Billedanalyse af AFM billeder

From Midtiby

Forskellige links jeg har samlet til et projekt omkring billedanalyse af AFM billeder.

Matlab og billedbehandling

1. MATLAB and Octave Functions for Computer Vision and Image Processing [1]

   Guld side!!!

Ikke sorteret

1. Image Feature extraction for Classification purposes [2]
2. Mathtools: C++ Image Processing [3]
3. Matlab tutorial [4]
4. COURSES ON MATHEMATICAL MORPHOLOGY [5]
5. Fundamentals of Image Processing [6]
6. Erode og dilate uden IPT [7] [8]
7. LSQ Legendre polynomial fitting, m.m. [9]

Referencer

1. Particle Characterization with AFM (Natasha Starostina, Paul West 2006) [10]

   Care should be taken when processing the image to avoid introducing artificats into the image. A common artifact is shown in Figure 1B. The bands in the image are derived from line by line leveling without using the threshholding. Such bands can result in erroneous nanoparticle characterization.

2. AFM Image Artifacts (Paul West, Natalia Starostina) [11]

   1 The piezoelectric scanners that move the probe in an atomic force microscope typically move the probe in a curved motion over the surface. The curved motion results in a “Bow” in the AFM image. Also, a large planar background or “Tilt” can be observed if the probe/sample angle is not perpendicular.

   Often the images measured by the AFM include a background “Bow” and a background “Tilt” that are larger than the features of interest. In such cases the background must be subtracted from the image. This is often called “leveling” or “flattening” the image. After “leveling” the desired features are typically directly seen in the image.

   2 As mentioned in section 2.4, most images have some tilt and bow that is introduced to the images by the scanner or stage configuration. There are a number of background subtraction options that are possible. The two most common types are:

   Line by line leveling - 0 to 4(th) order

   Plane Leveling - 0 to 4(th) order

   Also, software typically allows you to exclude areas from the leveling. When an area is excluded, it is not used for the calculation of the background in the image.

3. New method to estimate step heights in scanning-probe microscope images (Hal Edwards 1997) [12]

   Abstract. A new algorithm, the polynomial step-function fit (PSFF), is presented. The PSFF algorithm extracts step heights from noisy and distorted scanning-probe microscope (SPM) images. A one-dimensional, line-by-line implementation as well as a two-dimensional, full-image version are presented. The PSFF algorithm allows the correction of image distortions due to nonlinearities in the piezoelectric scanner and Abbe offset errors, but piezoelectric creep and hysteresis must be corrected separately, and may set the ultimate physical limitations on the accuracy of the PSFF algorithm. The PSFF algorithm is demonstrated with a real sample.

4. Influence of data analysis and other factors on the short-term stability of vertical scanning-probe microscope calibration measurements (Hal Edwards 1997)[13]