4.2. DISCUSSION.
The number of pixels in the picture depends on the resolution
of used digital camera; small resolution corresponds to less number of pixels
and high resolution results in more pixels.
The outlook of pixels on detected pictures depends on the
weight of each picture. When a picture has small weight i.e. brightness and
contast5 are at low level, pixels are not well seen. When a picture is being
zoomed out its weight increase and pixels are well seen.
If the picture contains sharp edges and its surface is rather
smooth, its boundaries could be detected easily without getting any erroneous
pixels.
CHAPTER 5. CONCLUSION AND
RECOMMENDATION.
5.1. CONCLUSION.
As it was tried to express on the beginning of this paper, the
main purpose of this project was intended to implement edge detection for
digital image.
There are several algorithms that are able to detect edges.
Our implementation was based on two edge detection operators
which are Sobel and Prewitt operators, for determination of existence and
location of edge in picture.
The Sobel operator yields the best result containing many
pixels on the image. It is very quick to computer and rather simple to
implement.
The Prewitt operator yields the result containing fewer pixels
on the processed image.
Both operators pass two masks over the image separately to
obtain X and Y magnitudes of the gradient
5.2. RECOMMENDATION.
Based on the investigation and experiences, we can give the
following recommendations:
Fist one can extend this work by providing a smooth surface as
background for the determination of the existence and location of edges in a
picture.
Secondary, we would like to suggest usage of edge detection
operators to improve security.
Thirdly, we would like to suggest using other techniques
different of Sobel operator and Prewitt operators
Also we suggest a usage of digital camera containing high
resolution.
To KIST administration, the period of final year project
should be separated from the learning period.
KIST administration should emphasize on the reported final year
projects.
REFERENCES.
From the books:
[1]. Mathematical, Digital Image Processing. Powerful,
Fast image processing and analysis.
[2]. Kristian Sandberg, Introduction to image processing in
matlab 1. Department of applied mathematics, University of Colorado at
Boulder.
[3]. Rafael C. Gonzalez, Richard E.Woods Digital Image
Processing, Addison-Wesley Publishing Company, 1992.
[4]. Canny J. (1986), Computational approach to edge
detection, IEEE Trans. Pattern Analysis and machine
intelligence,p.679-714.
[5]. Ronald J. Tocci Neal S. widmer, Digital system,
Principles and application, Eight edition.
[6]. Bob, Fisher, Simon Perkins, Ashley Wolker and Eric Wolfart
((c)1994), Hypermedia image processing reference.
[7]. Ma (1989),»A novel image sequence coding
Schem», IEEE International conference on system engineering.
p.15-18.
[8]. Adrian BILAN, Moshe BREINER, Addison-Wesley(1995),
MatLab for engineers.
[9].
Digital
Photography Milestones from Kodak. Women in Photography
International. Retrieved on
2007-
09-17
From Internet:
[10]. http://www.cee.hw.ac.uk/hipr/html/gsmooth.html[accessed
21st July 2007] .
[11]. http://www.mathworks.com. [Accessed 17th
November 2007]
[12]. http:// en.wikipedia.org/wiki/image: partly disassembled
Lunix digital camera. [Accessed 14thseptember 2007].
[13]. http://www.pemag.com/encyclopedia [accessed 6th
June 2007].
[14]. http://www.freepatents on line.com/2003 0223615 [accessed
12th august 2007].
[15].
http://en.wikipedia.org/wiki/lumunous-intensity.[accessed
12th September 2007]
[16]. (http://w ww.generation5.org/content/2002/im01.asp)
[accessed 15th October 2007]
[17].«
http://en.wikipedia.org/wiki/edge
detection»
APPENDICES
APPENDICES.
|
SI photometry units
|
|
Quantity
|
Symbol
|
SI unit
|
Abbr.
|
Notes
|
|
Luminous energy
|
Qv
|
lumensecond
|
lm·s
|
units are sometimes called talbots
|
|
Luminous flux
|
F
|
lumen (= cd·sr)
|
lm
|
also called luminous power
|
|
Luminous intensity
|
Iv
|
candela (= lm/sr)
|
cd
|
an SI base unit
|
|
Luminance
|
Lv
|
candela per square metre
|
cd/m2
|
units are sometimes called nits
|
|
Illuminance
|
Ev
|
lux (= lm/m2)
|
lx
|
Used for light incident on a surface
|
|
Luminous emittance
|
Mv
|
lux (= lm/m2)
|
lx
|
Used for light emitted from a surface
|
|
Luminous efficacy
|
|
lumen per watt
|
lm/W
|
ratio of luminous flux to radiant flux; maximum possible is
683.002
|
Functions by
Category
The tables below list all functions in the
Image Processing Toolbox by category. The tables include a few functions in
MATLAB that are especially useful for image processing, such as imagesc,
immovie, and imwshow.
|
Image
Display
|
|
colorbar
|
Display colorbar. (This is a MATLAB
function. See the online MATLAB Function Reference for its reference page.)
|
|
getimage
|
Get image data from axes
|
|
image
|
Create and display image object. (This is a
MATLAB function. See the online MATLAB Function Reference for its reference
page.)
|
|
imagesc
|
Scale data and display as image. (This is a
MATLAB function. See the online MATLAB Function Reference for its reference
page.)
|
|
immovie
|
Make movie from multiframe indexed image
|
|
imshow
|
Display image
|
|
montage
|
Display multiple image frames as
rectangular montage
|
|
subimage
|
Display multiple images in single figure
|
|
truesize
|
Adjust display size of image
|
|
warp
|
Display image as texture-mapped surface
|
|
zoom
|
Zoom in and out of image or 2-D plot. (This
is a MATLAB function. See the online MATLAB Function Reference for its
reference page.)
|
|
Neighborhood
and Block Processing
|
|
bestblk
|
Choose block size for block processing
|
|
blkproc
|
Implement distinct block processing for
image
|
|
col2im
|
Rearrange matrix columns into blocks
|
|
colfilt
|
Perform neighborhood operations using
columnwise functions
|
|
im2col
|
Rearrange image blocks into columns
|
|
nlfilter
|
Perform general sliding-neighborhood
operations
|
|
Image
Analysis
|
|
edge
|
Find edges in intensity image
|
|
qtdecomp
|
Perform quadtree decomposition
|
|
qtgetblk
|
Get block values in quadtree
decomposition
|
|
qtsetblk
|
Set block values in quadtree
decomposition
|
|
Colormap Manipulation
|
|
brighten
|
Brighten or darken colormap. (This is a
MATLAB function. See the online MATLAB Function Reference for its reference
page.)
|
|
cmpermute
|
Rearrange colors in colormap
|
|
cmunique
|
Find unique colormap colors and
corresponding image
|
|
colormap
|
Set or get color lookup table. (This is a
MATLAB function. See the online MATLAB Function Reference for its reference
page.)
|
|
imapprox
|
Approximate indexed image by one with fewer
colors
|
|
rgbplot
|
Plot RGB colormap components. (This is a
MATLAB function. See the online MATLAB Function Reference for its reference
page.)
|
|
Morphological Operations (Binary Images)
|
|
applylut
|
Perform neighborhood operations using
lookup tables
|
|
bwarea
|
Area of objects in binary image
|
|
bwareaopen
|
Binary area open; remove small objects
|
|
bwdist
|
Distance transform
|
|
bweuler
|
Euler number of binary image
|
|
bwfill
|
Fill background regions in binary image
|
|
bwhitmiss
|
Binary hit-miss operation
|
|
bwlabel
|
Label connected components in 2-D binary
image
|
|
bwlabeln
|
Label connected components in N-D binary
image.
|
|
bwmorph
|
Perform morphological operations on binary
image
|
|
bwpack
|
Pack binary image
|
|
bwperim
|
Find perimeter of objects in binary image
|
|
bwselect
|
Select objects in binary image
|
|
bwulterode
|
Ultimate erosion
|
|
bwunpack
|
Unpack a packed binary image
|
|
imregionalmin
|
Regional minima of image
|
|
imtophat
|
Perform tophat filtering
|
|
makelut
|
Construct lookup table for use with
applylut
|
|
Structuring
Element (STREL) Creation and Manipulation
|
|
getheight
|
Get the height of a structuring element
|
|
getneighbors
|
Get structuring element neighbor locations
and heights
|
|
getnhood
|
Get structuring element neighborhood
|
|
getsequence
|
Extract sequence of decomposed structuring
elements
|
|
isflat
|
Return true for flat structuring element
|
|
reflect
|
Reflect structuring element
|
|
strel
|
Create morphological structuring element
|
|
translate
|
Translate structuring element
|
|
|
|
Pixel Values and Statistics
|
|
corr2
|
Compute 2-D correlation coefficient
|
|
imcontour
|
Create contour plot of image data
|
|
imfeature
|
Compute feature measurements for image
regions
|
|
imhist
|
Display histogram of image data
|
|
impixel
|
Determine pixel color values
|
|
improfile
|
Compute pixel-value cross-sections along
line segments
|
|
mean2
|
Compute mean of matrix elements
|
|
pixval
|
Display information about image pixels
|
|
regionprops
|
Measure properties of image regions
|
|
std2
|
Compute standard deviation of matrix
elements
|
|
Toolbox Preferences
|
|
iptgetpref
|
Get value of Image Processing Toolbox
preference
|
|
iptsetpref
|
Set value of Image Processing Toolbox
preference
|
| 
