This paper presents a novel approach to automatic classifying and identifying of tree leaves using image segmentation fusion. With the development of mobile devices and remote access, automatic plant identification in images taken in natural scenes has received much attention. Image segmentation plays a key role in most plant identification methods, especially in complex background images. Wher...

This paper focuses on a method for automatically dividing speech utterances into phonemic segments, which are used for constructing synthesis unit inventories for speech synthesis. Here, we propose a new segmentation parameter called, “dynamics of fundamental frequency (DF0).” In the fine structures of F0 contours, there exist phonemic events which are observed as local dips at phonemic transit...

most of the erythrocyte related diseases are detectable by hematology images analysis. at the first step of this analysis, segmentation and detection of blood cells are inevitable. in this study, a novel method using a line operator and watershed algorithm is rendered for erythrocyte detection and segmentation in blood smear images, as well as reducing over-segmentation in watershed algorithm 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...

brain mr images tissue segmentation is one of the most important parts of the clinical diagnostic tools. pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. supervised segmentation methods lead to high accuracy but they need a large amount of labeled data, which is hard, expensive and slow to obtain. moreove...

This work investigates the possibility of designing a self-calibrating computer vision algorithm for given tracking or segmentation methods. We concentrate on a (suitably modified) segmentation algorithm, the meanshift segmentation of Comaniciu and Meer (2002). We show that even for a basic algorithm such as the one under study, the same set of parameter values cannot be universally optimal. Ho...

potato image segmentation is an important part of image-based potato defect detection. this paper presents a robust potato color image segmentation through a combination of a fuzzy rule based system, an image thresholding based on genetic algorithm (ga) optimization and morphological operators. the proposed potato color image segmentation is robust against variation of background, distance and ...

We transpose an optimal control technique to the image segmentation problem. The idea is to consider image segmentation as a parameter estimation problem. The parameter to estimate is the color of the pixels of the image. We use the adaptive parameterization technique which builds iteratively an optimal representation of the parameter into uniform regions that form a partition of the domain, he...

introduction: nowadays virtual colonoscopy has become a reliable and efficient method of detecting primary stages of colon cancer such as polyp detection. one of the most important and crucial stages of virtual colonoscopy is colon segmentation because an incorrect segmentation may lead to a misdiagnosis.  materials and methods: in this work, a hybrid method based on geometric deformable models...