Article
Nonadditive entropy: The concept and its use
Centro Brasileiro de Pesquisas Fisicas and National Institute of Science and Technology for Complex Systems Xavier Sigaud 150 22290180 Rio de JaneiroRJ Brazil; Santa Fe Institute 1399 Hyde Park Road 87501 Santa Fe USA
European Physical Journal A (Impact Factor: 2.04). 40(3):257266. DOI: 10.1140/epja/i2009107990 Source: arXiv

Conference Paper: Detection of skin lesions by fuzzy entropy based texel identification
[Show abstract] [Hide abstract]
ABSTRACT: This paper proposes automated detection of skin lesions by unsupervised feature based clustering based on a new fuzzy entropy function for characterizing texture. The parameterized entropy function is optimized using the Bacterial Foraging algorithm. The clustering of the entropy function of the image is done using the popular Fuzzy Cmeans algorithm (FCM). The experimental results obtained after the clustering process indicate a very good segregation of texture clusters with satisfactory visual results. The results also provide us with the normalized entropy values needed for texel identification.Image and Signal Processing and Analysis, 2009. ISPA 2009. Proceedings of 6th International Symposium on; 10/2009  [Show abstract] [Hide abstract]
ABSTRACT: In this work the nonadditive entropy is examined. It appears in isolated particle systems composed of few components. Therefore, the mixing of isolated particle systems S=S1+S2 has been studied. Two cases are considered T1=T2 and T1\leqT2, where T1,T2 are the initial temperatures of the system S1 and S2 respectively. The concept of similar systems containing interacting particles is introduced. These systems are defined by a common temperature and an identical time evolution process, i.e. the approach to the same thermodynamic equilibrium. The main results are: 1) The properties of the similar particle systems yield the nonadditive entropy and free energy. The Gibbs Paradox is not a paradox. 2) The relation between the initial temperatures T1 and T2 governs the mixing process. 3) In the two cases T1=T2, T1\leqT2 mixing of the systems S1, S2 results in a uniform union system S=S1+S2. The systems S, S1, S2 are similar one to the other. 4) The mixing process is independent of the extensive quantities (volume, particle number, energy) and of the particle type. Only the mean energy plays an important role in the mixing of the systems S1, S2. 5) Mixing in the case T1\leqT2 is in essence a thermalization process, but mixing in the case T1=T2 is not a thermodynamic process. 6)Mixing is an irreversible process. Keywords: Entropy; Similar systems of interacting particles; Mixing of systems; Thermal equilibrium06/2012;  [Show abstract] [Hide abstract]
ABSTRACT: This paper proposes a new probabilistic nonextensive entropy feature for texture characterization, based on a Gaussian information measure. The highlights of the new entropy are that it is bounded by finite limits and that it is non additive in nature. The non additive property of the proposed entropy makes it useful for the representation of information content in the non extensive systems containing some degree of regularity or correlation. The effectiveness of the proposed entropy in representing the correlated random variables is demonstrated by applying it for the texture classification problem since textures found in nature are random and at the same time contain some degree of correlation or regularity at some scale. The gray level cooccurrence probabilities (GLCP) are used for computing the entropy function. The experimental results indicate high degree of the classification accuracy. The performance of the new entropy function is found superior to other forms of entropy such as Shannon, Renyi, Tsallis and Pal and Pal entropies on comparison. Using the feature based polar interaction maps (FBIM) the proposed entropy is shown to be the best measure among the entropies compared for representing the correlated textures.Neurocomputing 11/2013; 120(Image Feature Detection and Description):214225. · 1.63 Impact Factor
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.