3. IMAGE PROCESSING:-
• Form of signal processing
•Input – image
•Output –image or set of characteristics or parameters
•Implemented through algorithms
•Current concentration - “Texture", "Surface Mapping", "Video
Tracking”.
•Concentration needed on Spectrum & Hierarchy of
processing levels
4. IMAGE ENHANCEMENT:-
•Image Enhancement-Processing an image such as
Sharpening, De blurring,…
•Enhance Multispectral Color Composite Images-suitable
for image interpretation, de-correlation
•Enhance Color Images-Contrast enhancement, converts
image from RGB to L*a*b.
•Imadjust
•Histeq
•Adaphisteq
5. IMADJUST:-
• Increases the contrast of the image.
HISTEQ:-
• Perform histogram equalization.
ADAPTHISTEQ:-
• Performs contrast-limited adaptive
histogram equalization.
• Values are already spread out between
the minimum of 0 and maximum of 255.
6. SEGMENTATION:-
process of partitioning a digital image into
multiple segments
Steps Involved:-
•Detect Entire Cell
•Fills Gaps
•Dilate the Images
•Fill Interior Gaps
•Remove Connected Objects on Border
•Smooth the Object
7. BLUR:-
Usually makes the images unfocused.
BLUR DECONVOLUTION:-
Algorithm can be used effectively
when no information about the distortion.
8. EXPERIMENT STUDY:-
Can be processed using software such as
C language Matrix- X, Visual Basic, Java program and
MATLAB programming.
13. 2d 3d
Comparison histogram for
blur for 2d and 3d images:Comparison histogram for
segmentation for 2d and 3d
14. CONCLUSION:-
•The presentation briefly elaborates the image
process operations for 3D images.
•Previous works-in 2D images.
•Proposed work-3D images.
•The output Algorithms are compared.
•The software used is MATLAB
15. REFERENCES:-
[1] ABDUL HALIM BIN BABA. image processing learning tool-edge detection
bachelor degree. university of technology malaysia 1996.
[2] FIONN MURTAGH. Image Processing data analysis. the multi-scale approach.
University of Ulster.
[3] Fundamentals of image processing,
hany.farid@dartmouth.edu .(http://www.cs.dartmouth.edu/~farid)