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[[File:Col10.jpg|left|frame|Fig. 3 Example of Intel camera RGB image.]]
[[File:Col10.jpg|left|frame|Fig. 3 Example of Intel camera RGB image.]]
[[File:Dep10.jpg|frame|none|center|Fig. 4 Example of Intel camera depth image.]]
[[File:Dep10.jpg|frame|none|center|Fig. 4 Example of Intel camera depth image.]]


As can be seen from Fig. 4, there are a large amount of dark black areas. These are referred to as holes, and are areas where the camera could not accurately detect a depth value. We will need to fill these in before using the depth maps.
As can be seen from Fig. 4, there are a large amount of dark black areas. These are referred to as holes, and are areas where the camera could not accurately detect a depth value. We will need to fill these in before using the depth maps.

Revision as of 05:18, 15 December 2016

Introduction

There are several examples of popular tools for filter creation. A popular one is Instagram, released in 2010 and acquired by Facebook in 2012.

Another one is Prisma, which makes use of convolutional neural networks to transfer artistic qualities of one image onto another [1].

Background

Methods

Data Capture

We captured RGB and Depth images with two tools: the Intel Realsense R200 world-facing camera and the Google Camera app.

Fig. 1 Intel Realsense R200.
Fig. 2 Intel Realsense R200.

The Intel camera (shown above) captures RGB images at up to 1920x1080 resolution, and depth images at up to 480x360 resolution. In order to keep the sizes consistent, we kept the resolution of both at 480x360. The depth maps themselves are created on-board the camera by first texturing the scene with an infrared projector (Fig. 2). The two infrared cameras then capture the textured scene and a depth map is created from the disparity between the two images. For best results, the depth map is rated at 0.5m-4m indoors, and up to 10m outdoors, although specular objects will produce inaccurate depth values. Examples of captured images are below.


Fig. 3 Example of Intel camera RGB image.
Fig. 4 Example of Intel camera depth image.



As can be seen from Fig. 4, there are a large amount of dark black areas. These are referred to as holes, and are areas where the camera could not accurately detect a depth value. We will need to fill these in before using the depth maps.

Hole Filling

Results

Conclusions

References

[1] http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf

Appendix I

Appendix II