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An adaptive fuzzy c-means (AFCM) clustering based algorithm was developed and applied to the segmentation and classification of multi-color fluorescence in situ hybridization (M-FISH) images, which can be used to detect chromosomal abnormalities for cancer and genetic disease diagnosis. The algorithm improves the classical fuzzy c-means (FCM) clustering algorithm by introducing a gain field, which...
A high rate of expression of Endothelin protein in the placental cell is very much regulated by inhalation of tobacco smoke and leads to placental abnormalities subjected to birth failure. Our application developed using Image Processing, Nearest Neighbor algorithm (NN) and Genetic Algorithms (GA), automates the study of these proteins to assist pathologists and lab technicians in achieving a more...
Detection of outliers and relevant features are the most important process before classification. In this paper, a novel semi-supervised k-means clustering is proposed for outlier detection in mammogram classification. Initially the shape features are extracted from the digital mammograms, and k-means clustering is applied to cluster the features, the number of clusters is equal with the number of...
Magnetic Resonance Imaging (MRI) is one of the best technologies currently being used for diagnosing brain tumor. Brain tumor is diagnosed at advanced stages with the help of the MRI image. Segmentation is an important process to extract suspicious region from complex medical images. Automatic detection of brain tumor through MRI can provide the valuable outlook and accuracy of earlier brain tumor...
This paper describes a new method for iris segmentation using HSI and RGB color spaces. The outer and inner boundaries of the iris are extracted using the k-means unsupervised clusterization method. For the outer boundary detection the best results is obtained using as input variables of the clusterization method the red and green components of the RGB space. The final outer boundary is detected through...
Recently, the most of traffic condition is researched by the person. It is studied widely to research traffic condition automatically. A transportation control system is being used to collect these data and to then perform analysis. A typical example is information technology like ITS(Intelligent Transport System). But there are many problems. Cluster distinction and template matching is used as conventional...
We propose a method for multi-object segmentation in a projection plane. Our algorithm requires a stereo camera system called Subtraction Stereo, which extracts foreground information with a fixed stereo camera. The main contribution of this paper is how the image sequences that include partial occlusion of the foreground objects can be accurately segmented using mean shift clustering in real-time...
An inherent problem of unsupervised texture segmentation is the absence of previous knowledge regarding the texture patterns present in the images to be segmented. A new efficient methodology for unsupervised image segmentation based on texture is proposed. It takes advantage of a supervised pixel-based texture classifier trained with feature vectors associated with a set of texture patterns initially...
This paper addresses the problem of improving the accuracy of character recognition with a limited quantity of data. The key ideas are twofold. One is distortion-tolerant template matching via hierarchical global/partial affine transformation (GAT/PAT) correlation to absorb both linear and nonlinear distortions in a parametric manner. The other is use of multiple templates per category obtained by...
In this paper we present a novel segmentation approach that performs fuzzy clustering and feature extraction. The proposed method consists in forming a new descriptor combining a set of texture sub-features derived from the Grating Cell Operator (GCO) responses of an optimized Gabor filter bank, and Local Binary Pattern (LBP) outputs. The new feature vector offers two advantages. First, it only considers...
For the robot vision system in apple harvesting robot, a new image segmentation method based on entropy clustering is proposed in HSI color space. Firstly, noise was wiped off by using weighted algorithm of median filtering in HSI color space instead of traditional algorithm in RGB model; secondly, Hue and Saturation components were extracted to do entropy clustering with their independence with Intensity,...
In the framework of remote-sensing image classification support vector machines (SVMs) have recently been receiving a very strong attention, thanks to their accurate results in many applications and good analytical properties. However, SVM classifiers are intrinsically noncontextual, which represents a severe limitation in image classification. In this paper, a novel method is proposed to integrate...
We address the problem of Kannada character recognition, and propose a recognition mechanism based on k-means clustering. The large dataset of Kannada characters and their similarity makes the problem one order of magnitude more difficult than for a standard language like English. We propose a segmentation technique to decompose each character into components from 3 base classes, thus reducing the...
This paper presents an innovative approach for localizing and segmenting duplicate objects for industrial applications. The working conditions are challenging, with complex heavily-occluded objects, arranged at random in the scene. To account for high flexibility and processing speed, this approach exploits SIFT keypoint extraction and mean shift clustering to efficiently partition the correspondences...
Image segmentation plays a key role in many image content analysis applications, and a lot of effort has aimed at improving the performance of established segmentation algorithms. In this paper, we present a mean shift-based combined Dirichlet process mixture (MDP)/Markov Random Field (MRF) image segmentation algorithm. Our method incorporates a mean shift process to iteratively reduce the difference...
In this work we demonstrate the potentiel of the InSAR coherence in classifying regions threatened by the desertification. We propose to combine features from InSAR amplitude and coherence images into a single 3D space in which every pixel is represented by its three-values vector (master image, slave image, coherence image) then to apply the multichannel Fuzzy C-Means (FCM) clustering algorithm....
This paper presents a template-based facial caricature generation approach. Since the major difficulty in generating facial caricature automatically is the uniqueness of individuals and the polymorphism of features, a modified active shape model (ASM) is first designed to locate key feature points accurately by using 2D local grey-level structures. Then, extracted facial components are classified...
Effective background reconstruction is the key for real time traffic flow monitoring. High traffic density and complexity of background scene make reconstruction more difficult. Background estimation based on the median method is imprecise under a complex traffic flow condition. In this paper, a new background estimation method based on the similarity of background using parameters of gray mean and...
This paper applies the multi-feature clustering analysis for SPOT5 color remote sensing images change detection based on image subtraction method and solves the problem of misjudge polygons. Subtraction chart is constructed by computing gray difference between pixels with a quite low threshold; Then, multi-feature-clustering-analysis is utilized to subtraction chart to delete misjudge polygons; Lastly,...
To improve the correctness and real-time performance in the process of image matching, this paper proposed a fast matching algorithm based on image K-gray-degree clustering. Given a template with irregular shape, this algorithm divides the image into certain size blocks called R_block, and calculates their mean gray value, then clusters original templates to K-degree templates according to gray distribution...
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