Optimization of Classified Satellite Images using DWT and Fuzzy Logic

The image taken by a satellite can be enhanced in terms of its resolution based on the interpolation can be obtained by DWT. Using DWT, the image at the input is divided into several sub bands and the speckle noise is also removed. Thereafter, the high-level images and low-level image at the input can be combined, to produce a better image applying IDWT. An intermediate stage for approximating high level is proposed here. The variation in detection approaches for SAR images are done by using image fusion strategy and novel fuzzy clustering algorithm. To retrieve an enhanced image, wavelet fusion directives are considered to combine the wavelet coefficients. A fuzzy C-means algorithm is proposed for identifying the altered and unaltered regions in the combined difference image

Image Segmentation based on Energy Fitting Models – A Review

As a result of changes in imaging technology, segmenting the area of interest (ROI) from medical images is an extremely important yet challenging task. It is still difficult for the global energy-based active contour model (ACM) to properly extract the ROI from medical images, despite the fact that many techniques based on the local region-based active contour model have been proposed to deal with intensity inhomogeneity. This brief study aims to assess the performance of current techniques that have been published in the recent years and have been used to image segmentation. The methods under consideration include the various energy fitting models that have been created to drive the active contour are highlighted in this review study. Each model was examined against a medical image, an MRI brain image, and an image that was not taken by a medical professional. According to the results of the comparison study, it can be determined which technique is better appropriate for image segmentation even when there is intensity inhomogeneity in the images

Nonlinear Spectral Unmixing using Semi-Supervised Standard Fuzzy Clustering

Coarse resolution captured in remote sensing causes the combination of different materials in one pixel, called the mixed pixel. Spectral unmixing estimates the combination of endmembers in mixed pixels and their corresponding abundance maps in the Hyper/Multi spectral image. In this paper, a nonlinear spectral unmixing based on semi-supervised fuzzy clustering is proposed. First, pure pixels (endmembers) using Vertex Component Analysis (VCA) are extracted and those pixels are the labelled pixels where the membership value of each is 1 for the corresponding endmember and 0 for the others. Second, the semi-supervised fuzzy clustering is applied to find the membership matrix defining the fraction of the endmember in each mixed pixel and hence extract the abundance maps. The experiments were conducted on both synthetic data such as the Legendre data and real data such as Jasper Ridge data. The non-linearity of the Legendre data was performed by the Fan model on different signal-tonoise ratio values. The results of the new unmixing model show its significant performance when compared with four state-of the art unmixing algorithm

New Method to Optimize Initial Point Values of Spatial Fuzzy c-means Algorithm

Fuzzy based segmentation algorithms are known to be performing well on medical images. Spatial fuzzy C-means (SFCM) is broadly used for medical image segmentation but it suffers from optimum selection of seed point initialization which is done either manually or randomly. In this paper, an enhanced SFCM algorithm is proposed by optimizing the SFCM initial point values. In this method in order to increasing the algorithm speed first the approximate initial values are determined by calculating the histogram of the original image. Then by utilizing the GWO algorithm the optimum initial values could be achieved. Finally By using the achieved initial values, the proposed method shows the significant improvement in segmentation results. Also the proposed method performs faster than previous algorithm i.e. SFCM and has better convergence. Moreover, it has noticeably improved the clustering effect

Analysis of Segmentation Parameters Effect towards Parallel Processing Time on Fuzzy C Means Algorithm

Fuzzy C Means algorithm or FCM is one of many clustering algorithms that has better accuracy to solve problems related to segmentation. Its application is almost in every aspects of life and many disciplines of science. However, this algorithm has some shortcomings, one of them is the large amount of processing time consumption. This research conducted mainly to do an analysis about the effect of segmentation parameters towards processing time in sequential and parallel. The other goal is to reduce the processing time of segmentation process using parallel approach. Parallel processing applied on Nvidia GeForce GT540M GPU using CUDA v8.0 framework. The experiment conducted on natural RGB color image sized 256x256 and 512x512. The settings of segmentation parameter values were done as follows, weight in range (2-3), number of iteration (50-150), number of cluster (2-8), and error tolerance or epsilon (0.1 – 1e-06). The results obtained by this research as follows, parallel processing time is faster 4.5 times than sequential time with similarity level of image segmentations generated both of processing types is 100%. The influence of segmentation parameter values towards processing times in sequential and parallel can be concluded as follows, the greater value of weight parameter then the sequential processing time becomes short, however it has no effects on parallel processing time. For iteration and cluster parameters, the greater their values will make processing time consuming in sequential and parallel become large. Meanwhile the epsilon parameter has no effect or has an unpredictable tendency on both of processing time

A Prototype-Based Modified DBSCAN for Gene Clustering

AbstractIn this paper, we propose, a novel DBSCAN method to cluster the gene expression data. The main problem of DBSCAN is its quadratic computational complexity. We resolve this drawback by using the prototypes produced from a squared error clustering method such as K-means. Then, the DBSCAN technique is applied efficiently using these prototypes. In our algorithm, during the iterations of DBSCAN, if a point from an uncovered prototype is assigned to a cluster, then all the other points of such prototype belongs to the same cluster. We have carried out excessive experiments on various two dimensional artificial and multi-dimensional biological data. The proposed technique is compared with few existing techniques. It is observed that proposed algorithm outperforms the existing methods

PENGARUH PENGAPLIKASIAN MASKER “ACTIVATED CHARCOAL” UNTUK MENGURANGI KADAR SEBUM PADA KULIT WAJAH BERMINYAK

Abstrak Masker adalah sediaan kosmetik untuk perawatan kulit wajah yang memiliki banyak manfaat. Masker alami berbahan dasar arang aktif atau ‘Activated Charcoal’ bermanfaat untuk mengatasi permasalahan kulit seperti mengurangi minyak, kotoran di wajah serta mengatasi kulit wajah berjerawat. Tujuan penelitian ini adalah untuk mengetahui pengaruh pengaplikasian masker “activated charcoal” untuk mengurangi kadar sebum pada jenis kulit wajah berminyak. Jenis penelitian ini adalah penelitian eksperimen. Variabel bebas dalam penelitian ini adalah pengaplikasian masker arang aktif “activated charcoal” dengan perlakuan X1(satu kali treatment dalam satu minggu), X2(dua kali treatment dalam satu minggu), dan X3(tiga kali treatment dalam satu minggu). Variabel terikat dari penelitian ini adalah reaksi kulit, kelembutan/kehalusan kulit, kondisi kulit, penurunan kadar minyak. Pengumpulan data dilakukan dengan cara observasi yang melibatkan 15 orang responden. Analisis data penelitian ini menggunakan Anava one way yang dilanjutkan dengan Uji Duncan menggunakan program SPSS versi 21. Hasil penelitian menunjukkan bahwa masker yang memiliki pengaruh terbaik adalah masker X3 dengan proporsi 6 gram bubuk masker “activated charcoal” dengan pengaruh pada wajah yaitu 1) kadar sebum pada kulit wajah berminyak mengalami penurunan, 2) kondisi kulit wajah mengalami perubahan menjadi lebih lembab dan halus. Kata Kunci: perawatan kulit, kulit berminyak, “activated charcoal