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COLOR TEXTURE SEGMENTATION USING FEATURE DISTRIBUTIONS. Hexagonal-HSV. Cellular decomposition. ( S,H)={ (0.665, 49.1), (0.665, 169.1), (0.665, 229.1), (0.665,289.1), and (0.665, 349.1) }. Cellular decomposition. V component is divided into 3 parts - PowerPoint PPT Presentation
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COLOR TEXTURE SEGMENTATION USING
FEATURE DISTRIBUTIONS
Hexagonal-HSV
Cellular decomposition
(S,H)={ (0.665, 49.1), (0.665, 169.1), (0.665, 229.1), (0.665,289.1), and (0.665, 349.1) }
Cellular decomposition
V component is divided into 3 parts
with three interval s (0, 0.45), (0.45, 0.75) and (0.75, 1).
Thus, the 7 (S, H) pairs and the 3 V values can be combined into 21 initial colors.
Cluster
* The Euclidean distances between each pixel and the 21 respective initial color centers are then computed.
* Each pixel can then be assigned to one of the 21 initial
color clusters whose center has the smallest color distance to the pixel.
* The cluster with the largest error is then chosen to perform further splitting
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This process can be iterated level by level.
The repetitive splitting will be stopped when the splitting level reaches 4 or the number of used colors is more than 192.
Quantized image
Cluster
*the nearest pair of color clusters will be merged until the distance between any two colors are greater than 26.
*PSNR value of the quantizatzed image is larger than 30
峰值訊號雜訊比 (peak signal to noise ratio: PSNR)
dBMSE
PSNR )255
(log102
10
*merged until the number of used colors reaches 50
DEFINITION FEATURE
Color histogram : distribution of color in a texture
Local_edge_pattern histogram : distribution fo local edge patterns in a texture region
Color histogram hc
the color histogram hc for a texture region R using the following equation.
where n is the number of pixels with color label i, N is the total number of pixels of the texture region.
Local edge pattern descriptors
Local edge pattern (LEP)
he for a texture region R can be derived using the
following equation
where n is the number of pixels with LEP value i, N is the total number of pixels ofthe texture region.
Homogeneity Measure(color)
the more the Hc value approaches to 1
the more alike the color histograms hc and hc
Homogeneity Measure(texture)
the more the He value approaches to 1
the more alike the color histograms he and h e
Homogeneity Measure
Segmentation method
Hierarchical splitting
a block is split to four subblocks,six pairwise H distance as specified in Eq. (6) between color and LEP histograms of the four subblocks.
choose a small value
The threshold X was experimentally set to value 1.1.
Agglomerative merging
p is the number of pixels in the smaller of the two regions and H is the histogram distance measure as specified in Eq.(7)
respective color and LEP histograms are summed to the newimage region
Agglomerative merging
MImin is the smallest merger importance value of all preceding mergers, MImax is the largest merger importance value of all preceding
mergers MIcur is the merger importance for the current best merge.
Pixelwise classification
discrete disk with radius r and compute the corresponding color and LEP histograms over the disc
v is the number of pixels belonging to the ith region and in the 4-neighboors of the examined point.
Thank you~!!