Gray connected components and image segmentation

1996 ◽  
Author(s):  
Yang Wang ◽  
Prabir Bhattacharya
Keyword(s):  
Image Segmentation ◽  
Connected Components

scholarly journals Guided lazy snapping for long thin object selection

2018 ◽  
Vol 189 ◽  
pp. 10026
Author(s):  
Nam H. Tran ◽  
Dongsun Seo ◽  
Henry T.H. Tu
Keyword(s):  
Image Segmentation ◽  
Connected Components ◽  
Segmentation Method ◽  
User Interactions ◽  
Object Selection ◽  
Segmentation Methods ◽  
Background Image ◽  
Lazy Snapping

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.

A SELF-DIRECTED METHOD FOR IMAGE SEGMENTATION USING A MODIFIED TOP-DOWN REGION DIVIDING APPROACH

2014 ◽  
Vol 28 (06) ◽  
pp. 1455008
Author(s):  
WALTER S. WEHNER ◽  
FRANK Y. SHIH
Keyword(s):  
Image Segmentation ◽  
Computational Complexity ◽  
Standard Deviation ◽  
Spatial Structure ◽  
Region Growing ◽  
Connected Components ◽  
Segmentation Method ◽  
Top Down ◽  
Class Information ◽  
Structure Destruction

We present a self-directed method for image segmentation using a modified top-down region dividing (TDRD) approach. The TDRD-based image segmentation method solves some of the issues with histogram and region growing-based segmentation techniques. The process is efficient and achieves proper results without over segmentation or spatial-structure destruction. In this paper, we examine seven user-defined parameters of the method. These parameters are converted from human inputs to values derived from in-class information created by the algorithm allowing for autonomous image segmentation, without the need of human input or feedback. Our new autonomous implementation also reduces the computational complexity of the algorithm. This reduction will produce significant savings for the total number of computations the algorithm needs to perform image segmentation. Experimental results show that the images using these new derived values yield superior results as compared to other methods, including the original TDRD method. We compare our results visually and numerically based on the within-class standard deviation (WCSD) and the number of connected components (NCC).

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