Estimating Scene-Oriented Pseudo Depth With Pictorial Depth Cues

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Estimating depth information from a single image has recently attracted great attention in 3D-TV applications, such as 2D-to-3D conversion owing to an insufficient supply of 3-D contents. In this paper, we present a new framework for estimating depth from a single image via scene classification techniques. Our goal is to produce perceptually reasonable depth for human viewers; we refer to this as pesudo depth estimation. Since the human visual system highly relies on structural information and salient objects in understanding scenes, we propose a framework that combines two depth maps: initial pseudo depth map (PDM) and focus depth map. We use machine learning based scene classification to classify the image into one of two classes, namely, object-view and non-object-view. The initial PDM is estimated by segmenting salient objects (in the case of object-view) and by analyzing scene structures (in the case of non-object-view). The focus blur is locally measured to improve the initial PDM. Two depth maps are combined, and a simple filtering method is employed to generate the final PDM. Simulation results show that the proposed method outperforms other state-of-the-art approaches for depth estimation in 2D-to-3D conversion, both quantitatively and qualitatively. Furthermore, we discuss how the proposed method can effectively be extended to image sequences by employing depth propagation techniques.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2013-06
Language
English
Article Type
Article
Keywords

2D-TO-3D VIDEO CONVERSION; STEREOSCOPIC VIDEO; IMAGE GENERATION; EDGE INFORMATION; 2D; PROPAGATION; SEQUENCE; SHAPE

Citation

IEEE TRANSACTIONS ON BROADCASTING, v.59, no.2, pp.238 - 250

ISSN
0018-9316
DOI
10.1109/TBC.2013.2240131
URI
http://hdl.handle.net/10203/174351
Appears in Collection
EE-Journal Papers(저널논문)
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