Bilan Zhu and Masaki Nakagawa Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo 184-8588, Japan E-mail: {zhubilan, nakagawa}@cc.tuat.ac.jp Abstract This paper describes a method of producing segmentation point candidates for on-line handwritten Japanese text by a support vector machine (SVM) to improve text recognition. This method extracts multi-dimensional featu...

A method is presented for segmenting text into subtopic areas. The proportion of related pairwise words is calculated between adjacent windows of text to determine their lexical similarity. The lexical cohesion relations of reiteration and collocation are used to identify related words. These relations are automatically located using a combination of three linguistic features: word repetition, ...

|Quantitative analysis of head images obtained by computed tomography (CT) requires accurate segmentation. A new method for automatic segmentation of human spontaneous intracerebral brain hemorrhage (ICH) from digitized CT lms is presented in the paper. The proposed segmentation method has a two-level hierarchical structure. The segmentation at both levels is based on the unsupervised fuzzy C-m...

Treatment planning for high precision radiotherapy of head and neck (H&N) cancer patients requires accurate delineation of many critical structures. Manual contouring is tedious and often suffers from large interand intra-rater variability. To reduce manual labor, we have previously developed a fully automated, atlas-based method for H&N CT image segmentation [1, 2]. In this work, we adapt the ...

A general framework for automatic segmentation of diatom images is presented. This segmentation is a critical first step in contour-based methods for automatic identification of diatoms by computerized image analysis. We review existing results, adapt popular segmentation methods to this difficult problem, and finally develop a method that substantially improves existing results. This method is...

A major challenge in brain tumor treatment planning and quantitative evaluation is determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) technique has emerged as a front-line diagnostic tool for brain tumors without ionizing radiation. Manual segmentation of brain tumor extent from 3D MRI volumes is a very time-consuming task and the performance is highly relied on...

In this paper we present a method for finding optimal parameters of graph Laplacian-based semantic segmentation. This method is fully unsupervised and provides parameters individually for each image. In the experiments on Graz dataset the accuracy of segmentation obtained with the parameters provided by our method is very close to the accuracy of segmentation obtained with the parameters chosen...

Object segmentation is a challenging task in image processing and computer vision. In this paper, we present a visual attention based segmentation method to segment small sized interesting objects in natural images. Different from the traditional methods, we first search the region of interest by using our novel saliency-based method, which is mainly based on band-pass filtering, to obtain the ...

Fingerprint segmentation is an important step in an automatic fingerprint recognition system. Due to applications of various sensors, fingerprint segmentation inevitably suffers from sensor interoperability problem. K-means algorithm is one solution to address the sensor interoperability problem in fingerprint segmentation. However, the traditional k-means based method does not well deal with t...

The key step in object-oriented image classification is the segmentation of the image into discrete meaningful objects. Generally the relation between the segmentation parameters and the corresponding segmentation outcome is far from being obvious, and the definition of suitable parameter values is usually done through a troublesome and time consuming trial and error process. This paper propose...