- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Reduction of False Microaneurysms in Retinal Fundus Images using Fuzzy C-Means Clustering in terms NLM Anisotropic Filter
摘要： The identification of MAs is an important phase in the research and grading of suffering from diabetes retinopathy. We present clustering strategy to identify the microaneurysms from the optic disk and cup in the retinal fundus pictures. Fuzzy C-Means (FCM) Clustering is used for clustering the information in which the information factors are grouped with different account level. The first and major phase is preprocessing function, in which the optic cup and disk of the feedback picture is being turned. Originally the optic hard disk is turned in some position and the range between the information factors is calculated and a group is established in accordance with the centroid. For retrieving micro aneurysms in all retinal images in our previous work we used SVM Classification filter in Fuzzy C-Means Clustering. In this paper we propose an effective filtering technique for micro aneurysms detection in retinal image preprocessing. Instead of SVM Filtering in terms of technique we used NLM Anisotropic Filter to process retinal images. Tested on the various simulated retina data repositories combining rotation and scaling, the developed method presents good results and shows robustness to rotations and scale changes.
关键词： Biomedical image processing,pattern recognition,Fuzzy C Means Clustering,Fundus Image,image classification,Anisotropic Diffusion Filter,medical decision-making,Non-Local Methodologies,Spatial Information
更新于2025-09-09 09:28:46
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Possibilistic Clustering Algorithm Incorporating Grey-Level Histogram and Spatial Information for Image Segmentation
摘要： Image segmentation is a process of segmenting an image into non-intersecting regions containing homogeneous pixels that are inhomogeneous with those in other adjacent regions. In this paper, a possibilistic clustering algorithm incorporating grey-level histogram and spatial information (PCA_HS) for image segmentation is proposed. The grey-level histogram speeds up the algorithm and the spatial information enhances its robustness to noise and outliers. To assess the proposed algorithm, four widely used validity indexes are computed and discussed. As the experimental quantitative and qualitative results on real images with and without noise show, PCA_HS can preserve the homogeneity and integrality of the regions and hence is more effective and efficient than traditional PCA.
关键词： grey-level histogram,Image segmentation,possibilistic clustering,spatial information
更新于2025-09-09 09:28:46
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Integrating WLI fuzzy clustering with grey neural network for missing data imputation
摘要： This paper proposes a novel approach, grey neural network (GNN) that is composed of Levenberg-Marquardt neural network and grey wolf optimiser. The WLI fuzzy clustering mechanism predicts the data by clustering the data into groups, and the neural network trains the missing attribute in the dataset. The Levenberg-Marquardt neural network is trained based on the grey wolf optimiser that determines the optimal weight. Finally, the two imputed values are combined significantly to impute the data where the missing data occurs. Experimentation using the medical dataset proves the accuracy of the proposed hybrid model and the results of the proposed GNN are compared with the existing methods like KNN, WLI and GWLMN. The proposed method exhibits a good efficiency with minimum values of MSE and RMSE compared to the existing methods. This method also attains a minimum RMSE of 0.11 which ensures the efficient data imputation.
关键词： neural network,grey wolf optimiser,data imputation,missing data,WLI fuzzy clustering
更新于2025-09-09 09:28:46
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Image noise reduction based on adaptive thresholding and clustering
摘要： In this paper, we present a novel image denoising method based on adaptive thresholding and k-means clustering. In this method, we adopt the adaptive thresholding technique as an alternative to the traditional hard-thresholding of the block-matching and 3D filtering (BM3D) method. This technique has a high capacity to adapt and change according to the amount of the noise. More precisely, in our method the soft-thresholding is applied to the areas with heavy noise, on the contrary the hard-thresholding is applied to the areas with slight noise. Based on the adaptation and stability of the adaptive thresholding, we can achieve optimal noise reduction and maintain the high spatial frequency detail (e.g. sharp edges). Owing to the capacity of k-means clustering in terms of finding the relevant candidate-blocks, we adopt this clustering at the last estimate to partition the denoised image into several regions and identify the boundaries between these regions. Applying k-means clustering will allow us to force the block matching to search within the region of the reference block, which in turn will lead to minimize the risk of finding poor matching. The main reason of applying the K-means clustering method on the denoised image and not on the noised image is specifically due to the flaw of accuracy in detecting edges in the noisy image. Experimental results demonstrate that the new algorithm consistently outperforms other reference methods in terms of visual quality, Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Furthermore, in the proposed algorithm the time consumption of the image denoising is less than that in the other reference algorithms.
关键词： Candidate-blocks,Block matching,Adaptive thresholding,Hard-thresholding,Reference-blocks,K-means clustering,Soft-thresholding
更新于2025-09-09 09:28:46
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Visual Saliency Analysis for Common Region of Interest Detection in Multiple Remote Sensing Images
摘要： Saliency detection is an effective tool to extract regions of interest (ROIs) from remote sensing images. However, some existing saliency detection models focus on extracting ROI from a single image, which cannot accurately detect ROI against complex background interference. In this paper, a novel visual saliency analysis and ROI extraction model is proposed to effectively extract common ROIs from remote sensing images and exclude images without ROIs. Firstly, the single saliency maps are generated by frequency-tuned (FT) method. Secondly, the cluster method based on synthesized features is proposed to group regions with similar feature into a cluster for multiple images. Thirdly, computing the mean of saliency value as the cluster saliency suppresses the saliency value of non-common ROIs. Finally, a ROI extraction method based on the maximum saliency value is proposed to extract ROIs while eliminating the image without ROIs. Experimental results indicate our model outperforms other state-of-the-art saliency detection models, achieving highest ROC and maximal PRF values.
关键词： saliency analysis,region of interest,feature clustering,Image processing,remote sensing
更新于2025-09-09 09:28:46
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New Algorithms for Energy-Efficient VLC Networks with User-Centric Cell Formation
摘要： The ever-increasing demand in high data rate has pushed the attention of the researchers to utilize the unregulated visible light spectrum for communication. This paper proposes a joint access-point (AP) association and power allocation algorithms for energy-ef?ciency (EE) maximization in visible light communication (VLC) networks. Based on the user-centric (UC) design, we ?rst show that the cell formation and power allocation are interlinked problems and should be treated jointly. We start by proposing a new algorithm for users’ clustering and then associating all the APs to the clustered users based on a proposed metric. We then propose two algorithms that jointly allocate the power, under quality-of-service (QoS) constraints, and decide which APs must be prevented from participating in communication. The ?rst algorithm is designed to maximize the EE, while the other algorithm is designed to reduce the complexity of the ?rst algorithm with acceptable degradation in the EE. Different from the related literature that allocated the power with the worst case interference information, we propose an iterative algorithm that allocates the power based on exact interference information, which signi?cantly improves the EE. The numerical results demonstrate that the proposed algorithms signi?cantly improve the EE compared to the existing work.
关键词： power allocation,energy ef?ciency,Visible light communication,user clustering
更新于2025-09-09 09:28:46
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Accurate and Scalable Image Clustering Based On Sparse Representation of Camera Fingerprint
摘要： Clustering images according to their acquisition devices is a well-known problem in multimedia forensics, which is typically faced by means of camera Sensor Pattern Noise (SPN). Such an issue is challenging since SPN is a noise-like signal, hard to be estimated and easy to be attenuated or destroyed by many factors. Moreover, the high dimensionality of SPN hinders large-scale applications. Existing approaches are typically based on the correlation among SPNs in the pixel domain, which might not be able to capture intrinsic data structure in union of vector subspaces. In this paper, we propose an accurate clustering framework, which exploits linear dependencies among SPNs in their intrinsic vector subspaces. Such dependencies are encoded under sparse representations which are obtained by solving a LASSO problem with non-negativity constraint. The proposed framework is highly accurate in number of clusters estimation and image association. Moreover, our framework is scalable to the number of images and robust against double JPEG compression as well as the presence of outliers, owning big potential for real-world applications. Experimental results on Dresden and Vision database show that our proposed framework can adapt well to both medium-scale and large-scale contexts, and outperforms state-of-the-art methods.
关键词： sparse subspace clustering,sensor pattern noise,divide-and-conquer,Image clustering
更新于2025-09-04 15:30:14
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[ACM Press the 2nd International Conference - Sydney, NSW, Australia (2018.10.06-2018.10.08)] Proceedings of the 2nd International Conference on Graphics and Signal Processing - ICGSP'18 - A Comparative Analysis of Clustering Algorithms for Ultrasound Image Despeckling Applications
摘要： This paper proposes a novel framework for speckle noise suppression and edge preservation using clustering algorithms in ultrasound images. The algorithms considered are K-means clustering, fuzzy C-means clustering, possibilistic C-means, fuzzy possibilistic C-means, and possibilistic fuzzy C-means clustering. This work presents an exhaustive comparative analysis of the above clustering algorithms to consider their suitability for despeckling and identifies the best clustering algorithm. Two types of dataset are considered: medical ultrasound images of the thyroid, and synthetically modelled ultrasound images. The framework consists of several distinct phases - first the edges of the image are identified using the Canny edge operator, and then a clustering algorithm applied on high frequency coefficients extracted using wavelet transform. Finally, the preserved edges are added back to speckle suppressed image. Thus, the proposed clustering method effectively accomplishes both speckle suppression and edge preservation. This paper also presents a quantitative evaluation of results to demonstrate the effectiveness of the clustering approach.
关键词： speckle noise,Image quality metrics,Wavelet transform,Ultrasound image analysis,Canny edge detector,Clustering algorithms
更新于2025-09-04 15:30:14
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Unmanned Aerial Vehicle Recognition Based on Clustering by Fast Search and Find of Density Peaks (CFSFDP) with Polarimetric Decomposition
摘要： Unmanned aerial vehicles (UAV) have become vital targets in civilian and military fields. However, the polarization characteristics are rarely studied. This paper studies the polarization property of UAVs via the fusion of three polarimetric decomposition methods. A novel algorithm is presented to classify and recognize UAVs automatically which includes a clustering method proposed in "Science", one of the top journals in academia. Firstly, the selection of the imaging algorithm ensures the quality of the radar images. Secondly, local geometrical structures of UAVs can be extracted based on Pauli, Krogager, and Cameron polarimetric decomposition. Finally, the proposed algorithm with clustering by fast search and find of density peaks (CFSFDP) has been demonstrated to be better than the original methods under the various noise conditions with the fusion of three polarimetric decomposition methods.
关键词： synthetic aperture radar (SAR),unmanned aerial vehicle,inverse synthetic aperture radar (ISAR),man-made targets,clustering methods,polarimetric decomposition
更新于2025-09-04 15:30:14
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Clusters partition and zonal voltage regulation for distribution networks with high penetration of PVs
摘要： Integration of distributed generation (DG) at large scale with high penetration challenges the radial structure of the traditional distribution networks and the effectiveness of the conventional voltage regulation methods. In this study, the clusters partitioning and voltage regulation are researched. The modified electrical distance is introduced. An effective method, based on spectral clustering algorithm, is proposed for the partitioning of the DG network via the judgement of critical load buses. Two-stage voltage regulation optimisation is realised in each sub-community. The optimal objects are the minimal voltage fluctuation and the network loss of the distributed network. The independent variables are reactive-power absorption and active-power curtailment for each controllable photovoltaic node. An advanced particle swarm optimisation algorithm is applied to the voltage regulation for the sub-communities. After a case study of the IEEE 33-bus system, a regional distribution network in Anhui province of China is analysed. Simulation results indicate that the node voltages are stabilised with the improvement of power quality employing the proposed clusters partitioning method and zonal power control scheme.
关键词： spectral clustering,particle swarm optimisation,voltage regulation,distributed generation,photovoltaic
更新于2025-09-04 15:30:14