37 resultados para Brand Recognition


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Correspondence analysis is introduced in the brand associationliterature as an alternative tool to measure dominance, for theparticular case of free choice data. The method is also used to analysedifferences, or asymmetries, between brand-attribute associations whereattributes are associated with evoked brands, and brand-attributeassociations where brands are associated with the attributes. Anapplication to a sample of deodorants is used to illustrate the proposedmethodology.

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This paper studies the interaction between ownership structure, taken as a proxy for shareholders commitment, and customer satisfaction - the main driver of consumer loyalty - and their impact on a firm s brand equity. The results show that customer satisfaction has a positive direct effect on brand equity but an indirect negative one because of reductions in ownership concentration. This latter effect emerges when managers are mainly customer-oriented. Such result gives out a warning signal that highlights the perverse effect of implementing policies, focused excessively on satisfying customers at the expense of shareholders, on a firm s brand equity. The empirical analysis uses an incomplete panel data comprising 69 firms from 11 nations, for the period 2002-2005.

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In this paper we argue that corporate social responsibility (CSR) to various stakeholders(customers, shareholders, employees, suppliers, and community) has a positive effect on globalbrand equity (BE). In addition, policies aimed at satisfying community interests help reinforcecredibility to social responsible polices with other stakeholders. We test these theoreticalcontentions using panel data comprised of 57 global brands originating from 10 countries (USA,Japan, South Korea, France, UK, Italy, Germany, Finland, Switzerland and the Netherlands) forthe period 2002 to 2008. Our findings show that CSR to each of the stakeholder groups has apositive impact on global BE. In addition, global brands that follow local social responsibilitypolicies over communities obtain strong positive benefits in terms of the generation of BE, as itenhances the positive effects of CSR to other stakeholders, particularly to customers. Therefore,for managers of global brands it is particularly productive for generating brand value to combineglobal strategies with the satisfaction of the interests of local communities.

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Several features that can be extracted from digital images of the sky and that can be useful for cloud-type classification of such images are presented. Some features are statistical measurements of image texture, some are based on the Fourier transform of the image and, finally, others are computed from the image where cloudy pixels are distinguished from clear-sky pixels. The use of the most suitable features in an automatic classification algorithm is also shown and discussed. Both the features and the classifier are developed over images taken by two different camera devices, namely, a total sky imager (TSI) and a whole sky imager (WSC), which are placed in two different areas of the world (Toowoomba, Australia; and Girona, Spain, respectively). The performance of the classifier is assessed by comparing its image classification with an a priori classification carried out by visual inspection of more than 200 images from each camera. The index of agreement is 76% when five different sky conditions are considered: clear, low cumuliform clouds, stratiform clouds (overcast), cirriform clouds, and mottled clouds (altocumulus, cirrocumulus). Discussion on the future directions of this research is also presented, regarding both the use of other features and the use of other classification techniques

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One of the most important problems in optical pattern recognition by correlation is the appearance of sidelobes in the correlation plane, which causes false alarms. We present a method that eliminate sidelobes of up to a given height if certain conditions are satisfied. The method can be applied to any generalized synthetic discriminant function filter and is capable of rejecting lateral peaks that are even higher than the central correlation. Satisfactory results were obtained in both computer simulations and optical implementation.

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Biometric system performance can be improved by means of data fusion. Several kinds of information can be fused in order to obtain a more accurate classification (identification or verification) of an input sample. In this paper we present a method for computing the weights in a weighted sum fusion for score combinations, by means of a likelihood model. The maximum likelihood estimation is set as a linear programming problem. The scores are derived from a GMM classifier working on a different feature extractor. Our experimental results assesed the robustness of the system in front a changes on time (different sessions) and robustness in front a change of microphone. The improvements obtained were significantly better (error bars of two standard deviations) than a uniform weighted sum or a uniform weighted product or the best single classifier. The proposed method scales computationaly with the number of scores to be fussioned as the simplex method for linear programming.

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In this paper we propose the inversion of nonlinear distortions in order to improve the recognition rates of a speaker recognizer system. We study the effect of saturations on the test signals, trying to take into account real situations where the training material has been recorded in a controlled situation but the testing signals present some mismatch with the input signal level (saturations). The experimental results for speaker recognition shows that a combination of several strategies can improve the recognition rates with saturated test sentences from 80% to 89.39%, while the results with clean speech (without saturation) is 87.76% for one microphone, and for speaker identification can reduce the minimum detection cost function with saturated test sentences from 6.42% to 4.15%, while the results with clean speech (without saturation) is 5.74% for one microphone and 7.02% for the other one.

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Within the context of rising competition between territories, identity has become the most important element of recognition, differentiation and commodification in the communicative process within which cities, regions and countries position themselves. Geographical spaces thus compete in terms of this identity, which is then subjected to fierce comparison and competition (Nogué, 1999; Anholt, 2007a). The territorial brand thus entails the reinvention of places through a process of brand construction (branding) based on the promotion of the individual and collective identities of geographical spaces; these identities, in turn, are imbued with the intangible factors associated with their respective territorial identities.

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In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used, and Principal Component Analysis (PCA) is applied in order to study which is the best number of components for the classification task, implemented by means of a Support Vector Machine (SVM) System. Obtained results are satisfactory, and compared with [4] our system improves the recognition success, diminishing the variance at the same time.

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In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used to characterize the leaves. Independent Component Analysis (ICA) is then applied in order to study which is the best number of components to be considered for the classification task, implemented by means of an Artificial Neural Network (ANN). Obtained results with ICA as a pre-processing tool are satisfactory, and compared with some references our system improves the recognition success up to 80.8% depending on the number of considered independent components.

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In this work we explore the multivariate empirical mode decomposition combined with a Neural Network classifier as technique for face recognition tasks. Images are simultaneously decomposed by means of EMD and then the distance between the modes of the image and the modes of the representative image of each class is calculated using three different distance measures. Then, a neural network is trained using 10- fold cross validation in order to derive a classifier. Preliminary results (over 98 % of classification rate) are satisfactory and will justify a deep investigation on how to apply mEMD for face recognition.

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In this paper we propose the inversion of nonlinear distortions in order to improve the recognition rates of a speaker recognizer system. We study the effect of saturations on the test signals, trying to take into account real situations where the training material has been recorded in a controlled situation but the testing signals present some mismatch with the input signal level (saturations). The experimental results shows that a combination of several strategies can improve the recognition rates with saturated test sentences from 80% to 89.39%, while the results with clean speech (without saturation) is 87.76% for one microphone.

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The design and synthesis of two Janus-type heterocycles with the capacity to simultaneously recognize guanine and uracyl in G-U mismatched pairs through complementary hydrogen bond pairing is described. Both compounds were conveniently functionalized with a carboxylic function and efficiently attached to a tripeptide sequence by using solid-phase methodologies. Ligands based on the derivatization of such Janus compounds with a small aminoglycoside, neamine, and its guanidinylated analogue have been synthesized, and their interaction with Tau RNA has been investigated by using several biophysical techniques, including UV-monitored melting curves, fluorescence titration experiments, and 1H NMR. The overall results indicated that Janus-neamine/guanidinoneamine showed some preference for the +3 mutated RNA sequence associated with the development of some tauopathies, although preliminary NMR studies have not confirmed binding to G-U pairs. Moreover, a good correlation has been found between the RNA binding affinity of such Janus-containing ligands and their ability to stabilize this secondary structure upon complexation.

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El present projecte analitza la creació i el desenvolupament dels conceptes que donen forma a les marques des d'un punt de vista creatiu. El treball proposa l'estudi del concepte de les brand idees com a resposta a l'evolució que han seguit les marques per aconseguir comunicar la seva essència. La finalitat de l'escrit és disseccionar les dues parts principals del procés creatiu d'una marca, les big ideas i les brand ideas, per trobar la forma més adequada de transmetre l'essència de les marques durant un recorregut de diversos anys. Un escenari que ens ensenyarà si aquest procés és tan nou com es creu, si realment les marques apliquen aquestes teories i els diferents elements a tenir en compte per fer créixer una marca des del punt de vista de la comunicació creativa.