This project compares the supervised logistic regression segmentation algorithm against the unsupervised k-means clustering segmentation. We observed that the difference between either method is not very significant. When performed on the 100 test cases for BSD300, the supervised method on average achieved a precision rate of 0.47 and the unsupervised method achieved a precision rate of 0.41. T...

In this paper the problems of deriving a taxonomy from a text and concept-oriented text segmentation are approached. Formal Concept Analysis (FCA) method is applied to solve both of these linguistic problems. The proposed segmentation method offers a conceptual view for text segmentation, using a context-driven clustering of sentences. The Concept-oriented Clustering Segmentation algorithm (COC...

Robust image segmentation plays an important role in a wide range of daily applications, like visual surveillance system, computer-aided medical diagnosis, etc. Although commonly used image segmentation methods based on pixel intensity and texture can help finding the boundary of targets with sharp edges or distinguished textures, they may not be applied to images with poor quality and low cont...

Data clustering refers to the method of grouping data into different groups depending on their characteristics. This grouping brings an order in the data and hence further processing on this data is made easier. This paper explains the clustering process using the simplest of clustering algorithms the K-Means. The novelty of the paper comes from the fact that it shows a way to perform clusterin...

Abstract Conventional algorithms fail to obtain satisfactory background segmentation results for underwater images. In this study, an improved K-means algorithm was developed image address the issue of improper K value determination and minimize impact initial centroid position grayscale during gray level quantization conventional algorithm. A total 100 images taken by robot were sampled test a...

<p class="Abstract">A proposed method using neuro-fuzzy k-means for the segmentation process of brain has been developed successfully, simulated and assessed. The assessed by clinical images magnetic resonance imaging (MRI) technology, to segment three main tissues brain. system is able important regions brain, which are white matter, grey matter cerebrospinal fluid (CSF) more accurately,...