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Introduction

Image segmentation is a fundamental task in computer vision that involves partitioning an image into multiple segments to simplify the representation of an image. In recent years, with the advancement of deep learning techniques, image segmentation has seen significant improvements in accuracy and efficiency. One of the emerging applications of image segmentation is virtual try-on, where users can virtually try on clothes before making a purchase. This technology has the potential to revolutionize the fashion industry by providing a more personalized and engaging shopping experience for consumers.

Background of Study

The concept of virtual try-on has gained popularity in the fashion industry as it allows customers to visualize how a piece of clothing would look on them without physically trying it on. In order to achieve accurate and realistic virtual try-on experience, image segmentation plays a crucial role in separating the clothing item from the background and fitting it onto the user’s body in a convincing manner.

Problem Statement

Despite the advancements in image segmentation algorithms, there are still challenges in accurately segmenting clothing items in images with complex backgrounds, varying lighting conditions, and different clothing textures. Furthermore, there is a lack of comprehensive studies focusing on image segmentation for virtual try-on applications.

Objective of Study

The main objective of this thesis is to investigate state-of-the-art image segmentation techniques for virtual try-on applications and propose novel solutions to improve the accuracy and efficiency of clothing segmentation.

Limitations of Study

Due to the complexity of image segmentation for virtual try-on applications, this study may not address all possible challenges and variations in clothing segmentation. The study will focus on a specific subset of clothing items and may not generalize to all types of clothing.

Scope of Study

This study will focus on image segmentation techniques, specifically tailored for virtual try-on applications. The study will explore both traditional and deep learning-based segmentation methods to determine the most effective approach for accurate clothing segmentation.

Significance of Study

The findings of this study have the potential to enhance virtual try-on applications by improving the accuracy of clothing segmentation. This can lead to increased customer satisfaction, reduced return rates, and a more engaging shopping experience for consumers.

Structure of the Thesis

Chapter 1: Introduction
1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of Image Segmentation
2.2 Virtual Try-on Applications
2.3 Traditional Image Segmentation Techniques
2.4 Deep Learning-Based Image Segmentation
2.5 Clothing Segmentation in Virtual Try-on
2.6 Challenges in Clothing Segmentation
2.7 Evaluation Metrics for Image Segmentation
2.8 Existing Datasets for Clothing Segmentation
2.9 State-of-the-Art Image Segmentation Algorithms
2.10 Summary of Literature Review

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Preprocessing of Data
3.4 Implementation of Image Segmentation Algorithms
3.5 Evaluation Metrics
3.6 Experimental Setup
3.7 Performance Evaluation
3.8 Ethical Considerations
3.9 Limitations of Research Methodology

Chapter 4: Discussion of Findings
4.1 Comparison of Image Segmentation Algorithms
4.2 Evaluation of Clothing Segmentation Results
4.3 Analysis of Experimental Results
4.4 Discussion on Challenges and Limitations
4.5 Comparison with Existing Studies
4.6 Implications of Findings
4.7 Future Directions
4.8 Conclusion

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Implications for Virtual Try-on Applications
5.4 Recommendations for Future Research
5.5 Conclusion

Thesis Overview

Image segmentation plays a vital role in virtual try-on applications by accurately separating clothing items from the background and fitting them onto the user’s body. This thesis aims to investigate state-of-the-art image segmentation techniques for virtual try-on applications and propose novel solutions to enhance clothing segmentation accuracy. The study will focus on traditional and deep learning-based segmentation methods, evaluate their performance using various metrics, and discuss the challenges and limitations of clothing segmentation. The findings of this study have the potential to improve the virtual try-on experience, leading to increased customer satisfaction and engagement in the fashion industry.

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