Search Results (37)

The 3D reconstruction of vessel hulls is crucial for enhancing safety, efficiency, and knowledge in the maritime industry. Neural Radiance Fields (NeRFs) are an alternative to 3D reconstruction and rendering from multi-view images; particularly, tensor-based methods have proven effective in improving efficiency. However, existing tensor-based methods typically suffer from a lack of spatial coherence, resulting in gaps in the reconstruction of fine-grained geometric structures. This paper proposes a spatial multi-scale weighted NeRF (MDW-NeRF) for accurate and efficient surface reconstruction of vessel hulls. The proposed method develops a novel multi-scale feature decomposition mechanism that models 3D space by leveraging multi-resolution features, facilitating the integration of high-resolution details with low-resolution regional information. We designed separate color and density weighting, using a coarse-to-fine strategy, for density and a weighted matrix for color to decouple feature vectors from appearance attributes. To boost the efficiency of 3D reconstruction and rendering, we implement a hybrid sampling point strategy for volume rendering, selecting sample points based on volumetric density. Extensive experiments on the SVH dataset confirm MDW-NeRF’s superiority: quantitatively, it outperforms TensoRF by 1.5 dB in PSNR and 6.1% in CD, and shrinks the model size by 9%, with comparable training times; qualitatively, it resolves tensor-based methods’ inherent spatial incoherence and fine-grained gaps, enabling accurate restoration of hull cavities and realistic surface texture rendering. These results validate our method’s effectiveness in achieving excellent rendering quality, high reconstruction accuracy, and timeliness. Full article

Background/Objectives: This study aimed to perform a comparative analysis of the original Viagra^® product and sildenafil-containing tablets obtained from illegal sources (the darknet). Specifically, the analyzed material consisted of samples seized by Polish law enforcement authorities from unverified vendors operating within the Central European darknet market. The study utilized thermal methods, specifically Thermogravimetry (TG), Derivative Thermogravimetry (DTG), and calculated Differential Thermal Analysis (c-DTA), as well as colorimetric analysis based on the International Commission on Illumination (CIE) L*a*b* system. Methods: Thermal analyses enabled the assessment of the thermal stability of the tested samples, identification of characteristic stages of thermal decomposition, and determination of differences in thermal behavior between the pure substance, the original preparation, and darknet samples. In turn, color measurements in the CIE L*a*b* space allowed for an objective comparison of tablet appearance and determination of the degree of color similarity to the original product. Results: The obtained results showed that only a few samples (V1, V3, V4, V6, V8) exhibited features similar to the original Viagra^®, both in terms of thermal profile and color. Most of the tested tablets were characterized by significant variability in physicochemical properties, indicating a lack of quality control and inconsistency in formulation. Samples V2 and V7 deviated particularly strongly—both thermally and visually—suggesting that they might not contain the original active substance or contained it in a different chemical form. Conclusions: The use of combined thermal and colorimetric methods proved to be an effective tool in the identification of counterfeit pharmaceutical products, enabling simultaneous evaluation of their composition and authenticity. The results confirm the validity of employing integrated physicochemical analyses for the detection of falsified medicines present on the illegal market. Full article

Deep neural networks (DNNs) excel in image classification but are vulnerable to backdoor attacks due to reliance on external training data, where specific markers trigger preset misclassifications. Existing attack techniques have an obvious trade-off between the effectiveness of the triggers and the stealthiness, which limits their practical application. For this purpose, in this paper, we develop a method—chromatic channel-based implicit backdoor attack (CCIBA), which combines a discrete wavelet transform (DWT) and singular value decomposition (SVD) to embed triggers in the frequency domain through the chromaticity properties of the YUV color space. Experimental validation on different image datasets shows that compared to existing methods, CCIBA can achieve a higher attack success rate without a large impact on the normal classification ability of the model, and its good stealthiness is verified by manual detection as well as different experimental metrics. It successfully circumvents existing defense methods in terms of sustainability. Overall, CCIBA strikes a balance between covertness, effectiveness, robustness and sustainability. Full article

This survey provides a review of the theoretical research on the classic system of matrix equations $AX=C$ and $XB=D$, which has wide-ranging applications across fields such as control theory, optimization, image processing, and robotics. The paper discusses various solution methods for the system, focusing on specialized approaches, including generalized inverse methods, matrix decomposition techniques, and solutions in the forms of Hermitian, extreme rank, reflexive, and conjugate solutions. Additionally, specialized solving methods for specific algebraic structures, such as Hilbert spaces, Hilbert $C^{\ast }$-modules, and quaternions, are presented. The paper explores the existence conditions and explicit expressions for these solutions, along with examples of their application in color images. Full article

Dynamic and changeable business scenarios pose significant challenges to the adaptability and verifiability of process models. Despite its widespread adoption as an ISO-standard modeling language, Business Process Model and Notation (BPMN) faces inherent limitations in formal semantics and verification capabilities, hindering the mathematical validation of process evolution behaviors under scenario changes. To address these challenges, this paper proposes a collaborative modeling framework integrating BPMN with hierarchical colored Petri nets (HCPNs), enabling the efficient iterative evolution and correctness verification of process change through formal mapping and localized evolution mechanism. First, hierarchical mapping rules are established with subnet-based modular decomposition, transforming BPMN elements into an HCPN executable model and effectively resolving semantic ambiguities; second, atomic evolution operations (addition, deletion, and replacement) are defined to achieve partial HCPN updates, eliminating the computational overhead of global remapping. Furthermore, an automated verification pipeline is constructed by analyzing state spaces, validating critical properties such as deadlock freeness and behavioral reachability. Evaluated through an intelligent AI-driven service scenario involving multi-gateway processes, the framework demonstrates behavioral effectiveness. This work provides a pragmatic solution for scenario-driven process evolution in domains requiring agile iteration, such as fintech and smart manufacturing. Full article

Underwater image enhancement (UIE) is inherently challenging due to complex degradation effects such as light absorption and scattering, which result in color distortion and a loss of fine details. Most existing methods focus on spatial-domain processing, often neglecting the frequency-domain characteristics that are crucial for effectively restoring textures and edges. In this paper, we propose a novel UIE framework, the Wavelet-based Enhancement Diffusion Model (WEDM), which integrates frequency-domain decomposition with diffusion models. The WEDM consists of two main modules: the Wavelet Color Compensation Module (WCCM) for color correction in the LAB space using discrete wavelet transform, and the Wavelet Diffusion Module (WDM), which replaces traditional convolutions with wavelet-based operations to preserve multi-scale frequency features. By combining residual denoising diffusion with frequency-specific processing, the WEDM effectively reduces noise amplification and high-frequency blurring. Ablation studies further demonstrate the essential roles of the WCCM and WDM in improving color fidelity and texture details. Our framework offers a robust solution for underwater visual tasks, with promising applications in marine exploration and ecological monitoring. Full article

Wireless capsule endoscopy (WCE) offers a non-invasive diagnostic alternative for the gastrointestinal tract using a battery-powered capsule. Despite advantages, WCE encounters issues with video quality and diagnostic accuracy, often resulting in missing rates of 1–20%. These challenges stem from weak texture characteristics due to non-Lambertian tissue reflections, uneven illumination, and the necessity of color fidelity. Traditional Retinex-based methods used for image enhancement are suboptimal for endoscopy, as they frequently compromise anatomical detail while distorting color. To address these limitations, we introduce QRNet, a novel quaternion-based Retinex framework. QRNet performs image decomposition into reflectance and illumination components within hypercomplex space, maintaining inter-channel relationships that preserve color fidelity. A quaternion wavelet attention mechanism refines essential features while suppressing noise, balancing enhancement and fidelity through an innovative loss function. Experiments on Kvasir-Capsule and Red Lesion Endoscopy datasets demonstrate notable improvements in metrics such as PSNR (+2.3 dB), SSIM (+0.089), and LPIPS (−0.126). Moreover, lesion segmentation accuracy increases by up to 5%, indicating the framework’s potential for improving early-stage lesion detection. Ablation studies highlight the quaternion representation’s pivotal role in maintaining color consistency, confirming the promise of this advanced approach for clinical settings. Full article

Underwater image enhancement (UIE) serves as a fundamental preprocessing step in ocean remote sensing applications, encompassing marine life detection, archaeological surveying, and subsea resource exploration. However, UIE encounters substantial technical challenges due to the intricate physics of underwater light propagation and the inherent homogeneity of aquatic environments. Images captured underwater are significantly degraded through wavelength-dependent absorption and scattering processes, resulting in color distortion, contrast degradation, and illumination irregularities. To address these challenges, we propose a contrastive feature disentanglement network (CFD-Net) that systematically addresses underwater image degradation. Our framework employs a multi-stream decomposition architecture with three specialized decoders to disentangle the latent feature space into components associated with degradation and those representing high-quality features. We incorporate hierarchical contrastive learning mechanisms to establish clear relationships between standard and degraded feature spaces, emphasizing intra-layer similarity and inter-layer exclusivity. Through the synergistic utilization of internal feature consistency and cross-component distinctiveness, our framework achieves robust feature extraction without explicit supervision. Compared to existing methods, our approach achieves a 12% higher UIQM score on the EUVP dataset and outperforms other state-of-the-art techniques on various evaluation metrics such as UCIQE, MUSIQ, and NIQE, both quantitatively and qualitatively. Full article

In marine remote sensing, underwater images play an indispensable role in ocean exploration, owing to their richness in information and intuitiveness. However, underwater images often encounter issues such as color shifts, loss of detail, and reduced clarity, leading to the decline of image quality. Therefore, it is critical to study precise and efficient methods for assessing underwater image quality. A no-reference multi-space feature fusion and entropy-based metrics for underwater image quality assessment (MFEM-UIQA) are proposed in this paper. Considering the color shifts of underwater images, the chrominance difference map is created from the chrominance space and statistical features are extracted. Moreover, considering the information representation capability of entropy, entropy-based multi-channel mutual information features are extracted to further characterize chrominance features. For the luminance space features, contrast features from luminance images based on gamma correction and luminance uniformity features are extracted. In addition, logarithmic Gabor filtering is applied to the luminance space images for subband decomposition and entropy-based mutual information of subbands is captured. Furthermore, underwater image noise features, multi-channel dispersion information, and visibility features are extracted to jointly represent the perceptual features. The experiments demonstrate that the proposed MFEM-UIQA surpasses the state-of-the-art methods. Full article

In this study, biodegradable and active films based on sodium alginate incorporated with different concentrations of oils (25% and 50%) from fruit seeds were developed for potential applications in food packaging. The ultraviolet and visible (UV-VIS) spectra of raspberry seed oil (RSO) and black currant seed oil (BCSO) indicated differences in bioactive compounds, such as tocopherols, phenolic compounds, carotenoids, chlorophyll, and oxidative status (amounts of dienes, trienes, and tetraenes) of active components added to alginate films. The study encompassed the color, structure, and thermal stability analysis of sodium alginate films incorporated with RSO and BCSO and their mixtures. The color of alginate films before and after the addition of oils from both fruit seeds was evaluated by measuring color coordinates in the CIELab color space: L* (lightness), a* (red-green), and b* (yellow-blue). The lightness values ranged between 94.21 and 95.08, and the redness values varied from −2.20 to −2.65, slightly decreasing for the films enriched with oils. In contrast, yellowness values ranged between 2.93 and 5.80 for the obtained active materials, significantly increasing compared to the control alginate film (L* = 95.48, a* = −1.92, and b* = −0.14). Changes in the structure and morphology of the alginate films after incorporating bioactive-rich oils were observed using scanning electron microscopy (SEM). Films with RSO and oil mixtures had more developed surfaces than films with BCSO. Moreover, the cross-sections of the films with RSO showed holes evenly distributed inside the films, indicating traces of volatile compounds. Thermal decomposition of the alginate films loaded with oils showed five separate stages (to 125 °C, 125–300 °C, 310–410 °C, 410–510 °C, and 750–1000 °C, respectively) related to the oil and surfactant decomposition. The shape of the thermogravimetric curves did not depend on the oil type. The added oils reduced the efficiency of alginate decomposition in the first stage. The obtained results showed that new functional and thermally stable food packaging films based on sodium alginate with a visual appearance acceptable to consumers could be produced by utilizing oils from fruit seed residues. Full article

The rapid development of artificial intelligence and remote sensing technologies is indispensable for modern agriculture. In orchard environments, challenges such as varying light conditions and shading complicate the tasks of intelligent picking robots. To enhance the recognition accuracy and efficiency of apple-picking robots, this study aimed to achieve high detection accuracy in complex orchard environments while reducing model computation and time consumption. This study utilized the CenterNet neural network as the detection framework, introducing gray-centered RGB color space vertical decomposition maps and employing grouped convolutions and depth-separable convolutions to design a lightweight feature extraction network, Light-Weight Net, comprising eight bottleneck structures. Based on the recognition results, the 3D coordinates of the picking point were determined within the camera coordinate system by using the transformation relationship between the image’s physical coordinate system and the camera coordinate system, along with depth map distance information of the depth map. Experimental results obtained using a testbed with an orchard-picking robot indicated that the proposed model achieved an average precision (AP) of 96.80% on the test set, with real-time performance of 18.91 frames per second (FPS) and a model size of only 17.56 MB. In addition, the root-mean-square error of positioning accuracy in the orchard test was 4.405 mm, satisfying the high-precision positioning requirements of the picking robot vision system in complex orchard environments. Full article

In the field of image watermarking technology, it is very important to balance imperceptibility, robustness and embedding capacity. In order to solve this key problem, this paper proposes a new color image adaptive watermarking scheme based on discrete wavelet transform (DWT), discrete cosine transform (DCT) and singular value decomposition (SVD). In order to improve the security of the watermark, we use Lorenz hyperchaotic mapping to encrypt the watermark image. We adaptively determine the embedding factor by calculating the Bhattacharyya distance between the cover image and the watermark image, and combine the Alpha blending technique to embed the watermark image into the Y component of the YCbCr color space to enhance the imperceptibility of the algorithm. The experimental results show that the average PSNR of our scheme is 45.9382 dB, and the SSIM is 0.9986. Through a large number of experimental results and comparative analysis, it shows that the scheme has good imperceptibility and robustness, indicating that we have achieved a good balance between imperceptibility, robustness and embedding capacity. Full article

Fruit cracking and rust spots are common diseases of nectarines that seriously affect their yield and quality. Therefore, it is essential to construct fast and accurate disease-identification models for agricultural products. In this paper, a sparse dictionary learning method was proposed to realize the rapid and nondestructive identification of nectarine disease based on multiple color features combined with improved LK-SVD (Label K-Singular Value Decomposition). According to the color characteristics of the nectarine itself and the significant color differences existing in the three categories of nectarine (diseased, normal, and background parts), multiple color spaces of RGB, HSV, Lab, and YCbCr were studied. It was concluded that the G channel in RGB space, Y channel in YCbCr space, and L channel in Lab space can better distinguish the diseased part from the other parts. At the model-training stage, pixels of the diseased, normal, and background parts in the nectarine image were randomly selected as the initial training sets, and then, the neighboring image blocks of the pixels were selected to construct the feature vectors based on the above color space channels. An improved LK-SVD dictionary learning algorithm was proposed that integrated the category label into the process of dictionary learning, and thus, an over-complete feature dictionary with significant discrimination was obtained. At the model-testing stage, the orthogonal matching pursuit (OMP) algorithm was used for sparse reconstruction of the original data, which can obtain the classification categories based on the optimized feature dictionary. The experimental results show that the sparse dictionary learning method based on multi-color features combined with improved LK-SVD can identify fruit cracking and rust spot diseases of nectarines quickly and accurately, and the average overall classification accuracies were 92.06% and 88.98%, respectively, which were better than those of k-nearest neighbor (KNN), support vector machine (SVM), DeepLabV3+, and Unet++; the identification results of DeepLabV3+ and Unet++ were also relatively high, but their average time costs were much higher, requiring 126.46~265.65 s. It is demonstrated that this study can provide technical support for disease identification in agricultural products. Full article

The pink teeth phenomenon has occasionally been observed in forensic autopsies. This study aimed to establish an experimental pink tooth model and an objective color tone evaluation method in order to clarify changes in the color tone of teeth and the relationship with hemoglobin monoxide and its decomposition products and with red pigment-producing bacteria, under various external environmental factors. It was confirmed that the color tone evaluation with ΔE and the L*C*h color space was useful. The results of various examinations using this model showed that color development was suppressed under aerobic conditions, faded early under light, became bright red under a low temperature and showed a tendency to be reddish at 3 days under high humidity and in the presence of soft tissue. The biochemical analysis revealed a significant increase in carboxyhemoglobin at 7 days and a tendency toward increasing the total heme pigment and bilirubin levels over time. The bacteriological analysis revealed that red pigment-producing bacteria increased over time but that the color faded after 7 days. These results suggest that putrefaction greatly affects the pink teeth phenomenon, whereas red pigment-producing bacteria have little effect on the occurrence of pink teeth. However, further studies are needed to clarify bacteriological involvement. Full article

The rock-salt ordered A₂CuWO₆ (A = Sr, Ba) with I4/m space group and disordered SrCu_0.5M_0.5O_3−δ (M = Ta, Nb) with Pm3m space group perovskites were successfully obtained via a solid-state reaction route. Heat treatment of Ba₂CuWO₆ over 900 °C in air leads to phase decomposition to the barium tungstate and copper oxide. Thermogravimetric measurements reveal the strong stoichiometric oxygen content and specific oxygen capacity (ΔW_o) exceeding 2.5% for Ba₂CuWO₆. At the same time, oxygen content reveals Cu³⁺ content in SrCu_0.5Ta_0.5O_3−δ. Under the following reoxidation of Ba₂CuWO₆, step-like behavior in weight changes was observed, corresponding to possible Cu⁺ ion formation at 900 °C; in contrast, no similar effect was detected for M⁵⁺ cations. The yellow color of Ba₂CuWO₆ enables to measure the band gap 2.59 eV. SrCu_0.5Ta_0.5O_3−δ due to high oxygen valance concentration has a low thermal conductivity 1.28 W·m⁻¹·K⁻¹ in the temperature range 25–400 °C. Full article