High Accuracy and High Robust Natural Image Segmentation Algorithm without Parameter Adjusting
Date Issued
2015
Date
2015
Author(s)
Lu, I-Fan
Abstract
In computer vision and image processing, image segmentation is always an important fundamental work. Though this topic has been researched for many years, it is still a challenging task to well segment most of the natural images automatically without adjusting any parameter. Recently, the researches of superpixels have great improvement. This new technique makes the traditional segmentation algorithms more efficient and has better performances. In this thesis, an automatic image segmentation algorithm based on superpixels and many other techniques is proposed. It can accurately segment almost all of the natural images without parameter adjustment. In our algorithm, the techniques of entropy rate superpixels (ERSs), edge detection, saliency detection, and computing texture feature are adopted. With the aid of ERSs, the proposed algorithm can be implemented very efficiently. To prevent over-merge of superpixels, modified edge detection which computes the gradient information of the contours and the interiors of superpixels is used. Saliency detection and the texture features of an image are also used to prevent over-segmentation. Moreover, an adaptive threshold is also used for superpixel merging. These techniques make the segmentation result more consistent with human perception without adjusting any parameter. Simulations show that our proposed method can well segment most of natural images and outperform state-of-the-art methods.
Subjects
Image segmentation
ERS superpixels
edge detection
saliency detection
computer vision
Type
thesis
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