Abstract
We show a novel way to select long thin objects in an image by enhancing the output of the existing foreground/background image segmentation methods. Most superpixel-based methods fail to select the long thin details, such as legs and whiskers, and extended curves from the main objects. We observe, however, the output without long thin details, can be used as the guided information to obtain the connected components. Based on this observation, our Guided Lazy Snapping method overcomes the limitation of the Lazy Snapping methods (or other alternatives superpixel-based segmentation method) to select long thin objects. The results show that connected components in the image can be selected without having a lot of user interactions (mouse clicks) on each extended parts of the object.
Highlights
Image segmentation [1,2,3,4,5,6,7,8,9,10] is one of the most challenging tasks of image processing field, which has various novel proposed approaches in its long history in computer vision
Based on the samples given by a user to the foreground and background set, the graph cut minimizes an energy function consisting of the data and prior knowledge as constraints
The main goal of our work is to investigate the effect of the middle layer to the framework, so the last step in the Lazy Snapping’s pipeline is not mentioned
Summary
Image segmentation [1,2,3,4,5,6,7,8,9,10] is one of the most challenging tasks of image processing field, which has various novel proposed approaches in its long history in computer vision. Graph cut technique [1] had become the heart of many current state-of-art image segmentation techniques [2,3,4]. The image segmentation problem is modelled as binary labelling problem such that each pixel is assigned a unique label which denotes object and background classes. Based on the samples given by a user to the foreground and background set, the graph cut minimizes an energy function consisting of the data and prior knowledge as constraints. The data constraint restricts the desired solution to be close to the observed data and the prior constraint confines the desired solution to have a form agreeable to the prior knowledge
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