The user-friendliness and cost-effectiveness have contributed to the growing popularity of mobile phone cameras.
However, images captured by such mobile phone cameras are easily distorted by a wide range of factors, such
as backlight, over-saturation, and low contrast. Although several approaches have been proposed to solve the
backlight problems, most of them still suffer from distorted background colors and high computational complexity.
Thus, they are not deployable in mobile applications requiring real-time processing with very limited resources. In
this paper, we present a novel framework to compensate image backlight for mobile phone applications based on
an adaptive pixel-wise gamma correction which is computationally efficient. The proposed method is composed
of two sequential stages: 1) illumination condition identification and 2) adaptive backlight compensation. Given
images are classified into facial images and non-facial images to provide prior knowledge for identifying the
illumination condition at first. Then we further categorize the facial images into backlight images and nonbacklight
images based on local image statistics obtained from corresponding face regions. We finally compensate
the image backlight using an adaptive pixel-wise gamma correction method while preserving global and local
contrast effectively. To show the superiority of our algorithm, we compare our proposed method with other
state-of-the-art methods in the literature.
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, Cameras, Digital cameras, Image processing algorithms and systems, 3D modeling, Imaging systems, Radon, Reconstruction algorithms, Communication engineering, Detection and tracking algorithms
In this paper, we present a novel method for removing foreground objects in multi-view images. Unlike the conventional
methods, which locate the foreground objects interactive way, we intend to develop an automated system. The proposed
algorithm consists of two modules: 1) object detection and removal, and 2) detected foreground filling stage. The depth
information of multi-view images is a critical cue adopted in this algorithm. By multi-view images, it is not meant a
multi-camera equipped system. We use only one digital camera and take photos by hand. Although it may cause bad
matching result, it is sufficient to detect and remove the foreground object by using coarse depth information. The
experimental results indicate that the proposed algorithm provides an effective tool, which can be used in applications for
digital camera, photo-realistic scene generation, digital cinema and so on.
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.