Abstract
It is difficult to separate objects from an image when its background is nonuniform. Traditional methods tend to get obvious targets by using many different algorithms, such as Ostu, morphology, etc. But it frequently fails in extracting objects with different size and shape in nonunfirom background. A new method is proposed for nonuniform image segmentation in this paper. First, on an initial image, grid sample method is performed to reduce data space and prepare for background estimation and an example image is formed by those grids. Then, Gaussian Low Pass Filter (GLPF) is used to filter the noise point in the small image. Then, the next step is to magnify the area of this example image through an interpolation algorithm. Facet Model is used to estimate the background image. Finally, the object image can be acquired by the initial image substracting this estimated background image. Experiments are performed and according to the results, the validity and adaptability of the method is enhanced obviously, compared with conventional image segmentation algorithms.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
