2nd Sem Graduation Project

Graduation Project
Image Reviving

Welcome
Prof. Sayed El- Harbete.
Dr. Ahmed Gawish.

Apology
For no Meetings:
No Clear Methodologies.
Midterm.
Feast.

Project Phases
Determining the geometric class of an image region through:-
Determining Super Pixels.
Segmentation(Constellations).
Labeling.
Classification:- determine each segment belong to which class(sky, vertical, ground).
Cutting and Folding.
Determining Camera Parameters.

Project Overview

Project Phases
Determining the geometric classes of an image region.
What is it?

Geometric classes for the image
Input Image
Vertical
Labels
Support
Sky
Center
Left
Right

Geometric classes for the image(Cont’)
How?
Across ( Color, Texture, Location in the image, shape, and projective geometry cues(using vanishing line)).
Steps :-
Determining Super Pixels.
Segmentation(Constellations).
Geometric Classification (using the classifier).
Classifier :- determine each segment belong to which class (ground, vertical (Left ,Right , Center) ,sky).

Project Phases(Cont’)
Cutting and Folding (Texture mapping).
Determining Camera Parameters and Determining the 3D model Coordinates.

Project Phases(Cont’)
10
Original Image
Object representation due to the 2D image
Objects representation due to 3D Computer Vision

Determining Super Pixels:-
Initially, an image is represented simply by a 2D array of RGB pixels. Without any knowledge of how those pixels should be grouped, we can compute only simple cues, such as pixel colors and filter responses. Our first step is to form super pixels from those raw pixel intensities. Super pixels correspond to small, nearly-uniform regions in the image and have been found useful by other computer vision and graphics researchers

Determining the geometric classes for the image (Cont’)
Segmentation(Forming the Constellations)
Constellations
Super pixels

Determining the geometric classes for the image (Cont’)
After the first phase we will be able to get the labeled image ( Ground ,
Vertical ,
Sky).
Labeled image

Segmentation Algo.
Start 80
______________________
Sigma = 20
______________________
Accept region
= [80-20 , 80+20]
= [60 , 100]
Queue
Start Pixel
9: 3  12
8: 1  6
8: 1014
8: 1  6
10 : 12
8: 1014
8: 1  6
10 :714
10 : 23
8: 1014
8: 1  6
Dequeue
(Old)
______________
Enqueue
(New)
Enqueue
(New)

Expected Result

Segmentation Review
12
6
21

Our Segm. Output

Challeges Result
Problem1: (edges between the segments)

Solution for the edges
Apply Smoothing Filters
Original
Smoothing 7

Challeges Result
Problem2: (Start Position)

Solution for the Start
Original
Put the fill color by Mean :
Problem
Mean
2nd Segment

Challeges Result
Problem3: (Small Region)

1st Solution for the small regions
1- Select Small region segment
2- Choose nearest Large region
3- Fill Small By Large
No Applicable

2nd Solution for the small regions
1- Smooth Original .
2- Put the fill Segment by Mean Color .
3- Start with small Sigma to Large :
Orginal
Smooth
Mean

Adv. From 3rd Problem
To recognize between the Textures:
Smooth Texture
coarse Texture

2nd Sem Graduation Project

by ChristinaSaweres on Mar 18, 2010

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2nd Sem Graduation Project — Presentation Transcript