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Image segmentation is a fundamental problem in computer vision. Recently, ensemble learning receives more and more attention for its robustness, novelty and stability. Generally there are two problems in ensemble learning. One is the generation of the individuals of ensemble. The other is the consensus function of the individuals. We focus on the second problem. A new consensus function is proposed...
In the fiber image analysis system, correctly segmenting fiber from fiber micrograph is critical for fiber feature extraction and further identification. In this paper, the GVF snake model with the initial contour obtained by contour tracking method based on K-means clustering segmentation is proposed for fiber segmentation. Firstly, the K-means clustering method is used to obtain the initial coarse...
With the development of the Broadcasting and Video network, the Monitoring System on Digital Video Broadcasting is becoming more and more important. Image recognition technology is widely applied to detect the degraded video in the television observation system. Mosaic block easily occurs in the TV signals, which will degrade the video quality. The conventional mosaic detection algorithm can't distinguish...
Contour layer of topographic maps can provide useful information for many vector based GIS applications. A topographic map consists of various semantic layers of overlapping information, differentiated primarily based on colour. Hence, to extract the contour layer from a topomap, colour segmentation algorithms are employed. However due to scanning and inherent features of topomap, colour space selection...
This paper describes a new bubble segmentation algorithm based on shape from shading for in-situ microscopy. An in-situ microscope is an instrument to capture and analyze intensity images of cells inside of a bioreactor with minimal operator intervention and without the risk of culture contamination. For bubble segmentation, the closed bubble boundaries are first extracted by thresholding a depth...
In this paper, an improved ant colony optimization based approach for image edge detection is proposed. The algorithm use ant colony clustering approach to extract edge feature. The approach set the heuristics information function and the initial cluster, thus avoiding the search blindness which carried out by traditional ant colony algorithm. And a series of simulation experiments demonstrate the...
Currently, it is an effective means of collecting diseases information from mural collections by using edge detection and manual interpretation. But it is hard to extract better edges of mural diseases with existing edge detection algorithms. Therefore, a new K-Means Sobel algorithm is proposed. Firstly, we process a gray orthophoto map with K-means clustering algorithm to obtain a gray value matrix...
As the coverage area of the sub-block is too large, the texture feature clustering based on sub-block often produces the mosaic phenomenon of inaccurate boundary. In this paper, the texture clustering algorithm based on pixel extracts the texture feature vector of its central pixel point from sub-block. The image texture feature is standardized, then the normalized feature vector is clustered and...
In this paper, we present a novel scene change detection algorithm for mobile camera platforms. Our approach integrates sparse 3D scene background modelling and dense 2D image background modelling into a unified framework. The 3D scene background modelling identifies inconsistent clusters over time in a set of 3D cloud points as the scene changes. The 2D image background modelling further confirms...
Hierarchical Texture Segmentation using Wavelet packet decomposition performs an unsupervised classification of Texture features, extracted using wavelet packet decomposition to generate a segmented image. Recursive decomposition of both the approximation and the detail coefficients derived from the original signal provides a wider spectrum for richer feature extraction. This Texture Segmentation...
Given a large collection of images, very few of which have labels, how can we guess the labels of the remaining majority, and how can we spot those images that need brand new labels, different from the existing ones? Current automatic labeling techniques usually scale super linearly with the data size, and/or they fail when only a tiny amount of labeled data is provided. In this paper, we propose...
A new integrated feature distributions based color textured image segmentation algorithm has been proposed. The proposed scheme uses histogram based new color texture extraction method which inherently combines color texture features rather then explicitly extracting it. Use of non parametric Bayesean clustering makes the segmentation framework fully unsupervised where no a priori knowledge about...
In this paper we present a divisive hierarchical method for the analysis and segmentation of visual images. The proposed method is based on the use of the k-means method embedded in a recursive algorithm to obtain a clustering at each node of the hierarchy. The recursive algorithm determines automatically at each node a good estimate of the parameter k (the number of clusters in the k-means algorithm)...
Humans are adept at identifying informative regions in individual images, but it is a slow and often tedious task to identify the salient parts of every image in a large corpus. A machine, on the other hand, can sift through a large amount of data quickly, but machine methods for identifying salient regions are unreliable. In this paper, we develop a new method for identifying salient regions in images...
Texture segmentation by Pseudo Jacobi -Fourier moments is presented in this paper. Given a window size, moments for each pixel in the image are computed within small local windows, and then texture feature images be obtained by using a nonlinear transducer. Finally, each pixel in the image is classified by K-mean clustering algorithm.
In this work we present a fully automated method for the accurate detection of cell nuclei boundaries in conventional Pap smear images, based on the watershed transform. For the extraction of nuclei and cytoplasm markers, which are used as starting points for the flooding process, a morphological reconstruction step is initially performed in each image. The watershed transform is then applied in the...
In this paper, a clustering method for handwritten digit recognition is studied. The digit samples, firstly are processed and features are extracted. Based on these features, a clustering method is designed and implemented to cluster the digit samples. Experiments finally show that the clustering method is efficient in handwritten digit recognition.
Classification algorithm is an important technique that has been studied for many years in hyperspectral image processing along with the development of hyperspectral remote sensing. Traditional classification algorithms mostly focus on the differences of spectral dimension but neglecting spatial structures of geography objects. By improving the ECHO algorithm, we put forward a classification algorithm...
An approach of ant colony optimization combing gradient and relative difference of statistical means to image edge detection is proposed in this paper. The values of gradient and the relative difference of statistical means are extracted for the ants' searching. Experimental results show that the superior performances of the proposed algorithm.
Knuckle print is a new biometric technology and contact less one is a convenient and cleaner manner. Extracting knuckle print from the original image is important to the improvement of identification rate. This paper introduces a new method of binarization and segmentation for contact less knuckle print authentication. Firstly, the RGB image is converted into HSV color space according to the clustering...
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