Review on 2D-to-3D Image and Video Conversion Methods

Patil, Shweta; Charles, Priya

doi:10.1109/iccubea.2015.192

Cited by 4 publications

(2 citation statements)

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“…For this purpose, many advanced depth extraction algorithms have been proposed that estimate the depth structure from defocus, motion, or shading. The main problem of these methods is that they cannot be universally applied to all the images that compose a movie (or generic video content) [1]. For example, structure from motion algorithms need that the camera is in motion.…”

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confidence: 99%

Automatic Depth Extraction from 2D Images Using a Cluster-Based Learning Framework

Herrera

del-Blanco

García

2018

IEEE Trans. on Image Process.

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There has been a significant increase in the availability of 3D players and displays in the last years. Nonetheless, the amount of 3D content has not experimented an increment of such magnitude. To alleviate this problem, many algorithms for converting images and videos from 2D to 3D have been proposed. Here, we present an automatic learning-based 2D-3D image conversion approach, based on the key hypothesis that color images with similar structure likely present a similar depth structure. The presented algorithm estimates the depth of a color query image using the prior knowledge provided by a repository of color + depth images. The algorithm clusters this database attending to their structural similarity, and then creates a representative of each color-depth image cluster that will be used as prior depth map. The selection of the appropriate prior depth map corresponding to one given color query image is accomplished by comparing the structural similarity in the color domain between the query image and the database. The comparison is based on a K-Nearest Neighbor framework that uses a learning procedure to build an adaptive combination of image feature descriptors. The best correspondences determine the cluster, and in turn the associated prior depth map. Finally, this prior estimation is enhanced through a segmentation-guided filtering that obtains the final depth map estimation. This approach has been tested using two publicly available databases, and compared with several state-of-the-art algorithms in order to prove its efficiency.

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confidence: 99%

Automatic Depth Extraction from 2D Images Using a Cluster-Based Learning Framework

Herrera

del-Blanco

García

2018

IEEE Trans. on Image Process.

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“…1.1. the 2D-to-3D conversion task for both images and video consists in two main steps [10]. In the first one, given a query image or video, it is generated an estimated depth map using image or video analysis techniques.…”

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confidence: 99%

2D to 3D image and video conversion using machine learning

Conejero¹

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Despite the concept of three-dimensional (3D) image and video was introduced many years ago, there has been recently a significant increase in the number of available 3D displays and players in the last years. Nevertheless, the amount of 3D content has not increased in the same magnitude, creating a gap between 3D offer and demand. To reduce this difference, many algorithms have appeared that perform 2D-to-3D image and video conversion. While many of this techniques usually required several images from the same scene to perform the conversion, the most recent family of these techniques, which corresponds to machine learningbased algorithms, is not restricted by this limitation computing the 3D image with a single view of the scene. Machine learning-based methods require the use of databases of 2D and 3D images to learn how to perform this conversion. Since the number of available datasets has recently increased significantly, these algorithms have become very popular. However, the quality achieved by the current 2D to 3D conversion techniques is far to be fully satisfying and they need to be improved in order be used for producing good quality 3D content. This thesis proposes two systems for 2D-to-3D conversion, one for image and another one for video, belonging to the machine learning family of methods. With respect to the image conversion system, a new approach is proposed that makes the algorithm more robust and adaptive to different types of scenarios by using a combination of feature descriptors. At the same time, the proposed clustering of the dataset makes the solution faster and more efficient to deal with large datasets. The system also learns how to automatically adapt the value of the different parameters involved in the conversion, resulting in a fully automatic solution.With respect to video conversion, the learning based approach for images is extended to video sequences. The algorithm is divided in three main parts. In the first one, a depth estimation of the background is computed using the previous presented approach. Then, the foreground is segmented analyzing the optical flow to manage the different objects individually.Finally, the depth estimation of the background is combined with the foreground information and filtered to obtain the final depth estimation.The developed algorithms have been tested with different publicly available datasets of 3D images and video sequences.

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Warping Techniques in Video Stabilization

Favorskaya

Buryachenko

2017

Intelligent Systems Reference Library

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Review on 2D-to-3D Image and Video Conversion Methods

Cited by 4 publications

References 18 publications

Automatic Depth Extraction from 2D Images Using a Cluster-Based Learning Framework

Automatic Depth Extraction from 2D Images Using a Cluster-Based Learning Framework

2D to 3D image and video conversion using machine learning

Warping Techniques in Video Stabilization

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