SCENE CLASSIFICATION FOR DEPTH ASSIGNMENT = 깊이 정보를 부여하기 위한 이미지 분류

다국어 초록 (Multilingual Abstract)

Due to development of 3D display technology, industries related 3D have been grown. For this reason, the demand of 3D contents increases, but there is a short sup- ply of 3D contents. Consequently, research on 2D-to-3D conversion is underway. In 2D-to-3D conversion, the depth information of scene is obtained through an analysis of several depth cues on input sequence and the depth map corresponding to a scene can be generated by combining several depth cues and assigning an appropriate depth level. Scene classification for depth assignment is needed in this process. This paper classifies a scene into landscape, linear perspective, and normal type automatically. The proposed method analyzes landscape type and found there is a relation between image pattern and distribution of color and edge, and suggest the criteria for clas- sification. Moreover, the other criteria for linear perspective classification based on vanishing point detection is proposed. To verify performance, the proposed features are fed into a linear SVM classifier, and 651 images are used. Experiment results show that the algorithm has an advantages in performance by about 13%.

목차 (Table of Contents)

• Chapter 1 Introduction 1
• Chapter 2 Scene Classification for depth assignment 3
• Chapter 3 Feature Extraction 8
• 3.1 Features for landscape classification 8
• 3.1.1 Image partition 8
• 3.1.2 Color-related features 9
• 3.1.3 Edge-related features 11
• 3.2 Features for linear perspective classification 14
• 3.2.1 Criterion for classification of linear perspective type scene 14
• 3.2.2 Vanishing point detection 14
• 3.2.3 Features based on vanishing point detection 17
• Chapter 4 Experiment results 20
• 4.1 Performance of classification on each step 20
• 4.2 Performance of scene classification result for depth assignment 24
• Chapter 5 Conclusion 27
• Bibliography 28
• 국문 초록 31