Search results for "MeaNS"

Dimensionality Reduction Techniques: An Operational Comparison On Multispectral Satellite Images Using Unsupervised Clustering

2006

Multispectral satellite imagery provides us with useful but redundant datasets. Using Dimensionality Reduction (DR) algorithms, these datasets can be made easier to explore and to use. We present in this study an objective comparison of five DR methods, by evaluating their capacity to provide a usable input to the K-means clustering algorithm. We also suggest a method to automatically find a suitable number of classes K, using objective "cluster validity indexes" over a range of values for K. Ten Landsat images have been processed, yielding a classification rate in the 70-80% range. Our results also show that classical linear methods, though slightly outperformed by more recent nonlinear al…

Comparing normal means: new methods for an old problem

2007

Comparing the means of two normal populations is an old problem in mathematical statistics, but there is still no consensus about its most appropriate solution. In this paper we treat the problem of comparing two normal means as a Bayesian decision problem with only two alternatives: either to accept the hypothesis that the two means are equal, or to conclude that the observed data are, under the assumed model, incompatible with that hypothesis. The combined use of an information-theory based loss function, the intrinsic discrepancy (Bernardo and Rueda 2002}, and an objective prior function, the reference prior \citep{Bernardo 1979; Berger and Bernardo 1992), produces a new solution to this…

Fuzzy technique for microcalcifications clustering in digital mammograms

2012

Abstract Background Mammography has established itself as the most efficient technique for the identification of the pathological breast lesions. Among the various types of lesions, microcalcifications are the most difficult to identify since they are quite small (0.1-1.0 mm) and often poorly contrasted against an images background. Within this context, the Computer Aided Detection (CAD) systems could turn out to be very useful in breast cancer control. Methods In this paper we present a potentially powerful microcalcifications cluster enhancement method applicable to digital mammograms. The segmentation phase employs a form filter, obtained from LoG filter, to overcome the dependence from …

Forms and Functions of the Real Estate Market of Palermo (Italy). Science and Knowledge in the Cluster Analysis Approach

2016

The analysis of the housing market of a city requires suitable approaches and tools, such as data mining models, to represent its complexity which derives on many elements, e.g. the type of capital asset-house is a common good and an investment good as well, the heterogeneity of the urban areas—each of them has own historical and representative values and different urban functions—and the variability of building quality. The housing market of the most densely populated area of Palermo (Italy), corresponding to ten districts, is analyzed to verify the degree of its inner homogeneity and the relations between the quality of the characteristics and the price of the properties. Five hundred set…

Mākslinieciskās izteiksmes līdzekļi vieglo automašīnu reklāmās

2016

Reklāmām ir nepieciešams būt pievilcīgām un pārliecinošām, ņemot vērā, ka to mērķis ir piesaistīt mērķauditorijas uzmanību un pārliecināt iegādāties produktu. Tādējādi par šī bakalaura darba centrālo pētījuma objektu kļūst reklāmu ekspresivitāte, kura tiek sasniegta, izmantojot tekstveida un attēla atveidojuma aspektus. Bakalaura darbā analizē, kā “Volkswagen” vieglo automašīnu drukātās reklāmas ir veidotas no tekstveida un attēla atveidojuma rakursa. Pētījuma korpuss sastāv no 41 drukātās vieglo automašīnu reklāmas, kuras aptver laika periodu 2000.- 2015. gads. Pētījuma teorētiskā bāze ir balstīta uz Leech un Short (1981) un Bergera (2011) raksta un reklāmu analīzes metodēm. Izvēlēto reklā…

Multispectral image denoising with optimized vector non-local mean filter

2016

Nowadays, many applications rely on images of high quality to ensure good performance in conducting their tasks. However, noise goes against this objective as it is an unavoidable issue in most applications. Therefore, it is essential to develop techniques to attenuate the impact of noise, while maintaining the integrity of relevant information in images. We propose in this work to extend the application of the Non-Local Means filter (NLM) to the vector case and apply it for denoising multispectral images. The objective is to benefit from the additional information brought by multispectral imaging systems. The NLM filter exploits the redundancy of information in an image to remove noise. A …

Fast PET Scan Tumor Segmentation Using Superpixels, Principal Component Analysis and K-Means Clustering

2018

Positron Emission Tomography scan images are extensively used in radiotherapy planning, clinical diagnosis, assessment of growth and treatment of a tumor. These all rely on fidelity and speed of detection and delineation algorithm. Despite intensive research, segmentation remained a challenging problem due to the diverse image content, resolution, shape, and noise. This paper presents a fast positron emission tomography tumor segmentation method in which superpixels are extracted first from the input image. Principal component analysis is then applied on the superpixels and also on their average. Distance vector of each superpixel from the average is computed in principal components coordin…

Human, Technologies and Quality of Education: Proceedings of Scientific Papers, 2019

2019

Distance-constrained data clustering by combined k-means algorithms and opinion dynamics filters

2014

Data clustering algorithms represent mechanisms for partitioning huge arrays of multidimensional data into groups with small in–group and large out–group distances. Most of the existing algorithms fail when a lower bound for the distance among cluster centroids is specified, while this type of constraint can be of help in obtaining a better clustering. Traditional approaches require that the desired number of clusters are specified a priori, which requires either a subjective decision or global meta–information knowledge that is not easily obtainable. In this paper, an extension of the standard data clustering problem is addressed, including additional constraints on the cluster centroid di…

Empirical Likelihood-Based ANOVA for Trimmed Means

2016

In this paper, we introduce an alternative to Yuen’s test for the comparison of several population trimmed means. This nonparametric ANOVA type test is based on the empirical likelihood (EL) approach and extends the results for one population trimmed mean from Qin and Tsao (2002). The results of our simulation study indicate that for skewed distributions, with and without variance heterogeneity, Yuen’s test performs better than the new EL ANOVA test for trimmed means with respect to control over the probability of a type I error. This finding is in contrast with our simulation results for the comparison of means, where the EL ANOVA test for means performs better than Welch’s heteroscedastic…