image segmentation and defect detection techniques using homogeneity

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Page 1: Image segmentation and defect detection techniques using homogeneity

IMAGE SEGMENTATION AND DEFECT DETECTION TECHNIQUES USING HOMOGENEITY

黃偉鑫

Page 2: Image segmentation and defect detection techniques using homogeneity

Outline Abstract Local homogeneity analysis and H-image Homogeneity for image segmentation Homogeneity for image defect detection Conclusion & Experiment result

Page 3: Image segmentation and defect detection techniques using homogeneity

Abstract survey on latest image segmentation and defect

detection techniques using local homogeneity analysis

image segmentation suffer from the problem local homogeneity based approaches for image

segmentation and image defect detectionregion merging Gabor filteringdiscrete cosine transform color features wavelet transform watershed algorithmFFD method Information Fusion

Page 4: Image segmentation and defect detection techniques using homogeneity

Local homogeneity analysis Criterion definition

Let P be the square window of width 2 N + 1

f be the sum of all the vectors defined in P

Page 5: Image segmentation and defect detection techniques using homogeneity

Local homogeneity analysis H value

Page 6: Image segmentation and defect detection techniques using homogeneity

Local homogeneity analysis how to determine the size of the local window

large windows are useful for detecting texture boundaries

small windows are useful in localizing the intensity edges

H-image gray-scale image

Page 7: Image segmentation and defect detection techniques using homogeneity

image segmentationHomogeneity With Region Merging

avoid the over segmentation faced the problem of selecting appropriate local

scale for determining the most appropriate local window sizeSeeded Region

Determination

Region Growing

Region Merging

Page 8: Image segmentation and defect detection techniques using homogeneity

image segmentationHomogeneity With Region Merging

Seeded Region Determination Calculate the average and the standard deviation of the H-

values in the H-imageLet them be and For each pixel p in H-image, compute the average and the standard deviation of pixels in the local window set a threshold Tp in p

If the H-value of p is less than T, p is considered as a candidate seeded region point (CSRP)

Page 9: Image segmentation and defect detection techniques using homogeneity

image segmentationHomogeneity With Region Merging

Region Growing average the H values of all the points that do not belong to any

seeded region connect pixels below the average assigned to their neighboring seeded regions

Region Merging For color images, the regions are merged based on their color

similarity For gray-scale images, although the merging can be based on

texture features The merging process continues until a maximum threshold for

the distance is reached

Page 10: Image segmentation and defect detection techniques using homogeneity

image segmentationHomogeneity with Information Fusion

a novel approach to natural scene segmentation

uses both color and texture features

Page 11: Image segmentation and defect detection techniques using homogeneity

image segmentationHomogeneity with Information Fusion

Feature Extraction suitable for inputting into the SOM for training and

subsequent classification each pixel is represented as a seven dimensional

vector {r, g, b, e5l5, e5s5, r5r5, l5s5} R, G, B values and Laws’ texture energy measures

Page 12: Image segmentation and defect detection techniques using homogeneity

image segmentationHomogeneity with Information Fusion

Selection of Training Samples non-homogeneity measureIn order to be used in color image, for the pixel location (i , j)

The average non-homogeneity value is calculated for the entire image

Local average non-homogeneity value for each block

Page 13: Image segmentation and defect detection techniques using homogeneity

image segmentationHomogeneity with Information Fusion

HSOM Training and Testing hierarchical two-stage self-organizing mapping

Region Merging output of the HSOM is often an over-segmented

image converted into the CIE color space

Page 14: Image segmentation and defect detection techniques using homogeneity

image segmentationHomogeneity With Color Features

This approach for color image segmentation which uses local homogeneity analysis and color features

modified the region merging approach

Page 15: Image segmentation and defect detection techniques using homogeneity

image segmentationHomogeneity With Color Features

Self-Constructing Fuzzy Clusteringeach pixel is associated with a color signal (x1, x2, x3) where x1, x2, and x3 denote the R, G, and B valuesCluster Cj is represented by (G1j,G2j,G3j) whereG1j , G2j , andG3j are Gaussian membership functions of R, G, and BEach Gij ,1 ≤ i ≤ 3, has the center mij and standard deviation σij

we create a new cluster Ck, k = J + 1, withmik = xi, σik = σ0

where 0 ≤ ρ1 ≤ 1 is a predefined thresholdd(x1, x2, x3;Cj) ≥ ρ1

Otherwise, let cluster Ct be the cluster with the largest value of similarity d

Page 16: Image segmentation and defect detection techniques using homogeneity

image segmentationHomogeneity With Color Features

Color-Based and H-based Region Growing A color-based region is formed by connecting a

set of pixels with the same color according to the spatial relationship of eight-connectivity

we find out those pixels with high local homogeneity by H-value calculation, group them into several seeded regions, and obtain a seeded region image

Page 17: Image segmentation and defect detection techniques using homogeneity

image segmentationHomogeneity With Color Features

Region-Based Region Growing we grow the regions based on the color-based

regions which are labeled as non-seeded calculate the Eucliden distance of colors between

each non-seeded color-based region and each seeded region we combine the non-seeded region with the

lowest distance into the corresponding seeded region and update the color and size of the seeded region

Page 18: Image segmentation and defect detection techniques using homogeneity

image segmentationHomogeneity With Color Features

Merging merge regions by color similarities

the pair of neighboring regions with the smallest dissimilarity are merged together to form a new region if the dissimilarity is smaller than a threshold

Combine the smallest region until all sizes of regions are bigger than a

threshold

Page 19: Image segmentation and defect detection techniques using homogeneity

image segmentationHomogeneity With Watershed

Transformation

watershed algorithm is that it produces many segments which make it difficult to merge the segments correctly

region merging cost plays an important role in the region merging process

Homogenei

ty

Watershed

segmentation

Region Mergin

g

Page 20: Image segmentation and defect detection techniques using homogeneity

image segmentationHomogeneity With Watershed

Transformation Region MergingRegion adjacency graph (RAG), G = (R,E,C) R denotes the sets of nodes(regions)E denotes the sets of edgesC denotes the sets of merging cost ofthe arbitrary pair of adjacent regions

For a pair of nodes rj and ri

Page 21: Image segmentation and defect detection techniques using homogeneity

image segmentationHomogeneity With Watershed

TransformationThe color homogeneity

The circle-like degree homogeneity

The rectangle-like degree homogeneity

l is the perimeter of the region

hcom represents the cluster degree of the pixels in the region

b is the perimeter of region bounding boxhsth represents the smoothness degree of the region boundary

The weights wcolor , wshape , wcom and wsth are the inputparameters

c the standard deviation within region of channel cThis value indicates the similar degree of the two adjacent regions

Page 22: Image segmentation and defect detection techniques using homogeneity

image segmentationHomogeneity With Watershed

Transformation

Page 23: Image segmentation and defect detection techniques using homogeneity

image segmentationHomogeneity With Gabor Filtering

Gabor filtering is a wavelet based method to extract texture features of a multispectral image

The approach worked very well for many multispectral images

GABOR FILTERING

FCM algorithm

Local homogene

ity analysis

Page 24: Image segmentation and defect detection techniques using homogeneity

image segmentationHomogeneity With Gabor Filtering

The Fuzzy C-means (FCM) clustering algorithm1.Suppose x=(x1, x2,…,xj....,xN) the pixels of the image to be partitioned. We choose the number of cluster, the fuzzification parameter m and the stopping condition ξ2.Initialize u using random value in range [0,1]3.Set the loop counter b =04.Calculate the cluster centre vi :

5.Update uij as a function of distance to clusters:

6.If Max{u(b)-u(b+1)}<ξ then stop, otherwise, set b=b+1and go to step 4

Page 25: Image segmentation and defect detection techniques using homogeneity

image segmentationHomogeneity With Gabor Filtering

Page 26: Image segmentation and defect detection techniques using homogeneity

Image defect detectionHomogeneity With FFD Model

used homogeneity and free form deformation model (FFD) for the attrition detection in banknotes

defect localization

Page 27: Image segmentation and defect detection techniques using homogeneity

Image defect detection Homogeneity with Discrete Cosine

TransformThese five energy measures are EH, EV, ED, EM and ES which are horizontal, vertical, diagonal, average and the standard deviation of energy value of a DCT block respectively

Page 28: Image segmentation and defect detection techniques using homogeneity

Image defect detection Homogeneity with Wavelet Transform

a new defect detection algorithm based on local homogeneity and hotteling model to localize defects in various textures images

Page 29: Image segmentation and defect detection techniques using homogeneity

Conclusion & Experiment result

optimal image segmentation for each images select local window size still rely experiment

result noise can also be treated as defect

Page 30: Image segmentation and defect detection techniques using homogeneity

Q&A