Thumbnails for Light Fields

-
Thumbnails for Light Fields

OUTPUT
Background and Context:

 Light field (LF) cameras are gaining popularity because of advances in lens technologies and new post-processing capabilities [1]. Consequently, new products are constantly being developed that are designed to attract more consumers. For examples, there are currently several projects to develop and apply LF technology on phone cameras (i.e. Linx and Pelican Imaging). As the technology becomes more readily available to everyday consumers, new and creative rendering procedures will be required to maximize the utility of the captured data. For the technology to be adopted, the rendered images must provide fun/creative attributes that are beyond conventional image manipulation (i.e. social media).

 Project Goals:

 Given the growing availability for consumer cameras, there is an increasing potential for using light fields online in applications such as social media or advertising [2]. For this project, we propose to develop a robust workflow to generate effective, practical, and dynamic thumbnails from light field images (i.e. gif). Currently available thumbnails for light fields require the user to click on them to refocus or perform simple sweeps that are not visually appealing. We intend to explore and define universally applicable and visually attractive filters/effects that could then be presented as short gif thumbnails such as camera shake effect, camera focusing effects etc. During the project,

we plan to explore various optical flow techniques and apply the ones within our reach that help

 Resources

 We plan to use publicly available LF images to perform our project. We will obtain images from the Stanford lightfields webpage as well as a dataset created by Rerabek and Ebrahimi [4] made up of images that were obtained by a Lytro Illum light field camera. The latter dataset is categorized
based on the content of the images.

 References

 3[1] M. Levoy and P. Hanrahan. Light field rendering. In Proceedings of the 23rd annual conference
on Computer graphics and interactive techniques, pages 31–42. ACM, 1996.
[2] R. Ng, M. Levoy, M. Br´edif, G. Duval, M. Horowitz, and P. Hanrahan. Light field photography
with a hand-held plenoptic camera. Computer Science Technical Report CSTR, 2(11):1–11,
2005.
[3] P. O’Donovan. Optical flow: Techniques and applications. The University of Saskatchewan,
2005.
[4] M. Rerabek and T. Ebrahimi. New light field image dataset. In 8th International Conference on Quality of Multimedia Experience (QoMEX), number EPFL-CONF-218363, 2016.


 FOR BASE PAPER PLEASE MAIL US 

 DOWNLOAD SOURCE CODE CLICK HERE

Comments

Post a Comment

Popular posts from this blog

Light Field Images for Background Removal

-
Image
Light Field Images for Background Removal OUTPUT OVERVIEW Standard edge detection or foreground/background separation techniques, such as Otzu’smethod, require color or intensity differences between the background and regions that need tobe separated. For example, green screens are routinely set up as the background in a scene so thatthere is a clear difference in color between the background and foreground.Light filed images, captured in 4D and passively containing depth information for thescene can be used to approximate this effect. Depth estimation in the scene alone could provide a metric to separate the foreground and background, but more sophisticated methods are available.Considering the edge detection from a single image from a single viewpoint and analyzing the depths from the light field around the edges in that image, occluded edges and intensity or color edges can be distinguished. Along with the rest of the depth information this can allow the foreground and b
Read more

Using Image Processing to Identify and Score Darts thrown into a Dartboard

-
Image
✔️Using Image Processing to Identify and Score Darts thrown into a Dartboard ✔️Introduction The game of darts is a throwing sport in which participants toss projectiles into a circular target  attached to a vertical surface. The target is divided into many regions which correspond to different point and multiplier values. Darts is commonly played across North America and Europe and is a popular past time for many so an application that allows players to more conveniently keep score would be widely  beneficial. “Darts” is a general term for a targeting game following this basic premise and many game variations exist within this archetype; all utilizing the standard dart projectiles with a regulation dart board. Each game variant may have different objectives for which players to aim but identifying the region in  which the dart has hit in the dartboard is necessary for proper scoring. This project proposes the use of image processing to identify thrown darts and to det
Read more

Digital Make up Face Generation

-
Image
Digital Make up Face Generation Goal Current make-up applications rely on using photoshop tools to apply makeup on the target's digital  faces and generate results. While these applications allow customization, a customer who wants to  quickly decide on the type of makeup kit to buy at a store will not find it useful. The customer might just  want to find out how the make-up look on the cover or billboard will look like on her face. The goal of  this project is to use an existing reference image of another subject with a make-up applied, and  transfer the reference's make up on the target's face. The application can be further extended to photo  retouching and illumination transfer from the reference image to the target. Methodology In order to transfer the make-up from the reference onto target in a pixel by pixel basis, the areas of  interest must align. Face features such as eyes, nose, mouth and contours of the face, will be recognized  using Active Shape
Read more