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On the Normalized Cut

100%

Nadarajah S.

Fundamenta Informaticae

2008

tom Vol. 86, nr 1-2

169-173

In the recent paper by Soundararajan and Sarkar (2003), the normalized cut, a graph partitioning measure for perceptual organization, was shown to be a sum of two beta distributed random variables and expressions derived for its mean and mode. Here, it is pointed out that the given expression for the mode is incorrect. The correct expression is derived and the implications of the error discussed.

A Graph-Based Image Segmentation Approach for Image Classification and Its Application on SAR Images

84%

Sharifi M. , Kiani K. , Kheirkhahan M.

Przegląd Elektrotechniczny

2013

tom R. 89, nr 4

202-205

In this paper, we propose a novel approach for image classification based on Graph-based image segmentation method and apply it on SAR images with satisfactory clustering performance and low computational cost. In this method first, the image pre-processes by mean shift algorithm to cluster into disjoint region, then the segmented regions are represented as a graph structure with all connected neighbourhood, and after that normalized cut method is applied to classify image into defined classes.

W artykule przedstawiono metodę klasyfikacji obrazów, z wykorzystaniem segmentacji metodą grafową. Proponowana rozwiązanie wykorzystano w analizie obrazów SAR.

Detection of hard exudates using mean shift and normalized cut method

84%

Banerjee S. , Kayal D.

Biocybernetics and Biomedical Engineering

2016

tom Vol. 36, no. 4

679--685

As diabetic retinopathy (DR) is one of the main causes of loss of vision among diabetic patients, an early detection using automated detection techniques can prevent blindness among diabetic patients. Hard exudates constitute one of the early symptoms of DR and this paper describes a method for its detection using fundus images of retina, employing a combination of morphological operations, mean shift (MS), normalized cut (NC) and Canny's operation. This combined technique avoids over segmentation and at the same time reduces the time complexity while clearly delineating the exudates. Output of the proposed method is evaluated using public databases and produces sensitivity, specificity and accuracy as 98.80%, 98.25% and 98.65%, respectively. The ROC curve gives 0.984 as area under curve. The sensitivity, specificity, accuracy and area under curve of ROC indicate the effectiveness of the method.