeMeasureArea(in, area)
+imAnalyzeMeasurePerimeter(in, perim)

+ + + + + + \ No newline at end of file -- cgit v1.2.3