Next Article in Journal
Laterally Loaded Single Pile Response Considering the Influence of Suction and Non-Linear Behaviour of Reinforced Concrete Sections
Previous Article in Journal
Wearable Plasma Pads for Biomedical Applications
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Appl. Sci. 2017, 7(12), 1309; https://doi.org/10.3390/app7121309

Fast Object Detection in Light Field Imaging by Integrating Deep Learning with Defocusing

State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
*
Author to whom correspondence should be addressed.
Received: 12 October 2017 / Revised: 8 December 2017 / Accepted: 8 December 2017 / Published: 17 December 2017
(This article belongs to the Section Computer Science and Electrical Engineering)
Full-Text   |   PDF [4098 KB, uploaded 17 December 2017]   |  

Abstract

Although four-dimensional (4D) light field imaging has many advantages over traditional two-dimensional (2D) imaging, its high computation cost often hinders the application of this technique in many fields, such as object detection and tracking. This paper presents a hybrid method to accelerate the object detection in light field imaging by integrating the deep learning with the depth estimation algorithm. The method takes full advantage of computation imaging of the light field to generate an all-in-focus image, a series of focal stacks, and multi-view images at the same time, and convolutional neural network and defocusing are consequently used to perform initial detection of the objects in three-dimensional (3D) space. The estimated depths of the detected objects are further optimized based on multi-baseline super-resolution stereo matching while efficiency is maintained, as well by compressing the searching space of the disparity. Experimental studies are conducted to demonstrate the effectiveness of the proposed method. View Full-Text
Keywords: light field imaging; deep leaning; object detection; focal stacks; stereo matching. light field imaging; deep leaning; object detection; focal stacks; stereo matching.
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Ren, M.; Liu, R.; Hong, H.; Ren, J.; Xiao, G. Fast Object Detection in Light Field Imaging by Integrating Deep Learning with Defocusing. Appl. Sci. 2017, 7, 1309.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top