ACKNOWLEDGEMENT

I am grateful to GOD Almighty for giving me the courage and strength

to complete my seminar successfully. I am thankful to our beloved principal

Prof. Shahir V K and our respected Head of the Department of Computer

Science and Engineering Mr. Gireesh T K, for their parental guidance and

support.

I would like to thank our seminar co-ordinators Ms. Janitha Krishnan

and Ms. Greeshma K for giving me innovative suggestions and assisting in

times of need. I gratefully acknowledge the excellent and incessant help given

by our faculty and my guide Mohammed Jaseem N, Assistant Professor,

Department of Computer Science & Engineering, to incite the work. I am

thankful for valuable guidance and enduring encouragement throughout this

study.

I also remember with thanks the timely help and constant

encouragements induced by other faculties of AWH Engineering College, my

friends and parents. I express my sense of gratitude to Department of Computer

Science & Engineering, AWH Engineering College, for providing me with

facilities to complete my work. 

DEEPA JOHNYDEEPA JOHNY

ABSTRACT

The first scheme “Model Of Saliency-Based Visual Attention” presents a

visual attention system, inspired by the behavior and the neuronal architecture

of the early primate visual system, is presented. Multiscale image features are

combined into a single topographical saliency map. A dynamical neural

network then selects attended locations in order of decreasing saliency. The

system breaks down the complex problem of scene understanding by rapidly

selecting, in a computationally efficient manner, conspicuous locations to be

analyzed in detail. The second scheme “Bilayer Segmentation Of Webcam

Videos” presents an automatic segmentation algorithm for video frames

captured by a (monocular) webcam that closely approximates depth

segmentation from a stereo camera. The frames are segmented into foreground

and background layers that comprise a subject (participant) and other objects

and individuals. The algorithm produces correct segmentations even in the

presence of large background motion with a nearly stationary foreground. The

last scheme “Exploring Visual And Motion Saliency For Automatic Video

Object Extraction” presents a saliency-based video object extraction (VOE)

framework. The framework aims to automatically extract foreground objects of

interest without any user interaction or the use of any training data (i.e., not

limited to any particular type of object).

CONTENTS

1. INTRODUCTION 1

2. LITERATURE SURVEY 10

2.1 MODEL OF SALIENCY – BASED VISUAL ATTENTION

SYSTEM 12

2.1.1 Extraction of Early Visual Features 14

2.1.2 The Saliency Map 15

2.2 BILAYER SEGMENTATION OF WEBCAM VIDEOS 18

2.2.1 Notation 20

2.2.2 Motons 20

2.2.3 Shape Filters 22

2.2.4 The Tree-Cube Taxonomy 23

2.2.5 Random Forests Vs Booster Of Trees Vs Ensemble Of

Boosters 25

2.2.6 Layer Segmentation 27

3. EXPLORING VISUAL AND MOTION SALIENCY FOR

AUTOMATIC VIDEO OBJECT EXTRACTION 28

3.1 AUTOMATIC OBJECT MODELING AND EXTRACTION 29

3.1.1 Determination of Visual Saliency 29

3.1.2 Extraction of Motion-Induced Cues 30

3.2 CONDITION RANDOM FIELD FOR VOE 33

3.2.1 Feature Fusion via CRF 34

3.2.2 Preserving Spatio-Temporal Consistency 35

4. COMPARISON 38

5. CONCLUSION 41

REFERENCES 42

GLOSSARY 43

LIST OF FIGURES

2.1 Motons 21

2.2 Shape Filters 22

2.3 The tree-cube taxonomy 25

LIST OF ABBREVIATIONS

1. FOA : Focus Of Attention

2. SM : Saliency Map

3. WTA : Winner-take-all neural network

4. DoG : Derivatives of Gaussian

5. EM : Expectation Maximization

6. LLR : log likelihood ratio

7. ARC : Adaptive reweighting and combining

8. RF : Random Forests

9. BT : Booster of Trees

10.EB : Ensemble of Boosters

11.GB : Gentle Boost

12.CRF : Conditional random field

13.EBT : Ensemble of Booster Trees

14.VOE : Video Object Extraction

15.HOG : Histogram of object gradients

16.GMM : Gaussian mixture models