Prepare effectively for your Image Analysis exam with concise and structured cheatsheet.
Spanning 2 pages, this resource is tailored for exam success, offering a quick reference guide highlighted key topics.
With designated areas for personal notes and example questions to help you tackle ch...
Red0 - Yellow60 - Green120 - Cyan180 - Blue240-Magenta300 Thinning & Skeleton
Hue: The “true color” attribute. Saturation: The amount by The end points of the skeleton extend all the way to the edges
which the color as been diluted with white. Value: The degree of the input object. Medial axis skeletonization:
of brightness: a well-lit color has high intensity; a dark color Segmentation [[TN, FP], [FN, TP]] P: Predictive Postive
has low intensity Accuracy: (TP + TN) / all; Precision = TP / (TP + FP); Recall
Aliasing occurs when a signal is sampled at regular time =TP/(TP + FN); F = 2 x pr/(p+r); Jaccard Index: IoU, overlap /
intervals at slightly less than the period of the original signal. union
linear filters: a linear combination of the intensity values of the Segmentation principles: Discontinuity: To partition an image
center pixel and all neighboring pixels. based on abrupt changes in intensity (Point, Line
Laplacian [.-.] – sharpen / Gaussian / Averaging filters and Edge Detection); Similarity: To partition an image into
Median Filter - non linear filter similar regions (thresholding, region growing)
The basic difference between convolution and correlation is Canny Edge Detector: Noise reduction; Gradient calculation
that the convolution process rotates the kernel by 180 degrees. (Directional change in intensity in an image; Change in the
Edge Opreator [ - , 0, +] intensity in both the horizontal and vertical directions); Non-
You should normalize your image (scale between 0 and 1) for maximum suppression (finds the pixels with the maximum
Log & Gamma transformations value in the edge directions);Double thresholding (Strong –
High frequency components -large changes in grey values over most likely an edge; Weak – possibly an edge; Non-relevant –
small distances; (edges and noise) not an edge); Connectivity analysis (Connects weak pixels to
Low frequency components -by little change in the gray values. strong ones, if and only if at least one of the pixels around the
(backgrounds, skin textures) one being processed is a strong one); sigma : large detects
High pass filters (Sharpening) passes over the high frequency large scale edges; small detects fine features
components and reduces or eliminates low frequency Morphological Gradient: Beucher gradient - difference
components. Low pass filters (Smoothing) between the dilation and the erosion (D - E) by the SE
Band pass filters passes frequencies within a certain range and Morphological Watersheds:1.Initially, the set of pixels with
rejects (attenuates) frequencies outside that range. minimum gray level are 1, others 0. 2. In each subsequent step,
notch filter and butterworth filter are band-stop filter with a we flood the 3D topography from below and the pixels covered
narrow stopband. by the rising water are 1s and others 0s.
Smoothing Filters: Spatial Domain: Gaussian, Averaging - linear The watershed transform finds "catchment basins" and
filters, Median - good for impulse noise "watershed ridge lines” in an image by treating it as a surface
Frequency domain: Gaussian LPF, Ideal - Block all frequencies where - light pixels are high - dark pixels are low. (Dam
higher than the cut-off frequency ;Ringing (ripple effect) when Construction)
the cut-off too high,Butterworth: motion Distance transform of a binary image is defined by the distance
Sharpening Filter Spatial Domain: Laplacian (can be converted from every pixel to the nearest non-zero valued pixel
to frequency space) Frequency domain: HPF Ideal, Butterworth Detect Lines: Hough Transform (max votes in Hough space ->
and Gaussian image space); Each edge pixel votes (accumulator) in
XOR, A + B - AB parameter space for each possible line through it; Overlap of
Dilation (Overlap+) 1. If there is no overlap, the input pixel is circles can cause spurious centers
left at the background value. 2.If at least one pixel in the SE Markers: Internal markers are used to limit the number of
overlaps with a foreground pixel in the image underneath, the regions by specifying the objects of interest; External markers
input pixel is set to the foreground value. are those pixels we are confident to belong to the background
Erosion (Overlap-)1.If at least one pixel in the structuring The markers should be the local minima values; The further
element overlaps with a background pixel in the image away these pixels are from the markers, the higher its value.
underneath, the input pixel is set at the background value. 2.If Types of Edges: step edge: ideal edge
all pixels in the structuring element overlap with a foreground
pixel in the image underneath, the input pixel is left at the
foreground value.
opening (Erosion + Dilation) retaining the original object size;
clear an image of noise;may distort the shape size of the Superpixels create: Felzenszwalb's algorithm (a graph-based
object; Opening can remove small bright spots (i.e. “salt”) and approach)
connect small dark cracks. D(C1, C2) = true: Dif(C1, C2) > MInt(C1, C2) -> no NOT merge
closing (Dilation + Erosion): retaining the original object size; Else: Merge; MInt(C1, C2) = min( Int(C1) + t, Int(C2) + t);
fill holes in a region; Closing can remove small dark spots (i.e. Int(C) = max(inter distance)
“pepper”) and connect small light cracks
opening + closing remove both bright and dark artifacts of
noise.
grayscale dilation (Max) and grayscale erosion (Min)
White Top-hat: Original image minus its opening; Returns the
bright spots of the image that are smaller than the structuring
element
Black Top-hat: Closing minus the original image; Returns the
dark spots of the image that are smaller than the structuring Int(red) = 18 - 15 (or 15 – 12) = 3 (Note: there is no edge
element between 18 and 12.); Int(gray) = 29 – 23 = 6; T = 3; MInt(red,
gray) = min(3 + 3, 6 + 3) = 6; Dif(red, gray) = min(23 – 15, 26 -
Voordelen van het kopen van samenvattingen bij Stuvia op een rij:
Verzekerd van kwaliteit door reviews
Stuvia-klanten hebben meer dan 700.000 samenvattingen beoordeeld. Zo weet je zeker dat je de beste documenten koopt!
Snel en makkelijk kopen
Je betaalt supersnel en eenmalig met iDeal, creditcard of Stuvia-tegoed voor de samenvatting. Zonder lidmaatschap.
Focus op de essentie
Samenvattingen worden geschreven voor en door anderen. Daarom zijn de samenvattingen altijd betrouwbaar en actueel. Zo kom je snel tot de kern!
Veelgestelde vragen
Wat krijg ik als ik dit document koop?
Je krijgt een PDF, die direct beschikbaar is na je aankoop. Het gekochte document is altijd, overal en oneindig toegankelijk via je profiel.
Tevredenheidsgarantie: hoe werkt dat?
Onze tevredenheidsgarantie zorgt ervoor dat je altijd een studiedocument vindt dat goed bij je past. Je vult een formulier in en onze klantenservice regelt de rest.
Van wie koop ik deze samenvatting?
Stuvia is een marktplaats, je koop dit document dus niet van ons, maar van verkoper binli. Stuvia faciliteert de betaling aan de verkoper.
Zit ik meteen vast aan een abonnement?
Nee, je koopt alleen deze samenvatting voor €5,49. Je zit daarna nergens aan vast.
