The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
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.
The following topics are dealt with: linear approximation; license plate recognition; color image segmentation; image quantization; wireless video transmission; congestion control; stochastic search; transmembrane helical segments; wavelet transform; semisupervised cluster algorithm; anomaly detection; data privacy; online market information processing; user behavior; particle swarm optimization;...
The selection of the clustering parameter based on k-means plays an important part in the cell image segmentation. By combination different clustered image's color information entropy calculation with original image, it can gain the optimal clustering number for color cell image segmentation. It also introduces a clustering number k-related mutual information and mutual information error calculation...
Video segmentation is a crucial pass to content-based video summarization and retrieval. In this paper, we present a practical method to efficiently group video content into semantic segments. First we detect shots with double-threshold method to find raw shots quickly, followed by redundant frames removal though spatial color distribution to get the key frames. Finally, we cluster the key frames...
In the paper, we formulate a new energy function followed by the use of graph cuts to refine the disparity map which takes segment as node. Firstly, the robust disparity plane fitting is modeled and the method of Singular Value Decomposition (SVD) is used to solve least square. In order to ensure reliable pixel sets for the segment, we filter out outliers through three main rules, namely; cross-checking,...
K-Means algorithm is an unsupervised clustering algorithm that classifies the input data points into multiple classes based on their inherent distance from each other. Success of k-means color image segmentation depends on parameter k. If numbers of clusters are estimated correctly, k-means image segmentation can provide good results. This paper proposes a novel method based on edge detection to estimate...
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 this paper, a new scalable clustering method named “APANC” (Affinity Propagation And Normalized Cut) is proposed. During the APANC process, we firstly use the “Affinity Propagation” (AP) to preliminarily group the original data in order to reduce the data-scale, and then we further group the result of AP using “Normalized Cut” (NC) to get the final result. Through such combination, the advantages...
Detection of brain tumors from MRI is a time consuming and error-prone task. This is due to the diversity in shape, size and appearance of the tumors. In this paper, we propose a clustering algorithm based on Particle Swarm Optimization (PSO). The algorithm finds the centroids of number of clusters, where each cluster groups together brain tumor patterns, obtained from MR Images. The results obtained...
This paper addresses a novel issue of intuitionistic fuzzy c means color clustering using intuitionistic fuzzy set theory. The intuitionistic fuzzy set theory takes into the membership degree and non membership degree. Non membership degree is calculated from Sugeno type intuitionistic fuzzy complement. The introduction of another uncertainty term i.e. the non membership degree helps to converge the...
It has been well documented that radiologists' performance is not perfect: they make both false positive and false negative decisions. For example, approximately thirty percent of early lung cancer is missed on chest radiographs when the evidence is clearly visible in retrospect. Currently computer-aided detection (CAD) uses software, designed to reduce errors by drawing radiologists' attention to...
Perceptual information is quickly gaining importance in mesh representation, analysis and rendering. User studies, eye tracking and other techniques are able to provide ever more useful insights for many user-centric systems, which form the bulk of computer graphics applications. In this work we build upon the concept of Mesh Saliency - an automatic measure of visual importance for triangle meshes...
In this paper, it is described a new unsupervised approach based on wavelet packet transform for texture images segmentation. This transform is able to decompose an image not only from the low frequency parts, but also from the middle-high frequency parts, in which there is a certain amount of texture information. After the extraction of the features, a clustering is carried out, by using an immune-inspired...
Several general-purpose algorithms and techniques have been developed for image segmentation. Since there is no general solution to the image segmentation problem, these techniques often have to be combined with domain knowledge in order to effectively solve an image segmentation problem for a problem domain. This paper presents a comparative study of the basic image segmentation techniques i.e. edge-based,...
This paper presents a novel content-based hidden transmission method for secret data to improve the security and secrecy. In the proposed method, the secret data is encrypted by chaotic map before embedding. Then the cover image is segmented by watershed algorithm and fuzzy c-means clustering. At last we extract the feature of each region and embed the secret data into the cover image according to...
To locate the object accurately in a scene for further vision processing, a novel approach for figure-ground segmentation is proposed, which combines the normalized-cut method (Ncut) and top-down method inspired by the trickle-up and trickle-down processing in primate visual pathways. Firstly, as the trickle-up stage, the Ncut method groups the pixels into multiple partitions based on the global criterion,...
For conveniently navigating and editing the news programs, it is very important to segment the video into meaningful units. The effective indexing of news videos can be fulfilled by the anchorperson shot because it is an indicator which denotes the occurrence of upcoming news stories. The paper presents a novel anchorperson detection algorithm based on spatio-temporal slice (STS). With STSpattern...
Proximity-based, or pairwise, data clustering techniques are gaining increasing popularity due to their versatility and their ability to easily integrate information of different nature. Despite this, most applications to image segmentation incorporate only region-based information, mainly color and texture similarity. In this paper we propose a general approach for integrating boundary information...
Over the last years computer vision researchers have shown great interest for the so called spectral clustering, where the data are clustered analysing the first few eigenvectors (i.e., the ones relative to the first eigenvalues) of a the Laplacian matrix, derived directly from the data-set. Note that for the purpose of data clustering the eigenvectors need not to be determined accurately. When clustering...
We present a generative model to perform cosegmentation on an arbitrary number of images, where cosegmentation has been defined as the task of segmenting simultaneously the common parts between a pair of images. We build upon a previous work that introduced a new approach to model-based clustering under prior knowledge, and exploit its simplicity and flexibility to solve the problem of cosegmentation...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.