In many applications of image processing there is a need to extract the solid background which it is usually sky for outdoor images. In our application we present this solution. We developed an automatic algorithm for detection and extraction of sky regions in the color image based on color classification.
In which the input image should be in the RGB color space and the blue color is detected and classified. Then the color image is transformed to the binary form and the connected regions are extracted separately.
The connected regions are then sorted in a descending order according to the biggest area and the biggest region is identified. Then we merged all objects that have similar sky properties. Experimental results showed that our proposed algorithm can produce good results compared to other existing algorithms.
Color extraction is quite important in image processing field since it can be used in the extraction of different kind of objects. There are many applications based on color classification such as, in satellite images to extract specific vegetation type using color information that this vegetation has the same color, in alpine ecosystem for monitoring snowmelt and estimating the influence of future climate change , in computer vision for recognize objects in real time [1,2], in video recognition systems for tracking of facial region , in medical field to detect the homogeneous regions for pathology study .
In our particular application, we aim to detect the background of the buildings since our main task is to extract buildings. It is difficult to define buildings since they have different shapes, different windows, different types of roofs and many details so it is hard to define them but we can define everything else and when we extract everything from image only the buildings will remains.
In this research study, we will use color classification for one specific application which always has a sky for a background. The images that will be used to test the proposed algorithm are provided by the research team.
Source: University of Gävle
Authors: Abdelrahman, Ahmed