Feature description Flashcards

1
Q

what are the four types of feature description

A

Statistical Features
Contour features
Moments
Texture features

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2
Q

what is feature extraction

A

generally, it is a process that is performed after segmentation, that allow to extract specific features from the image

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3
Q

what are the statistical features

A

are the features that are extracted from the distribution of labels in the image

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4
Q

what is the Normalized Difference Vegetation Index

A

we really NDVI index where we rely on the near-infra red-spactrum which is not visible to the human eye but very close to the infrared spectrum

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5
Q

what is the contour feature

A

allows providing information about the shape of the object

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6
Q

why we maynot want to use edge detector for the contour

A

usually for the contour features we more interest in a sequence, for this we may want to use chain code algorithm

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7
Q

explain the chain code algorithm

A

uses a certain pattern for the order of pixels ;
initially look for the first pixel in the object .
iteratively compute the direction using mod7(n+5)
until we reach the goal location

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8
Q

what are some exmaples of the contour use case

A

it allows the computation of the area and the centroid

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9
Q

what is a polygonal approximation

A

the idea consists of describing the object using shapes

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10
Q

How can we perform a polygonal approximation

A

for a given object. we initially draw the long line linking the two sides of the object, then we iteratively compute the maximum available error, to later link it and obtain a polygon each time

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11
Q

what are some shape descriptors

A

The form factor , compare the opbject perimeter with its circle perimeter
convexity: makes the relation between the convex perimeter and the original object perimeter
Aspect ration: the maximum and the minimum diameter

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12
Q

explain the Fourier contour description

A

The idea is the we take the distance to the centroid as the function of the angle , we would obtain a periodic function describing signal we can then use the Fourier transform

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13
Q

how t deal with different r(θ) in a shape

A

we can use the curvilinear coordinates to express the radius with respect to the coordinates

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14
Q

what is the fractal analysis

A

we performs some sort of measurement to compute the scale in terms of the length,to later on obtain the line and compute the corresponding angle winch reflect the fractal value

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15
Q

what is object moment

A

the moment equation describes the double summation on every axe for the pixel, know that
for p=0,q=0 it simply corresponds to the area of the shape
ΣxΣy x^q y^q f(x,y) to eliminate the influence of the shape position, the mean is subtracted
ΣxΣy (x-meanx)^q (y-mean_y)^q f(x,y)

then we introduce normalization to allow making it scale-invariant and translation invariant (normalized μ00 )
it is still not rotation invariant,
finally, we introduce the 7 Hu invariant to make rotation invariant

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16
Q

what are feature texture

A

consist of a way to measure the the object texture characterisitics

17
Q

how can we describe textures

A

we can use the coeccurence matrix to get propertyise about the texture this include contrast dissilarity and homogeneity

18
Q

what is a gabor filter used for

A

it is a linear filter that can be used to determine the texture through twisting its parameters them:
the parameters include theta for the orientation
sigma for the wave length
and gamma for the form factor

19
Q

explain the frcatal analysis

A

the idea is to analyse the image through a guassian filter where we plot the pixel value in terms of the sigma value , note that we may take only few points as a refference

20
Q

explain eccentricity

A

it is a moment feature which reflects the ratio between the focal length and the major axis length

21
Q

give an example of use case for the detectors

A

expert systems when we try to classify object based solely on their features