Midlevel,Hough+morphologica,Segmentation,Otsu+cluster

Question 1

BIlateral Filter

Answer 1

• Very commonly used
• A spatial filtering!
  -Gaussian + Edge Preseve
  -Kernel depends on image content
  -Weight depends on distance and also Grey Intensity level

Question 2

Derivatives

Answer 2

1st Order
Large Values: On all the edge
Edge type: Single
Edge thickness: Thick
Noise Sensitivity:moderate

2nd order:
Start and end of edge
Double
Thin
High

They aplify noise

Question 3

First edge detector

Answer 3

Steps of a possible approach:
1. Low-pass filter for noise removal
2. Gradient calculation
3. Gradient thresholding |𝛻𝑓| > 𝑇

1-2: Smoothing (low pass) can be used prior calculation reduces noise effect
3-Selects only strongest edges

Question 4

Canny Edge Detector: Targets + Steps

Answer 4

Targets:
-Low error rate
-Edge points are well localized
-Single edge point response

Canny algorithm
1. Smoothing with a Gaussian filter
2. Gradient computation (magnitude and phase)
3. Quantize the gradient angles
4. Non-maxima suppression
5. Hysteresis thresholding

Question 5

Canny steps

Answer 5

1-Smoothing: Noise Reduction
2-Gradinet Computation: Edges are calculated using
vertical, horizontal and
diagonal masks
Edge direction is also
calculated
3-Edge quantization: Group orientations on bins of 45 degrees.
4-Non Maxima Suppression:
Reduces thickness
Gradient->1 edge
Accurate location of edge point

This process crosses an edge and selects the strongest point
5-Hysteresis thresholding: Keep strong edges
Keep week ones connected to strong edges
Reject isolated ones
Strength = Gradient Magnitude
Used thressholdin + masks

Small Sigma - finer details

Question 6

Resampling

Answer 6

Change Image dimension
Interpolation->enlarge
Decimation->minimize image

Question 7

Resampling Algorithms

Answer 7

1-Nearest neighbor interploation
Pixel color taken from closest pixel.
Fast but poor performance

2-Bilinear Interpolation
Weighted average of the closest 4 samples
Coefficients depend on the distance to the
samples
Balanced

3-Bicubic interpolation
Closest 16 samples
3degree polynomials
slow but better

Question 8

Hough

Answer 8

Works in parameter space
line->intersection

Few cells: Handle not perfectly aligned pixels.
Poor line localization

Many cells:
Require Precise Alignment
Accurate

Finding lines:
Compute fi/ro values, crossed cells ++

Question 9

Morphologica operators

Answer 9

a)Erosion
-thinning

b)Diltation
-thickening and merging

c)Opening
Erosion + diltation
-contur smoothing and removes protursions

d)Closing
Diltation + erosion
-fuse narrow breaks without increasing size

Question 10

Segmentation

Answer 10

Subdivide image in regions based on similarity and discontinuity

Question 11

Segmentation: Region Growing

Answer 11

Group pixels or subregions into larger regions
Based on the concept of connectivity
Makes use of predefined merging criteria
Starts from seed points, to be defined
– E.g., every pixel, pixels in a neighborhood
satisfying merging criteria
Stopping rule

Question 12

Segmentation: Watershed

Answer 12

Features:
a)Connected segmentation boundaries
b)Connected components

Topographic surface related to intensity

Goal of the algorithm: find
watershed lines where a point would drop in 2+ minimas

Algorithm
a)Fill Dam
-Flood suface from minima where each flooded region is a basin
-Prevend merging of water->build dams taller then others-> watershed lines (diltation)

Using it on gradient:
Useful when small gradients -> thick edges
Correspond to homogenous greylevel regions

Question 13

Watershed:oversegmentation

Answer 13

Direct application because of noise->oversegmentation.
Use markers to:
-drive segmentation
Internal:Object of interest
External:Background

Selection:
Preprocessing + criteria definition

Question 14

Segmentation By thressholding

Answer 14

Threshold selecting 2 resulting segments.
Right threshold is tricky:
-noisy images
-illumination changes
-small regions
-distance between peaks

Otsu makes this process automatic

Question 15

Segmentation:Otsu method

Answer 15

Global thresholding method based on histogram.

-Maximise interclass variance
-Minimize intraclass variance

Question 16

Otsu algorithm

Answer 16

1. Compute normalized histogram and set threshold
2. Compute probability to be below thresshold and above
3. Compute global mean (avvg image intensity) and cumulative mean up to k.
   4.Compute pixel mean intensity value in classes.
   5.Calculate global variance and inter class variance, quality is raport of them.

6.Find optimal threshold maximizin that raport

Smoothing helps it.

problem-noise and size

Question 17

Extending otsu

Answer 17

Can be combined with edge detection.
Can be generalized to nonglobal thresgolding and multicategories.

Edge detection:
compute histogram and threshold on the edge of image.
Apply it in original image

Question 18

Segmentation Clustering

Answer 18

each pixel a feature vector.

types:
1. Dvisive: from dataset to clusters.
2. Agglomerative: from pixels to clustrs

Question 19

Segmentation clustering kmeans

Answer 19

Init:
1.Forgy-random in entire dataset
2.Random partition: build k clusters randomly then compute centroids

• Pros
  – Light and simple
  – Computational complexity can be reduced using euristics
  – Fast convergence
• Cons
  – Optimality is not guaranteed
  – Solution found depends on initialization
  – The number of clusters, k, needs to be known in advance
  – Forces spherical symmetry of clusters (in the Ndimensional space)
• K-means clustering can be done based on
  – The histogram (AKA gray levels, faster)
  – Pixel vectors (better results, tunable)
• Possible distance measures
  – Intensity level difference (grayscale)
  – Color channel difference (color image, depends on
  color space)
  – Combinations of position, color, texture
  descriptor

Clusters
* Some segments shown – not necessarily connected
* Some clusters associated with objects
– Similar objects in the same cluster
* Some clusters are meaningless
* Problems with textured objects (e.g., the cabbage)