Learning and Incorporating Top-Down Cues in Image Segmentation

2006 ◽  
pp. 338-351 ◽  
Author(s):  
Xuming He ◽  
Richard S. Zemel ◽  
Debajyoti Ray
Keyword(s):  
Image Segmentation ◽  
Top Down

A top-down region dividing approach for image segmentation

2008 ◽  
Vol 41 (6) ◽  
pp. 1948-1960 ◽  
Author(s):  
Yi-Ta Wu ◽  
Frank Y. Shih ◽  
Jiazheng Shi ◽  
Yih-Tyng Wu
Keyword(s):  
Image Segmentation ◽  
Top Down

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).

scholarly journals Top-Down Method Used for Pancreas Segmentation

2020 ◽  
Vol 9 (3) ◽  
pp. 1790-1793
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Medical Image Analysis ◽  
Input Image ◽  
Crack Identification ◽  
Density Estimator ◽  
Top Down ◽  
Scale Invariant ◽  
Bottom Up ◽  
Pancreas Segmentation

Image segmentation is actively an imperative title role in image analysis. Image segmentation is advantageous in many applications like traffic detection, surface crack identification, medical image analysis, face recognition, crop disease detection. Two Approaches are used for automatic pancreas segmentation. Top-Down and Bottom-Up approach used for CT image segmentation. In Top Down approach, Grey Level Co-occurrence Matrix, Simple Linear Iterative Clustering, Scale-Invariant Feature transform, Novel Modified Kernel fuzzy c-means clustering (NMKFCM) and Kernel Density Estimator methods used and automatic bottomup technique is used for pancreas subgrouping in C.T. scans. Top-Dow approach accuracy rate is less than bottom-up approach. Top-down approach required less time period as compare to bottom-up approach. In top-down approach, input image manually selected and processed it. KDE, NMKFCM and SIFT are used to detect feature of image. NMKFCM works on neighborhood point value. In KDE, Edge detection based on the kernel estimation of the probability density function .In SIFT, comprehensive information of local feature of image is focused.

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