This is a table summary of all content for the exam of the Research Skill: Image Analysis at Tilburg University (-M-3). It summarises the lectures slides and the book Digital Image Processing by Gonzales. This table is an overview of everything you will need to pass the exam.
Step Methods
Pre 1. Spatial domain
Process 1.1 Point operators
sing 1.1.1 Adaptive Intensity Thresholding is hold local A pixel becomes white if its gray level is > T
Thresholding threshold for intensity value. Adaptive A pixel becomes black if its gray level is <=T
thresholding is threshold using operations
like Otsu.
1.1.2 Gamma Gamma <1 expands low-intensity values
Transformation (black) and compresses high-intensity
values (white) à makes image brighter
Gamma >1 expands high-intensity values
(white) and compresses low-intensity
values (black) à makes image darker
1.1.3 Log Maps a narrow range of low grayscale
Transformation values in input image into a wider range of
values in output
1.1.4 Histogram Equalizes the pixel intensities and
Equalization magnitudes, thus flattening high peaks in
the histogram
,1.1.5 Contrast Improve image contrast by stretching range
Stretching of intensity values
1.2 Spatial filtering
1.2.1 Averaging Replace pixel values with mean of a kernel
filter used to smooth time series, denoise image,
and unsharp masking. used to locally
smooth data and diminish noise
1.2.2 Gaussian Used to blur image and used in image
filter processing for smoothing, reducing noise,
and computing derivatives of an image
, 1.2.3 Laplacian Filter that sharpens a blurry image (Find
filter edges in an image or Subtract the Laplacian
filter from the original image to emphasize
detail)
- used for sharpening
- Sensitive to noise -prior smoothing
(preferably Gaussian smoothing) is
recommended
1.2.4 Median filter Filter that preserves the edges of an image
while smoothing to remove noise, used to
locally smooth data and diminish noise.
1.2.5 Gradient used to detect regions of rapid change in
filter signals and images
1.2.6 Prewitt Used particularly within edge detection
operator algorithms. Technically, it is a discrete
differentiation operator, computing an
approximation of the gradient of the image
intensity function. Unlike the Sobel,
this does not place any emphasis on the
pixels that are closer to the center of the
mask.
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