Date post: | 16-Jul-2015 |
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Science |
Upload: | gmidhubala |
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Introduction Image processing is any form of signal processing for
which the input is an image, such as a photography or videoframe.
The output of image processing may be either an image or aset of characteristics related to the image.
Image Analysis - to extract high level information on animage.
Image Segmentation - to change the representation of anoriginal image into meaningful portions which makes iteasier to analysis.
To locate objects and boundaries.
Segmentation Techniques
•Thresholding
•Edge Detection
•Color Image Segmentation
•Histogram Based Method
File formats
JPG uses lossy compression
GIF always uses lossless LZW compression, but it is always an
indexed color file (8-bits, 256 colors maximum), which is poor for
24-bit color photos.
PNG is transparency for 24 bit RGB images. lossless
compression, of different types), but PNG is perhaps slightly slower
to read or write.
TIF is lossless (including LZW compression option), which is
considered the highest quality format for commercial work.
Thresholding
Original image into binary image
Foreground can be separated from the background by selecting the
threshold value
Global Thresholding -only one threshold value for entire image
Local Thresholding - different value for different regions
Methods
Edge Based - detects and links edge pixels to form contour.
Region Based - detects the entire region
Edge Detection
Reduce the amount of data in an image.
Provides ability to extract the exact edge.
Corners, lines, curves .
Meaningful discontinuities in the grey level.
Edge detected image
Canny Edge Detection:(Criteria)
Detection: The probability of detecting real edge
points is maximized and falsely detecting non-edge
points is minimized. This corresponds to maximizing the
signal-to-noise ratio.
Localization: The detected edges should be as close as
possible to the real edges.
Number of Responses: One real edge should be result
in more than one detected edge.
Canny Edge Detection Algorithm
Smoothing:
Blurring of the image to remove noise.
Finding gradients:
The edges should be marked where the gradients of the
image has large magnitudes.
Non-maximum suppression:
Only local maxima should be marked as edges.
Double thresholding :
Potential edges are determines by thresholding.
Edge tracking by hysteresis:
Final edges are determined by suppressing all edges that are
not connected to a very certain strong edges.
Color Image Segmentation:Color image segmentation is used to extract high level
information of the image based on color. Three phases are
Phase1: Preprocessing:
Morphological methods are applied to remove the noises away
from image which applied to smooth some spots on uniformed
patterns.
Phase2: Transformation:
Color space transformed methods are used to transform other
color space to RGB.
Phase3: Segmentation:
Applying clustering algorithm like K-means algorithm for
finding the appropriate cluster numbers and segment images in
different color spaces. The cluster with the maximum average
variance is split into new clusters.
Segmented image
Histogram- based methods: Compute- Pixels , peaks, valleys
Locate – clusters
Recursively applied for finding the smaller clusters.
Distinguishes the two homogeneous regions of the
foreground and background of an image.
Histogram
Conclusion
Partitioning an Image using segmentation
techniques leads to extract different regions with
similar attributes . It also detects high level
information of an image for image analysis and
further researches.