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This paper delineates the proprietary contest developed around a highly valued prestige item: a silver roofed tankard owned by a Romanian, Gabor Roma man.

The author applies “methodological fetishism” (Appadurai, 1986, p. 5), the perspective of things-in-motion, as well as the biographic method to interpret data collected during 31.5 months of multi-sited anthropological fieldwork carried out in the Transylvanian Gabor and Cărhar Roma groups.

As the tankard in question crossed the borders of three Transylvanian Roma groups, and thus went through the processes of de- and re-contextualization three times, it is characterized by a transethnic/transcultural biography. This paper pays special attention to the agency associated with the tankard (the social and economic practices, processes and emotions it caused or influenced), the transformations concerning its symbolic properties, and its movement between various social contexts and value regimes. Furthermore, it examines how the analysis of these issues contributes to a deeper understanding of prestige relations and consumption, morality and business ethics, and measures of success in two Transylvanian Roma groups.

This paper reveals how subjects create, manipulate, and represent their identities, and social and economic differences through the construction of commodity biographies and ownership histories interpreted as symbolic pantheons. By combining the terms of Marcus (1995) and Fowles (2006), it argues that analyses based on multi-sited fieldwork focusing on commodities crossing cultural or social boundaries, and their transnational/transcultural biographies, should be defined as multi-sited commodity ethnographies.

This chapter focuses on brokerage in the prestige economy of the Gabor Roma ethnic subgroup in Romania. It argues that the necessity of the brokers’ employment as well as their economic importance comes from the significant uncertainty characterising the prestige-object transactions, that is, these brokers are occasional entrepreneurs specialised in risk management. The theoretical purpose of the chapter is to demonstrate that brokers and goods mediated by them can be associated with more than one socially constructed transactional identity (cultural, political, economic, etc.) at the same time. It also aims to highlight the shifting nature of these identities – they are not constant and context-free qualities, but highly context-sensitive social constructions that can appear in various combinations.

The traditional methods have difficulty to inspection various types of copper strips defects as inclusions, pits and delamination defects under uneven illumination. Therefore, this paper aims to propose an optimal real Gabor filter model for inspection; however, improper selection of Gabor parameters will cause the boundary between the defect and the background image to be not very clear. This will make the defect and the background cannot be completely separated.

The authors proposed an optimal Real Gabor filter model for inspection of copper surface defects under uneven illumination. This proposed method only requires a single filter by calculating the specific convolution energy of the Gabor filter with the image. The Real Gabor filter’s parameter is optimized by particle swarm optimization (PSO), which objective fitness function is maximization of the Gabor filter’s energy average divided by the energy standard deviation, the objective makes a distinction between the defect and normal area.

The authors have verified the effect with different iterations of parameter optimization using PSO, the effects with different control constant of energy and neighborhood window size of real Gabor filter, the experimental results on a number of metal surface have shown the proposed method achieved a well performance in defect recognition of metal surface.

The authors propose a defect detection method based on particle swarm optimization for single Gabor filter parameters optimization. This proposed method only requires a single filter and finds the best parameters of the Gabor filter. By calculating the specific convolution energy of the Gabor filter and the image, to obtain the best Gabor filter parameters and to highlight the defects, the particle swarm optimization algorithm’s fitness objective function is maximize the Gabor filter's average energy divided by the energy standard deviation.

Automatic slub detection is vital in the classification and identification of fabric images. This paper seeks to present a rapid and accurate approach for automatic detection of slub in fabric images using Gabor filters.

Slub can be regarded as defects along weft or warp. Gabor filters as bandpass filters consider the directional characteristics of slub and its frequency spectrum after Fourier transform. Choosing appropriate parameters for Gabor filters, slub can be detected accurately.

The proposed method achieves automatic detection of slub. The experimental results suggest that the authors approach is effective.

This paper considers appropriate parameters to design a Gabor filter for automatic detection of slub. And it is helpful to classify and identify fabric images.

Hyperspectral imaging (HSI) systems are becoming potent technologies for computer vision tasks due to the rich information they uncover, where each substance exhibits a distinct spectral distribution. Although the high spectral dimensionality of the data empowers feature learning, the joint spatial–spectral features have not been well explored yet. Gabor convolutional networks (GCNs) incorporate Gabor filters into a deep convolutional neural network (CNN) to extract discriminative features of different orientations and frequencies. To the best if the authors’ knowledge, this paper introduces the exploitation of GCNs for hyperspectral image classification (HSI-GCN) for the first time. HSI-GCN is able to extract deep joint spatial–spectral features more rapidly and accurately despite the shortage of training samples. The authors thoroughly evaluate the effectiveness of used method on different hyperspectral data sets, where promising results and high classification accuracy have been achieved compared to the previously proposed CNN-based and Gabor-based methods.

The authors have implemented the new algorithm of Gabor convolution network on the hyperspectral images for classification purposes.

Implementing the new GCN has shown unexpectable results with an excellent classification accuracy.

To the best of the authors’ knowledge, this work is the first one that implements this approach.

The purpose of this paper is to review and provide a detailed performance evaluation of a number of texture descriptors that analyse texture at micro‐level such as local binary patterns (LBP) and a number of standard filtering techniques that sample the texture information using either a bank of isotropic filters or Gabor filters.

The experimental tests were conducted on standard databases where the classification results are obtained for single and multiple texture orientations. The authors also analysed the performance of standard filtering texture analysis techniques (such as those based of LM and MR8 filter banks) when applied to the classification of texture images contained in standard Outex and Brodatz databases.

The most important finding resulting from this study is that although the LBP/C and the multi‐channel Gabor filtering techniques approach texture analysis from a different theoretical perspective, in this paper the authors have experimentally demonstrated that they share some common properties in regard to the way they sample the macro and micro properties of the texture.

Texture is a fundamental property of digital images and the development of robust image descriptors plays a crucial role in the process of image segmentation and scene understanding.

This paper contrast, from a practical and theoretical standpoint, the LBP and representative multi‐channel texture analysis approaches and a substantial number of experimental results were provided to evaluate their performance when applied to standard texture databases.

Aims to develop an efficient and robust system for real‐time personal identification by automatic face recognition.

A wavelet‐based image hierarchy and a guided coarse‐to‐fine search scheme are introduced to improve the computation efficiency in the face detection task. In addition, a Gabor‐based low feature dimensional pattern is proposed to deal with the face recognition problem.

The proposal of a wavelet‐based image hierarchy and a guided coarse‐to‐fine search scheme is effective to improve the computation efficiency in the face detection task. The introduction of a low feature dimensional pattern is powerful to cope with the transformed appearance‐based face recognition problem. In addition, the use of aggregated Gabor filter responses to represent face images provides a better solution to face feature extraction.

Provides guidance in the design of automatic face recognition system for real‐time personal identification.

Biometrics recognition has been emerging as a new and effective identification technology that attains certain level of maturity. Among many body characteristics that have been used, face is one of the most commonly used characteristics and has drawn considerably large attentions. An automated system to confirm an individual's identity employing features of face is very attractive in many specialized fields.

Introduces a wavelet‐based image hierarchy and a guided coarse‐to‐fine search scheme to improve the computation efficiency in the face detection task. Introduces a Gabor‐based low feature dimensional pattern to deal with the face recognition problem.

Large amount of data are stored in image format. Image retrieval from bulk databases has become a hot research topic. An alternative method for efficient image retrieval is proposed based on a combination of texture and colour information. The main purpose of this paper is to propose a new content based image retrieval approach using combination of color and texture information in spatial and transform domains jointly.

Various methods are provided for image retrieval, which try to extract the image contents based on texture, colour and shape. The proposed image retrieval method extracts global and local texture and colour information in two spatial and frequency domains. In this way, image is filtered by Gaussian filter, then co-occurrence matrices are made in different directions and the statistical features are extracted. The purpose of this phase is to extract noise-resistant local textures. Then the quantised histogram is produced to extract global colour information in the spatial domain. Also, Gabor filter banks are used to extract local texture features in the frequency domain. After concatenating the extracted features and using the normalised Euclidean criterion, retrieval is performed.

The performance of the proposed method is evaluated based on the precision, recall and run time measures on the Simplicity database. It is compared with many efficient methods of this field. The comparison results showed that the proposed method provides higher precision than many existing methods.

The comparison results showed that the proposed method provides higher precision than many existing methods. Rotation invariant, scale invariant and low sensitivity to noise are some advantages of the proposed method. The run time of the proposed method is within the usual time frame of algorithms in this domain, which indicates that the proposed method can be used online.

“In the provision of terminology and apparatus to aid thought I do not think that Marshall did economists any greater service than by the explicit introduction of the idea of ‘elasticity’.”

Presents here a theoretical model study based on the results of an empirical study, which relates brand shares to price differences in a fully quantitative fashion. Posits in its present form that it is applicable to the analysis of markets largely dominated by two brands. States that the model is simple to use and provides an eminently practical technique for market research and pricing decisions. Uses figures to emphasise the distribution function.