| Summary: | 碩士 === 國立臺灣大學 === 電信工程學研究所 === 105 === As a basic preprocessing procedure, image segmentation plays an important role in computer vision and image processing. There are many applications for image segmentation, such as object recognition and image compression. Recently, different kinds of image segmentation algorithms have been proposed.
In this thesis, we propose an image segmentation algorithm based on superpixel, color, edge, texture and saliency information. The algorithm is designed to segment image into a certain number of regions assigned by the user. By using the superpixel information, one can improve the computation efficiency. The color and texture information of superpixels are mainly used in the superpixel growing process. On the contrast, the edge information on the boundary of two adjacent superpixels is used for determining whether the two superpixels should be prevented from merging. Saliency information is also a factor to suppress the merging process in order to keep the object integrity. In addition, we adjust the weight of edge, texture, and saliency information by measuring the foreground significance. In the adaptive region merging process, the merging criterion will be adaptive to the current region number.
When the foreground significance is applied to medical cell image, we can estimate the imaging characteristic such that a better threshold can be chosen and further improve the cell image segmentation and tracing result.
|
|---|
