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Light field images (LFIs) have gained popularity as a technology to increase the field of view (FoV) of plenoptic cameras since they can capture information about light rays with a large FoV. Wide FoV causes light field (LF) data to increase rapidly, which restricts the use of LF imaging in image processing, visual analysis and user interface. Effective LFI coding methods become of paramount importance. This paper aims to eliminate more redundancy by exploring sparsity and correlation in the angular domain of LFIs, as well as mitigate the loss of perceptual quality of LFIs caused by encoding.

This work proposes a new efficient LF coding framework. On the coding side, a new sampling scheme and a hierarchical prediction structure are used to eliminate redundancy in the LFI's angular and spatial domains. At the decoding side, high-quality dense LF is reconstructed using a view synthesis method based on the residual channel attention network (RCAN).

In three different LF datasets, our proposed coding framework not only reduces the transmitted bit rate but also maintains a higher view quality than the current more advanced methods.

(1) A new sampling scheme is designed to synthesize high-quality LFIs while better ensuring LF angular domain sparsity. (2) To further eliminate redundancy in the spatial domain, new ranking schemes and hierarchical prediction structures are designed. (3) A synthetic network based on RCAN and a novel loss function is designed to mitigate the perceptual quality loss due to the coding process.

Single-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.

To address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.

Empirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.

This paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.

The study aims to assess the efficiency of nanocomposite to improve the properties of gap-filling materials for pottery artifacts.

Five different pastes were used in the laboratory studies. The pastes consist mainly of pottery powder (grog), dental plaster, microballoons and an adhesive of Primal AC33, nano-silica and nano kaolinite in various concentrations. The prepared samples were subjected to accelerated heat and light aging. Besides, some investigations were used to evaluate the efficacy of the additive nanomaterials, such as TEM, digital and scanning electron microscopy microscopes. Contact angle, color change, shrinkage degree, physical properties and compressive strength tests were also conducted.

The results indicated that using Nano-silica considerably improves the mechanical strength and decreases the shrinkage of gap-filling materials. According to the results, a mixture of grog, microballoons and Primal AC33/Nano-silica Nanocomposites is the optimal gap-filling paste for archaeological pottery. Moreover, this paste showed a higher contact angle (120°), lower color change (ΔE = 2.62), lower shrinkage (3.3%), lower water absorption (3.36%), lower porosity (5.05%) and higher compressive strength (5124 N/mm²).

This paper attains to develop an economic polymer-nanocomposite that can be used with gap-filling materials for pottery artifacts.

This study aims to offer an effective nanocomposite for potential use to consolidate and protect deteriorated archaeological pottery.

Three nanocomposites were used in the experimental study. This study used nano Primal AC33, silicon dioxide (SiO₂) and montmorillonite (MMT) nanoparticles to protect and consolidate pottery specimens. Pottery specimens were made at 800°C for this investigation. Consolidation materials were applied with a brush. The properties of the treated pottery specimens were assessed using several methods such as digital and scanning electron microscopes, static water contact angle, color alteration, physical properties and compressive strength.

Microscopic examination indicated the ability of the nano Primal AC33/MMT nanocomposites to cover the outer surface well and bind the inner granules. Concerning specimens with code F treated with nano Primal AC33 5%/MMT 3% nanocomposites, it achieved an increase in contact angle (120°), density (1.23 g/cm³) and compressive strength (561 kg/cm²), as well as a decrease in color change (ΔE = 2.62), water absorption (4.45%) and porosity (5.46%). The novelty of the results is due to the characteristics of nano Primal AC33 5%/MMT 3% nanocomposites used in the current study.

This study describes the significant results of the analytical methods used for evaluating consolidation materials used in this study. The findings offer useful information for the protection of archaeological pottery. The investigation indicated that nano Primal AC33 5%/MMT 3% nanocomposites gave the best results. Therefore, it is recommended to use this nanocomposite to consolidate archaeological pottery. As a result, the current work provides a promising first step in conserving archaeological pottery for future studies.