KEYWORDS: Video, Detection and tracking algorithms, Algorithm development, Video processing, RGB color model, Communication engineering, Cameras, Video compression, Visualization, 3D image processing
In this paper, we present a method for reducing the intensity of shadows cast on the ground in outdoor sports videos to
provide TV viewers with a better viewing experience. In the case of soccer videos taken by a long-shot camera
technique, it is difficult for viewers to discriminate the tiny objects (i.e., soccer ball and player) from the ground
shadows. The algorithm proposed in this paper comprises three modules, such as long-shot detection, shadow region
extraction and shadow intensity reduction. We detect the shadow region on the ground by using the relationship between
Y and U values in YUV color space and then reduce the shadow components depending on the strength of the shadows.
Experimental results show that the proposed scheme offers useful tools to provide a more comfortable viewing
environment and is amenable to real-time performance even in a software based implementation.
KEYWORDS: Image segmentation, 3D image processing, Image analysis, 3D displays, Communication engineering, Image classification, Edge detection, Imaging systems, Digital image processing, Radio over Fiber
With increasing demands of 3D contents, conversion of many existing two-dimensional contents to three-dimensional
contents has gained wide interest in 3D image processing. It is important to estimate the relative depth map in a single-view
image for the 2D-To-3D conversion technique. In this paper, we propose an automatic conversion method that
estimates the depth information of a single-view image based on degree of focus of segmented regions and then
generates a stereoscopic image. Firstly, we conduct image segmentation to partition an image into homogeneous regions.
Then, we construct a higher-order statistics (HOS) map, which represents the spatial distribution of high-frequency
components of the input image. the HOS is known to be well suited for solving detection and classification problems
because it can suppress Gaussian noise and preserve some of non-Gaussian information. We can estimate a relative depth
map with these two cues and then refine the depth map by post-processing. Finally, a stereoscopic image is generated by
calculating the parallax values of each region using the generated depth-map and the input image.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.