Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
In this paper, we propose an automatic image thresholding method based on an index of fuzziness and a fuzzy similarity measure. This work aims at overcoming the limitation of the existing method which is semi-supervised. Using an index of fuzziness, two initial regions of gray levels located at the boundaries of the histogram are defined based on the fuzzy region. Then the threshold point is found...
An Accurate, Fast and Noise-Adaptive segmentation of Brain MR Images for clinical Analysis is a challenging problem. An improved Hybrid Clustering Algorithm is presented here, which integrates the concept of recently popularized Rough Sets and that of Fuzzy Sets. The concept of lower and upper approximations of rough sets is incorporated to handle uncertainty, vagueness, and incompleteness in class...
Aiming at threshold uncertainty caused by fuzziness of image for image segmentation, an adaptive thresholding method for gray-level image segmentation using type-II fuzzy sets is proposed . Fuzzy index is got using type-II fuzzy sets technique, that can overcome effectively the uncertainty, histogram peaks value of an image can be find out automatically based on maximum Mahalanobis distance and minimum...
The objective of this research was to develop a method for segmenting rice seedlings from background in a multispectral image of paddy field. Multispectral images of paddy field were taken by a high resolution multispectral camera. The DVI (Difference vegetation index) image which could reduce the image noises was suitable for seedlings recognition by analyzing spectral characteristics of the objects...
This paper addresses the problem of region-based color image segmentation using a fuzzy clustering algorithm, e.g. a spatial version of fuzzy c-means, in order to partition the image into clusters corresponding to homogeneous regions. We propose to determine the optimal number of clusters, and so the number of regions, by using a new cluster validity index computed on fuzzy partitions. Experimental...
This paper presents a kernel-based fuzzy c-means algorithm with partition index maximization, called KPIM algorithm. The proposed KPIM algorithm is more robust than the partition index maximization algorithm proposed by Özdemir and Akarum. Experiments show that the advantage of KPIM are robust properties: (1) robust to fuzziness parameter m, (2) robust to outlier, (3) robust to image artifacts; and...
This papers introduces a new family of iris encoders which use 2-dimensional Haar Wavelet Transform for noise attenuation, and Hilbert Transform to encode the iris texture. In order to prove the usefulness of the newly proposed iris encoding approach, the recognition results obtained by using these new encoders are compared to those obtained using the classical Log-Gabor iris encoder. Twelve tests...
In this paper, a new dynamic clustering approach based on the harmony search algorithm (HS) called DCHS is proposed. In this algorithm, the capability of standard HS is modified to automatically evolve the appropriate number of clusters as well as the locations of cluster centers. By incorporating the concept of variable length in each harmony memory vector, DCHS is able to encode variable numbers...
Image segmentation is considered as one of the crucial steps in image analysis process and it is the most challenging task. Image segmentation can be modeled as a clustering problem. Therefore, clustering algorithms have been applied successfully in image segmentation problems. Fuzzy c-mean (FCM) algorithm is considered as one of the most popular clustering algorithm. Even that, FCM can generate a...
We propose a new approach to tackle the well known fuzzy c-means (FCM) initialization problem. Our approach uses a metaheuristic search method called Harmony Search (HS) algorithm to produce near-optimal initial cluster centers for the FCM algorithm. We then demonstrate the effectiveness of our approach in a MRI segmentation problem. In order to dramatically reduce the computation time to find near-optimal...
In this work, image segmentation is addressed as the starting point within a motion analysis methodology intended for biomechanics behavior characterization. First, we propose a general segmentation framework that uses Atanassov's intuitionistic fuzzy sets (A-IFSs) to determine the optimal image threshold value. Atanassov's intuitionistic fuzzy index values are used for representing the unknowledge/ignorance...
The Probabilistic Index Map (PIM) model was originally proposed for video processing to extract background of video frames. In this paper, we introduce the PIM model for texture segmentation. We first extract texture features based on Laws and Gabor filters respectively. Then we present a fuzzy k-means method to generate the index map and palette, and use the PIM model to improve the segmentation...
This article describes a multiobjective genetic fuzzy clustering scheme that utilizes the search capabilities of NSGA-II, a popular multiobjective genetic algorithm and optimizes a number of fuzzy cluster validity measures. Real-coded encoding of the cluster centers is used for this purpose. The multiobjective clustering scheme produces a number of non-dominated solutions, each of which contains some...
Spatial information is a crucial aspect of image processing, computer vision and structural recognition for modeling context as well as resolving the uncertainties caused by the ambiguities in low-level features. This calls for the framework of fuzzy sets, which exhibits nice features to represent spatial imprecision, and which provides powerful tools for fusion, decision-making and reasoning. In...
We have designed a novel query retrieval scheme for the information just in time (iJIT) system to retrieve handwritten annotations from digital documents based on typed/handwritten query. The two key components of the developed query retrieval system (QRS) are the character recognition engine and the query retrieval engine. The character recognition engine uses Tesseract 2.01 open source Optical Character...
The article compares three methods for segmentation of environmental images. Hue and saturation values of the image pixels were used as the input values for the clustering. The methods that have been examined are K-medoid, fuzzy Cmeans and Gustafson-Kessel. Results of the fuzzy clustering methods were compared with the results obtained with method using the mean-shift algorithm.
In this paper, an improved fuzzy C-means clustering (IFCM) algorithm for color image segmentation is proposed to solve the problem of heavy calculating burden and the disadvantage that clustering performance is affected by initial cluster centers for FCM, which is simple and easy to implement in color Image segmentation. For one thing, the quick subtractive clustering (QSC) is used for getting initial...
The problem of segmentation in spite of all the work over the last decades is still an important research field. Moreover, during the past years, fuzzy logic theory has been successfully applied to image thresholding. Considering that for segmentation purposes, in most cases, image pixels have an inherent ambiguity in the predicate that they must fulfill to belong to an object, which results in the...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.