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Document Image Binarization refers to the task of transforming a scanned image of a handwritten or printed document into a bi-level representation containing only characters and background. Here, we address the historic document image binarization problem using a three-stage methodology. Firstly, we remove possible stains and noise from the document image by estimating the document background image...
This paper describes a novel region segmentation method designed to avoid complications of the threshold process used in traditional segmentation methods in 2-D optical coherence tomography (OCT) images. Analysis of the layers and regions in OCT images is used to diagnose the presence of cancer and identify the stage of the cancer if present. However, scattering during OCT images generates a speckle...
The monitoring of electrical and water fixtures in the home is being applied for a variety of “smart home” applications, such as recognizing activities of daily living (ADLs) or conserving energy or water usage. Fixture monitoring techniques generally fall into two categories: fixture recognition and fixture disaggregation. However, existing techniques require users to explicitly identify each individual...
This paper introduces a novel technique to track structures in time evolving graphs. The method is based on a parameter free approach for three-dimensional co-clustering of the source vertices, the target vertices and the time. All these features are simultaneously segmented in order to build time segments and clusters of vertices whose edge distributions are similar and evolve in the same way over...
This paper focuses on the research of image segmentation accuracy problem because traditional Sobel operator image segmentation is easy to cause the vagueness of image segmentation, contrast is not apparent, segmentation accuracy is low. Directed against these defects, this paper puts forward an improved Sobel operator 2-d maximum entropy digital image segmentation method. This algorithm firstly carries...
To solve the mis-clusters caused by the traditional information cut algorithm when it is applied to segment images with gray changes, modified information cut in wavelet domain (W-MIC) algorithm is proposed. First, using the gray relevance and space relevance between image pixels, a modified information cut (MIC) is presented, which utilizes a new Parzen windowing function to evaluate probability...
Harris corner detection is a widely used corner detection algorithm, however deficiencies still exist: the detected corners are clustered and anti-interference ability is poor. In this paper, a method based on image compression and block processing is proposed to solve these deficiencies. It reduces the clustering phenomenon, increases the detection accuracy, and improves processing speed greatly...
In this article we present a new approach to extract points that belongs to several ellipses or circles presented on a same image and with the presence of outliers. Each geometric form is extracted by means of a robust fitting, that is a nonlinear optimization problem, solved with two different heuristics: differential evolution and RANSAC. Once the geometric form is fitted, its points are extracted...
Image edge detection plays an important role in the system of computer vision. Wavelet is a powerful tool in image processing and has wide application to edge detection for its multiscale characteristic. Based on wavelet modulus maximum edge detection algorithm, an improved method is proposed in this paper, which gives an automatic determination function of eliminateing noise threshold by using the...
Unsharp masking is a popular and simple technique for contrast enhancement and sharpening in digital images. The basic idea in this technique is to emphasize edges and discontinuities in the image by adding the edge information back to the original image. In order to achieve higher contrast level in the processed image, the edge information can be scaled prior addition to the original image. However,...
In this paper, we propose an improved Kernel-based Fuzzy C-means Algorithm (iKFCM) with spatial information to reduce the effect of noise for brain MR image segmentation. We use k-nearest neighbour model and a neighbourhood controlling factor by estimating image contextual constraints to optimize the objective function of conventional KFCM method. Conventional KFCM algorithms classify each pixel in...
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.
To quickly detect the defection of moving object, base on the experiments and analysis of the advantages and disadvantages of the classical operator, a new detection method of SUSAN Operator and K-means clustering algorithm fusion is presented in this paper. This method integrates the advantages of the high precision of edge detection of the SUSAN Operator and the accurate online detection of K-Means...
Multiscale error diffusion (MED) is superior to conventional error diffusion algorithm as it can eliminate directional hysteresis completely and possesses good blue noise characteristics. However, due to its filter design, it is not suitable for printing processes which suffer instable dot generation and large dot gain. This paper presents a feature preserving multiscale error diffusion algorithm...
The clustering method “Fuzzy-C-Means” (FCM) is widely used in image segmentation. However, the major drawback of this method is its sensitivity to the noise. In this paper, we propose a variant of this method which aims at resolving this problem. Our approach is based on an adaptive distance which is calculated according to the spatial position of the pixel in the image. The obtained results have...
We propose a novel image database categorization approach using a possibilistic clustering algorithm. The proposed algorithm is based on a robust data modeling using the Generalized Dirichlet (GD) finite mixture and generates two types of membership degrees. The first one is a posterior probability that indicates the degree to which the point fits the estimated distribution. The second membership...
A number of binarization techniques have been proposed in the past for automatic document processing. Although some studies have aimed to evaluate the performance of binarization algorithms, there is no automatic system that is capable of selecting the most appropriate method of binarization. While preprocessing techniques can be applied, binarization is essential to extract the objects in the first...
On the basis of analyzing the blur images with noise, this paper presents a new segmentation method which is based on the morphology method, fuzzy K-means algorithm and some parts operator of the Canny algorithm. Because of the Canny's good performance on good detection, good localization and only one response to a single edge, we introduce the course of Canny operator that calculating the value and...
Clustering spatial data is a well-known problem that has been extensively studied. Grouping similar data in large 2-dimensional spaces to find hidden patterns or meaningful sub-groups has many applications such as satellite imagery, geographic information systems, medical image analysis, marketing, computer visions, etc. Spatial clustering has been an active research area in spatial data mining (SDM)...
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