Full text
|
PDF
- Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (1MB) | Preview |
Arciniegas Herrera, Jose Luis and Konrad, Janusz and Blanco Adán, Carlos Roberto del and García Santos, Narciso (2015). Learning-based depth estimation from 2D images using GIST and saliency. In: "IEEE International Conference on Image Processing (ICIP 2015)", 27/09/2015 - 30/09/2015, Quebec City, Canada. pp. 4753-4757.
Title: | Learning-based depth estimation from 2D images using GIST and saliency |
---|---|
Author/s: |
|
Item Type: | Presentation at Congress or Conference (Unspecified) |
Event Title: | IEEE International Conference on Image Processing (ICIP 2015) |
Event Dates: | 27/09/2015 - 30/09/2015 |
Event Location: | Quebec City, Canada |
Title of Book: | IEEE International Conference on Image Processing (ICIP 2015) |
Título de Revista/Publicación: | 2015 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) |
Date: | 2015 |
ISSN: | 1522-4880 |
Subjects: | |
Freetext Keywords: | 2D-to-3D Image Conversion, Depth maps, GIST Descriptor, Saliency |
Faculty: | E.T.S.I. Telecomunicación (UPM) |
Department: | Ingeniería de Sistemas Telemáticos |
Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
|
PDF
- Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (1MB) | Preview |
Although there has been a significant proliferation of 3D displays in the last decade, the availability of 3D content is still scant compared to the volume of 2D data. To fill this gap, automatic 2D to 3D conversion algorithms are needed. In this paper, we present an automatic approach, inspired by machine learning principles, for estimating the depth of a 2D image. The depth of a query image is inferred from a dataset of color and depth images by searching this repository for images that are photometrically similar to the query. We measure the photometric similarity between two images by comparing their GIST descriptors. Since not all regions in the query image require the same visual attention, we give more weight in the GIST-descriptor comparison to regions with high saliency. Subsequently, we fuse the depths of the most similar images and adaptively filter the result to obtain a depth estimate. Our experimental results indicate that the proposed algorithm outperforms other state-of-the-art approaches on the commonly-used Kinect-NYU dataset.
Type | Code | Acronym | Leader | Title |
---|---|---|---|---|
Government of Spain | TEC2010-20412 | Unspecified | Unspecified | Añadiendo percepción de profundidad a las comunicaciones visuales (enhanced 3dtv) |
Government of Spain | TEC2013-48453 | Unspecified | Unspecified | Mixed reality over ultra high definition television |
Item ID: | 41374 |
---|---|
DC Identifier: | http://oa.upm.es/41374/ |
OAI Identifier: | oai:oa.upm.es:41374 |
Official URL: | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7351709 |
Deposited by: | Memoria Investigacion |
Deposited on: | 06 Jul 2016 16:57 |
Last Modified: | 06 Jul 2016 16:57 |