A Fast Image Cutout Tool Based on Gaussian Mixture Model and Region Growing

• Main Authors: Chen, Ting-Pu, 陳定樸
• Other Authors: Lin, Ja-Chen
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/79543475821069297899

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 101 === In this thesis a fast and accurate image cutoff method is developed. The method enables the users to clip object of interest out of an image, which is a useful tool for various applications such as image composition and/or editing. The proposed method represents the colors of an input image in Gaussian Mixture Model, and designs an iterative region growing based segmentation algorithm to draw out the target object. The proposed scheme has the following advantages: (1) the level of user interaction is low. The cut out operation is accomplished through simply drawing a rectangle encompassing the target object, and (2) the extracted objects are well-tailored. Both object with explicit contour and object with complicate contour can be extracted accurately. The proposed scheme is implemented and compared with the efficient object extraction method – the GrapCut. Experiment results show the proposed method exhibits higher performance than the GrapCut, both in the completeness of the extracted object and the computation time.