sample thesis defense ppt

7/24/2019 sample thesis defense ppt

1/26

Low Resolution Image

Enhancement

Aquino, Juan Miguel A.

Arcelo, Christian


2/26

Objective of the study

Main objective: enhance still images taken from standard lo resolution

la!to! cameras.

"ub objectives:

#esearch and find noise filtering technique hich can be used to com!are andsubstitute to $uassian %limination

&ind and a!!ly an image illumination correction method hich can be used to

com!are and substitute to Multi'"cale #etine( Algorithm

find and a!!ly best !arameter to reconstruct the lo resolution image

find a method hich can be used to visually and mathematically com!are the lo

resolution image to the !rocessed image


3/26

"co!e and )imitations

*+(+- lo resolution la!to! camera

+- unique lo resolution images as taken.

"tanding or sitting !osition and facing the camera, smiling or neutral

emotion. "ubjects face must be visible ithout obstruction such as

hat, scarf, hood or any face garments.

directly facing the camera and only one !erson can be !rocessed in a

given time

%(cluding !hotos taken outdoors and ith bad eather conditions

such as heavy rain or foggy environment. /oo much sunlight e(!osure


4/26

Methodology

0ata $athering

1hat2s our data to su!!ort our study3 &ili!ino face images of +- individuals

1hat tool did e used to get these face images3 built'in la!to! ebcam of )enovo y45 ith a resolution of *+(+- at .6 mega!i(els


5/26

Methodology

0ata $athering &lo : "etu!

)a!to! ith built'in ebcam is !laced on a table

&or sitting !osition a chair is !rovided

1hite background

7eutral'lighted room


6/26

Methodology

0ata $athering &lo : 8rocedure

"ubject as instructed to sit don on the

chair

"ubject as guided to !osition face and look

in front of the eb camera 6+ inches aay

from it

"ubject as requested to follo the rules on

taking !hoto of him9her


7/26

Methodology

$eneral 8rocess "te! 6: %(!erimental Algorithm /esting;

7oise #eduction

est est "u!er #esulotion ?@'+4@'64@;


8/26

Adding Gaussian Noise


9/26

Noise Reduction Testing (Fuzzy Filter,Guassian with Edge Preservation,Median Filter)


10/26

ME and PNR testing


11/26

Methodology

"te! 6: 8re!rocessing 7ormali=ation Cro!!ing and resi=ing;

&our face !oints that ere used in cro!!ing the image:

/o! of the hair, left ear, right ear, ti! of the chin

After cro!!ing, resolution of the images as converted to 5(6+5 !i(els


12/26

Methodology

"te! 6: 8re!rocessing 7oise #eduction &u==y &ilter;

#$> values of the image ill undergo &u==y logic rules


13/26

Methodology

"te! 6: 8re!rocessing 7oise #eduction; %ach !i(el is com!osed of #$> values +-,B,6B;

Assign a membershi! value for each color !i(el &or vertical a(is:

,B4D E : 7on noisy

B?,644D E .6'6: 8robably noisy

64?,+44D E 6: 7oisy !i(el

&or Fori=ontal a(is: 0ivide the !i(el value by +4?

Classify hether dark medium or light

&u==y #ules


14/26

Methodology

"te! 6: 8re!rocessing rightness adjustment increase the total contrast of the image


15/26

Methodology

"te! +: Main 8rocess 8hase 6 E 8i(el #atio %valuation

8hase + E Actual #esults and /esting

Image Database

Phase 1

Parameter Testing Set

+ lo resolution in!uts ill be used for the 4@ !i(el

ratio

+ lo resolution images ill be included in the face

database for the 6@ and -@ !i(el ratio

Phase 2

Main Process Set

+ lo resolution in!uts ill be used for the 4@ !i(el

ratio 0ifferent from the !revious set;

+ lo resolution images ill be included in the face

database for the 6@ and -@ !i(el ratio


16/26

Methodology


!"#T$!%(M)& !(M) & !'(M)

"


17/26

Methodology



18/26

Methodology


GOH/6IG


19/26

Methodology


M"% and 8"7# Calculation

&acial &eature %(traction


20/26

Methodology"te! +: Main 8rocess

8hase 6 E #esults

Pixel Ratio (M) Unprocesse

d Image

Processed

Image

MSE

PSNR

Feature

Extraction

Input = 5!

Similar Images =

"!#ig$est Similar

= %!

*+

.*...

Input = 5!

Similar Images =

&!

#ig$est Similar

= '!

./0*

.1*//1

Input = 5!

Similar Images =

'!

#ig$est Similar

= &!

./23*+

./*+.0


21/26


8hase 6 E "ummari=ed #esults

8arameter 4'6'- yielded highest overall detection rate getting 54@

mouth detection

)oest M"%

Fighest 8"7#Pixel Ratio (M)

E*e

R

E*e

Nose Mout$ +,e

MSE

+,e

PSNRInput = 5!

Similar Images =

"!

#ig$est Similar =

%!

.++

4

.++4 +4 314 .22.* .*31

Input = 5!

Similar Images =

&!

#ig$est Similar =

'!

.++

4

.++4 314 14 .0*+1 .1*1.

Input = 5!

Similar Images =

'!

#ig$est Similar =

&!

.++

4

.++4 +4 14 .+*02 .*.


22/26


8hase + E Actual #esults and /esting GOH/6IG).4; Gs.6; GF.-;

8erformed on another + images


23/26


8hase + E Actual #esults and /esting &acial &eature %(traction Algorithm


24/26


8hase + E Actual #esults and /esting #esults "ummary

.$ole Face e/t E*e Rig$t E*e Nose Mout$

Unprocesse

d

14

(. out o5 2+

i6ages)

+4

(.3 out o5 2+

i6ages)

14

(.1 out o5 2+

i6ages)

14

(. out o5

2+ i6ages)

.14

( out o5 2+

i6ages)

Processed .++4

(2+ out o5 2+

i6ages)

.++4

(2+ out o5 2+

i6ages)

.++4

(2+ out o5 2+

i6ages)

.++4

(2+ out o5

2+ i6ages)

2+4

(0 out o5 2+

i6ages)

Impro,eme

nt

1*2/4 ..*..4 *4 1*2/4 *4


25/26

Conclusion

#esults shoed that the combination of the algorithms significantly

im!roved the quality of face images taken from a lo resolution

camera by getting !ositive im!rovement on facial feature detection

4.+?@ facial e(traction,

66.66@ left eye **.**@ right eye

4.+?@ nose

**.**@ mouth


26/26

#ecommendation A larger face database may !rove to be useful for better image

reconstruction etter data gathering setu! for face database considering subjects

orientation and !ositioning

sample thesis defense ppt

Documents

Transcript of sample thesis defense ppt