Fuzzy image segmentation using location and intensity information

The segmentation results of any clustering algorithm are very sensitive to the features used in the similarity measure and the object types, which reduce the generalization capability of the algorithm. The previously developed algorithm called image segmentation using fuzzy clustering incorporating spatial information (FCSI) merged the independently segmented results generated by fuzzy clustering-based on pixel intensity and pixel location. The main disadvantages of this algorithm are that a perceptually selected threshold does not consider any semantic information and also produces unpredictable segmentation results for objects (regions) covering the entire image. This paper directly addresses these issues by introducing a new algorithm called fuzzy image segmentation using location and intensity (FSLI) by modifying the original FCSI algorithm. It considers the topological feature namely, connectivity and the similarity based on pixel intensity and surface variation. Qualitative and quantitative results confirm the considerable improvements achieved using the FSLI algorithm compared with FCSI and the fuzzy c-means (FCM) algorithm for all three alternatives, namely clustering using only pixel intensity, pixel location and a combination of the two, for a range of sample of images

Fuzzy image segmentation of generic shaped clusters

The segmentation performance of any clustering algorithm is very sensitive to the features in an image, which ultimately restricts their generalisation capability. This limitation was the primary motivation in our investigation into using shape information to improve the generality of these algorithms. Fuzzy shape-based clustering techniques already consider ring and elliptical profiles in segmentation, though most real objects are neither ring nor elliptically shaped. This paper addresses this issue by introducing a new shape-based algorithm called fuzzy image segmentation of generic shaped clusters (FISG) that incorporates generic shape information into the framework of the fuzzy c-means (FCM) algorithm. Both qualitative and quantitative analyses confirm the superiority of FISG compared to other shape-based fuzzy clustering methods including, Gustafson-Kessel algorithm, ring-shaped, circular shell, c-ellipsoidal shells and elliptic ring-shaped clusters. The new algorithm has also been shown to be application independent so it can be applied in areas such as video object plane segmentation in MPEG-4 based coding

Fuzzy rule for image segmentation incorporating texture features

The generic fuzzy rule-based image segmentation algorithm (GFRIS) does not produce good results for images containing non-homogeneous regions, as it does not directly consider texture. In this paper a new algorithm called fuzzy rules for image segmentation incorporating texture features (FRIST) is proposed, which includes two additional membership functions to those already defined in GFRIS. FRIST incorporates the fractal dimension and contrast features of a texture by considering image domain specific information. Quantitative evaluation of the performance of FRIST is discussed and contrasted with GFRIS using one of the standard segmentation evaluation methods. Overall, FRIST exhibits considerable improvement in the results obtained compared with the GFRIS approach for many different image types

A fuzzy rule-based colour image segmentation algorithm

Most fuzzy rule-based image segmentation techniques to date have been primarily developed for gray level images. In this paper, a new algorithm called fuzzy rule-based colour image segmentation (FRCIS) is proposed by extending the generic fuzzy rule-based image segmentation (GFFUS) algorithm G.C. Karmakar, L.S. Dooley [2002] and integrating a novel algorithm for averaging hue angles. Qualitative and quantitative analysis of the performance of FRCIS is examined and contrasted with the popular fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms for both the hue-saturation-value (HSV) and RGB colour models. Overall, FRCIS provides considerable improvement for many different image types

Fuzzy image segmentation combining ring and elliptic shaped clustering algorithms

Results from any existing clustering algorithm that are used for segmentation are highly sensitive to features that limit their generalization. Shape is one important attribute of an object. The detection and separation of an object using fuzzy ring-shaped clustering (FKR) and elliptic ring-shaped clustering (FKE) already exists in the literature. Not all real objects however, are ring or elliptical in shape, so to address these issues, this paper introduces a new shape-based algorithm, called fuzzy image segmentation combining ring and elliptic shaped clustering algorithms (FCRE) by merging the initial segmented results produced by FKR and FKE. The distribution of unclassified pixels is performed by connectedness and fuzzy c-means (FCM) using a combination of pixel intensity and normalized pixel location. Both qualitative and quantitative analysis of the results for different varieties of images proves the superiority of the proposed FCRE algorithm compared with both FKR and FKE

Fuzzy image segmentation using shape information

Results of any clustering algorithm are highly sensitive to features that limit their generalization and hence provide a strong motivation to integrate shape information into the algorithm. Existing fuzzy shape-based clustering algorithms consider only circular and elliptical shape information and consequently do not segment well, arbitrary shaped objects. To address this issue, this paper introduces a new shape-based algorithm, called fuzzy image segmentation using shape information (FISS) by incorporating general shape information. Both qualitative and quantitative analysis proves the superiority of the new FISS algorithm compared to other well-established shape-based fuzzy clustering algorithms, including Gustafson-Kessel, ring-shaped, circular shell, c-ellipsoidal shells and elliptic ring-shaped clusters

Passive source localization using power spectral analysis and decision fusion in wireless distributed sensor networks

Source localization is a challenging issue for multisensor multitarget detection, tracking and estimation problems in wireless distributed sensor networks. In this paper, a novel source localization method, called passive source localization using power spectral analysis and decision fusion in wireless distributed sensor networks is presented. This includes an energy decay model for acoustic signals. The new method is computationally efficient and requires less bandwidth compared with current methods by making localization decisions at individual nodes and performing decision fusion at the manager node. This eliminates the requirement of sophisticated synchronization. A simulation of the proposed method is performed using different numbers of sources and sensor nodes. Simulation results confirmed the improved performance of this method under ideal and noisy conditions

Fuzzy image segmentation considering surface characteristics and feature set selection strategy

The image segmentation performance of any clustering algorithm is sensitive to the features used and the types of object in an image, both of which compromise the overall generality of the algorithm. This paper proposes a novel fuzzy image segmentation considering surface characteristics and feature set selection strategy (FISFS) algorithm which addresses these issues. Features that are exploited when the initially segmented results from a clustering algorithm are subsequently merged include connectedness, object surface characteristics and the arbitrariness of the fuzzy c-means (FCM) algorithm for pixel location. A perceptual threshold is also integrated within the region merging strategy. Qualitative and quantitative results are presented, together with a full time-complexity analysis, to confirm the superior performance of FISFS compared with FCM, possibilistic c-means (PCM), and suppressed FCM (SFCM) clustering algorithms, for a wide range of disparate images

Detection and separation of generic-shaped objects by fuzzy clustering

Purpose - Existing shape-based fuzzy clustering algorithms are all designed to explicitly segment regular geometrically-shaped objects in an image, with the consequence that this restricts their capability to separate arbitrarily-shaped objects. Design/Methodology/Approach – With the aim of separating arbitrary shaped objects in an image, this paper presents a new detection and separation of generic shaped objects (FKG) algorithm that analytically integrates arbitrary shape information into a fuzzy clustering framework, by introducing a shape constraint that preserves the original object shape during iterative scaling. Findings - Both qualitative and numerical empirical results analysis corroborate the improved object segmentation performance achieved by the FKG strategy upon different image types and disparately shaped objects. Originality/Value - The proposed FKG algorithm can be highly used in the applications where object segmentation is necessary. Like this algorithm can be applied in MPEG-4 for real object segmentation that is already applied in synthetic object segmentation

Review on Fuzzy Clustering Algorithms

Image segmentation especially fuzzy-based segmentation techniques are widely used due to effective segmentation performance. For this reason, a number of algorithms are proposed in the literature. This paper presents a survey report of different types of classical fuzzy clustering techniques which are available in the literature