In this paper, a modified segmentation algorithm for printed Farsi words is presented. This algorithm is based on a previous work by Azmi that uses the conditional labeling of the upper contour to find the segmentation points. The main objective is to improve the segmentation results for low quality prints. To achieve this, various modifications on local baseline detection, contour labeling an...

In image processing, image segmentation is one of the important tasks to extract information from the images. A variety of segmentation algorithm is developed to satisfy increasing requirement of image segmentation. Fuzzy CMeans is unsupervised method that has been applied for the variety of purposes such as clustering, classification, image segmentation and target recognition. This method can ...

In computer vision, image segmentation is an important problem and plays vital role in medical imaging. Analysis and diagnosis of tumor in MRI brain image involves segmentation as very essential steep. It separates the region of interest objects from the background and the other objects. Several approaches are used for MRI brain tumor segmentation. Fuzzy C Means (FCM) is most widely used fuzzy ...

A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Among most of the biometric methods, Iris recognition is regarded as the most reliable and accurate biometric identification system. The performance of iris recognition system highly depends on the accurate segmentation. For the Iris Segmentation there i...

Interpretation of MRI images is difficult due to inherent noise and inhomogeneity. Segmentation is considered as vitally important step in medical image analysis and classification. Several methods are employed for medical image segmentation such as clustering method, thresholding method, region growing etc. In this paper, attention has been focused on clustering method such as Fuzzy C-means cl...

The findings of image segmentation reflects its expansive applications and existence in the field of digital image processing, so it has been addressed by many researchers in numerous disciplines. It has a crucial impact on the overall performance of the intended scheme. The goal of image segmentation is to assign every image pixels into their respective sections that share a common visual char...

through this paper we proposed the methodology that incorporates the K-means and fuzzy c means algorithm for the color image segmentation. The image segmentation may be defined as the process of dividing the given image into different parts. Here we are taking color image as the input and we are supposed to segment the given image on the basis of its color. The clustering algorithm also proved ...

Image segmentation especially fuzzy based image segmentation techniques are widely used due to effective segmentation performance. For this reason, a huge number of algorithms are proposed in the literature. This paper presents a survey report of different types of classical fuzzy clustering techniques which available in the literature. Keyword: fuzzy clustering, image segmentation, fuzzy c-mea...

Unsupervised Techniques of segmentation are simple and the segmented output using these techniques gives the best results. This paper presents an automatic segmentation method based on unsupervised segmentation done on Ultrasound (US) images received from the radiologist. US imaging is widely used in clinical diagnosis and image-guided interventions, but suffers from poor quality. One of the mo...