Color Information Research Articles

Carbon Dots-Inked Paper with Single/Two-Photon Excited Dual-Mode Thermochromic Afterglow for Advanced Dynamic Information Encryption.

Achieving thermochromic afterglow (TCAG) in a single material for advanced information encryption remains a significant challenge. Herein, TCAG in carbon dots (CDs)-inked paper (CDs@Paper) was achieved by tuning the temperature-dependent dual-mode afterglow of room-temperature phosphorescence (RTP) and thermally activated delayed fluorescence (TADF). The CDs were synthesized through thermal treatment of levofloxacin in melting boric acid with post-purification via dialysis. CDs@Paper exhibited TCAG and excitation-dependent afterglow color properties. The TCAG of CDs@Paper exhibited dynamic color changes from blue at high temperatures to yellow at low temperatures by adjusting the proportion of the temperature-dependent TADF and phosphorescence. Notably, two-photon afterglow in CDs-based afterglow materials and time-dependent two-photon afterglow colors were achieved for the first time. Moreover, leveraging the opposite emission responses of phosphorescence and TADF to temperature, CDs@Paper demonstrated TCAG with temperature-sensing capabilities across a wide temperature range. Furthermore, a CDs@Paper-based 3D code containing color and temperature information was successfully developed for advanced dynamic information encryption. This article is protected by copyright. All rights reserved.

A Straightforward Camera Calibration Method Based on a Single Low-cost Cubical Target

Abstract. Calibrating optical sensors with common targets facilitates the efficient and convenient acquisition of the sensor's internal parameters. In this paper, we present a new method of camera calibration utilizing a low-cost foamy cube, in a form of dice, which is based on the fact that arrangement of pip and cubical die surfaces is mutually orthogonal. Initially, each face and pips are identified through the color information on the die’s surfaces. Subsequently, the centers of pips are corrected using a circular projection model, and radial distortion coefficients are estimated based on centers’ one-to-one correspondences. After that, the tangent information between pairs of pips on orthogonal dice faces are utilized to compute vanishing points, leading to estimation of intrinsic parameters. Experimental results demonstrate that our method has similar effects compared to well-known checkerboard calibration method, reaching an average relative error of 2.43%, simplifying the calibration process in practical applications and showcasing good practicality and robustness.

Biomimetic Photonic Elastomer Exhibiting Stress/Moisture Reconfigurable Wrinkle-Lattice for Reversible Deformation Information Storage.

Photonic elastomers, capable of converting imperceptible deformations into visible colors, show significant potential in smart materials. However, instantaneous deformation is arduous to record accurately due to the disappearance of optical information after deformation recovery. Herein, inspired by the folding structures of iridocytes in cephalopods, a stress- and moisture-triggered wrinkling and erasure effect is proposed to be introduced in the construction of a photonic elastomer. Implemented in a dual-network polymer framework with modulatable locking, it allows for reversible deformation storage. The photonic elastomer comprises a surface one-dimensional photonic crystal (1DPC) and a poly(dimethylsiloxane) (PDMS) substrate. The deformed 1DPC lattice transforms into a wrinkled state due to a substrate deformation mismatch, preserving strain-induced structural color information through interchain hydrogen bonding and crystalline shape-locking in dual-network polymers. Reading the color provides multidimensional information about the instantaneous deformation degree and distribution. Moreover, the moisture-induced shape-memory feature of the 1DPC can be triggered with a minute amount of water, like fingertip perspiration or humidity change (35% to 80%), to restore the original color. This stress/moisture-responsive photonic elastomer, with its dynamically reconfigurable wrinkle-lattice, holds great promise for applications in mechanical sensing, inkless writing, and anticounterfeiting, significantly enhancing the versatility of photonic materials.

360-Degree Tri-Modal Scanning: Engineering a Modular Multi-Sensor Platform for Semantic Enrichment of BIM Models

Abstract. Point clouds, image data, and corresponding processing algorithms are intensively investigated to create and enrich Building Information Models (BIM) with as-is information and maintain their value across the building lifecycle. Point clouds can be captured using LiDAR and enriched with color information from images. Complementary to such dual-sensor systems, thermography captures the infrared light spectrum, giving insight into the temperature distribution on an object’s surface and allowing a diagnosis of the as-is energetic health of buildings beyond what humans can see. Although the three sensor modes are commonly used in pair-wise combinations, only a few systems leveraging the power of tri-modal sensor fusion have been proposed. This paper introduces a sensor system comprising LiDAR, RGB, and a radiometric thermal infrared sensor that can capture a 360-degree range through bi-axial rotation. The resulting tri-modal data is fused to a thermo-color point cloud from which temperature values are derived for a standard indoor building setting. Qualitative data analysis shows the potential for unlocking further object semantics in a state-of-the-art Scan-to-BIM pipeline. Furthermore, an outlook is provided on the cross-modal usage of semantic segmentation for automatic, accurate temperature calculations.

The Effect of Apple Vinegar Addition on the Quality and Shelf Life of Cooked Sausage during Chilling Storage

As more and more consumers are becoming conscious of the safety and taste of meat products, the use of natural additives and innovative processing techniques has gained significant attention. Naturally fermented fruit vinegar is rich in organic acids and antioxidant phenolic compounds. In addition, it contains amino acids, minerals, vitamins, and provitamin beta-carotene, and the presence of acetic acid bacteria may have a positive effect on consumer health. The study aimed to assess the impact of different concentrations of apple vinegar addition on the quality of cooked sausage, focusing on physicochemical parameters, including fatty acid profile and oxidative stability, as well as microbiological quality and sensory changes after production and during chilling storage. Four variants of sausage were prepared: C—sausage without apple vinegar; V1—sausage with 1% of apple vinegar; V3—sausage with 3% of apple vinegar; and V5—sausage with 5% of apple vinegar. All of the tests were carried out after production, as well as after 7 and 14 days of refrigeration storage. The addition of apple vinegar decreased the pH value and increased the oxidation-reduction potential and lipid oxidation in the samples V1, V3, and V5. The sausage with the 5% addition of apple vinegar (V5) was characterized by significantly more intensive brightness (parameter L* = 54.67) in comparison to the C sample (parameter L* = 52.78). The sausages that were tested showed good microbiological quality concerning the total number of microorganisms, lactic acid bacteria, and the absence of pathogenic bacteria. The addition of apple vinegar contributed to the reduction in the intensity of the cured meat flavor and the fatty flavor. Therefore, according to the results presented in this work, it can be concluded that 3% of vinegar is the optimal addition, which may be used in the next step of investigation, taking into account color formation abilities as well as microbiological quality and lipid oxidation processes.

Image-Aided LiDAR Extraction, Classification, and Characterization of Lane Markings from Mobile Mapping Data

The documentation of roadway factors (such as roadway geometry, lane marking retroreflectivity/classification, and lane width) through the inventory of lane markings can reduce accidents and facilitate road safety analyses. Typically, lane marking inventory is established using either imagery or Light Detection and Ranging (LiDAR) data collected by mobile mapping systems (MMS). However, it is important to consider the strengths and weaknesses of both camera and LiDAR units when establishing lane marking inventory. Images may be susceptible to weather and lighting conditions, and lane marking might be obstructed by neighboring traffic. They also lack 3D and intensity information, although color information is available. On the other hand, LiDAR data are not affected by adverse weather and lighting conditions, and they have minimal occlusions. Moreover, LiDAR data provide 3D and intensity information. Considering the complementary characteristics of camera and LiDAR units, an image-aided LiDAR framework would be highly advantageous for lane marking inventory. In this context, an image-aided LiDAR framework means that the lane markings generated from one modality (i.e., either an image or LiDAR) are enhanced by those derived from the other one (i.e., either imagery or LiDAR). In addition, a reporting mechanism that can handle multi-modal datasets from different MMS sensors is necessary for the visualization of inventory results. This study proposes an image-aided LiDAR lane marking inventory framework that can handle up to five lanes per driving direction, as well as multiple imaging and LiDAR sensors onboard an MMS. The framework utilizes lane markings extracted from images to improve LiDAR-based extraction. Thereafter, intensity profiles and lane width estimates can be derived using the image-aided LiDAR lane markings. Finally, imagery/LiDAR data, intensity profiles, and lane width estimates can be visualized through a web portal that has been developed in this study. For the performance evaluation of the proposed framework, lane markings obtained through LiDAR-based, image-based, and image-aided LiDAR approaches are compared against manually established ones. The evaluation demonstrates that the proposed framework effectively compensates for the omission errors in the LiDAR-based extraction, as evidenced by an increase in the recall from 87.6% to 91.6%.

Logical Platforms for Mobile Application in Decision Support Systems Based on Color Information Processing

Logical Platforms for Mobile Application in Decision Support Systems Based on Color Information Processing

Abstract PO2-28-12: Rapid diagnosis of breast biopsies with open-top light-sheet microscopy

Abstract Introduction: Breast cancer is typically diagnosed through a diagnostic work-up that involves specialized breast imaging, image-guided biopsy, and pathological assessment. Pathology results require 2 to 7 business days before a diagnosis is rendered, creating anxiety in women presenting with a breast abnormality. Decreasing pathology evaluation lead-time has the potential to reduce anxiety, streamline patient care, and reduce heathcare costs. In this pilot study, we evaluated the potential of open-top light-sheet microscopy to deliver a preliminary breast pathology diagnosis within 30 minutes of biopsy. Methods: Fresh 14-gauge breast biopsies (N=40) in normal saline were received directly from the breast imaging clinic and immediately stained using the nuclear marker SYBR Gold (Invitrogen) and pan-protein marker Atto 655 NHS Ester (Sigma) prepared in dimethyl sulfoxide, washed, and cleared for imaging at a refractive index of 1.46 using 2,2’-Thiodiethanol (Sigma). The full process requires approximately 14 minutes for staining and clearing. After staining, we placed the biopsy in an in-house-built specimen holder and imaged a 100 micron cross section along the full length of the biopsy using our custom open-top light-sheet microscope. Images were subsequently converted computationally to a standard H&E color format using Fiji and Aivia (Leica) software and were ready for evaluation by a pathologist the same day they were collected. mages were assessed by two pathologists with subspecialization in breast pathology for a two-category diagnosis: benign or atypical. After imaging, the biopsy was frozen, a frozen section was cut, and an H&E-stained slide was produced. Results: Using the protocol above, we demonstrated the ability to stain, clear, image, and visualize needle core biopsies within 30 minutes of receiving the tissue sample. Processing and converting the data to an H&E color palette required additional time, surpassing the 30-minute turnaround time goal. The images contained readily identifiable stroma, epithelial cells, immune cells, and duct structures to a depth of 100 microns. 29/40 biopsies yielded interpretable results. 11/40 had insufficient staining, clearing, or imaging quality for diagnosis. The number of non-diagnostic images improved in the second half of the study (5%) compared to the first half of the study (50%) with sample prep and imaging optimization. Agreement between the two breast pathologists on classifying the biopsies as benign vs. atypical was 89.7%. Discussion: We describe a novel method to obtain a microscopic image of breast core biopsy specimens to facilitate preliminary diagnosis within 30 minutes of receipt of tissue. The quality of the images produced by the method shows promise for preliminary diagnosis, especially after protocol optimization for the second half of the study. Additional optimization is needed in data processing to meet the 30-minute turnaround time requirement. This optimization can be achieved by parallelization of the data processing on a cluster or cloud, which is currently under investigation by our team. Citation Format: Brandy Olin Pope, Rebeca Alvarez, Charles Childers, Suzanne Dintzis, Sarah Javid, Nicholas Reder, Habib Rahbar. Rapid diagnosis of breast biopsies with open-top light-sheet microscopy [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-28-12.

Machine learning-aided evaluation of oxidative strength of cold atmospheric plasma-treated water.

Plasma medicine is gaining attraction in the medical field, particularly the use of cold atmospheric plasma (CAP) in biomedicine. The chemistry of the plasma is complex, and the reactive oxygen species (ROS) within it are the basis for the biological effect of CAP on the target. Understanding how the oxidative power of ROS responds to diverse plasma parameters is vital for standardizing the effective application of CAP. The proven applicability of machine learning (ML) in the field of medicine is encouraging, as it can also be applied in the field of plasma medicine to correlate the oxidative strength of plasma-treated water (PTW) according to different parameters. In this study, plasma-treated water was mixed with potassium iodide-starch reagent for color formation that could be linked to the oxidative capacity of PTW. Corresponding images were captured resulting from the exposure of the color-forming agent to water treated with plasma for different time points. Several ML models were trained to distinguish the color changes sourced by the oxidative strength of ROS. The AdaBoost Classifier (ABC) algorithm demonstrated better performance among the classification models used by extracting color-based features from the images. Our results, with a test accuracy of 63.5%, might carry a potential for future standardization in the field of plasma medicine with an automated system that can be created to interpret the oxidative properties of ROS in different plasma treatment parameters via ML.

Patterned Photonic Actuators with Dynamic Shape‐Morphing and Color‐Changing Capabilities Fabricated by Athermal Embossing Technology

Stimuli‐responsive patterned photonic actuators, characterized by their patterned nano/microscale structures and capacity to demonstrate synergistic color changes and shape morphing in response to external stimuli, have attracted intense scientific attention. However, traditional patterned photonic actuator systems still face limitations such as cumbersome and time‐consuming preparation processes and small‐scale deformations. Herein, we introduce a facile approach involving an athermal embossing technique to rapidly fabricate patterned photonic actuators based on near‐infrared (NIR) light‐responsive liquid crystal elastomers. The resulting patterned photonic actuators demonstrate remarkable features, including brilliant angle‐dependent structural color, complex three‐dimensional actuation, and good color durability under NIR light stimulation. As illustrative demonstrations of the proof‐of‐concept, we fabricate two light‐fuelled patterned photonic soft actuators: a butterfly‐inspired actuator that can produce wing‐flapping dynamic changes in structural color, and an origami crane‐shaped actuator with shape memory, structural color information storage, and dynamic display properties. This strategy provides distinct insights into the design and fabrication of various patterned photonic soft robotic devices and intelligent actuators.

Patterned Photonic Actuators with Dynamic Shape-Morphing and Color-Changing Capabilities Fabricated by Athermal Embossing Technology.

Stimuli-responsive patterned photonic actuators, characterized by their patterned nano/microscale structures and capacity to demonstrate synergistic color changes and shape morphing in response to external stimuli, have attracted intense scientific attention. However, traditional patterned photonic actuator systems still face limitations such as cumbersome and time-consuming preparation processes and small-scale deformations. Herein, we introduce a facile approach involving an athermal embossing technique to rapidly fabricate patterned photonic actuators based on near-infrared (NIR) light-responsive liquid crystal elastomers. The resulting patterned photonic actuators demonstrate remarkable features, including brilliant angle-dependent structural color, complex three-dimensional actuation, and good color durability under NIR light stimulation. As illustrative demonstrations of the proof-of-concept, we fabricate two light-fuelled patterned photonic soft actuators: a butterfly-inspired actuator that can produce wing-flapping dynamic changes in structural color, and an origami crane-shaped actuator with shape memory, structural color information storage, and dynamic display properties. This strategy provides distinct insights into the design and fabrication of various patterned photonic soft robotic devices and intelligent actuators.

Genome-wide association study reveals candidate genes critical for skin pigmentation in common carp (Cyprinus carpio) strains including koi

The differentiation and variation of body color in fish is an important component of biological evolution and phylogenetic research, and is influenced by several factors such as heredity, nutrition, physiological hormones and rearing conditions, with heredity being the most critical factor. An improved comprehension of the genetic regulation in different carp populations will facilitate future applications of precision breeding and pigment cell development-related genes editing. In this study, we used restriction site-associated DNA sequencing (2d-RAD) to determine the distribution characteristics of single nucleotide polymorphisms (SNPs) in eight common carp strains including six koi strains, i.e., Kohaku (Hb), Taisho (Dz), Showa (Zh), Golden (Hj), Matsukawabake (Sch) and Five-color (Ws), and two genetically improved breeding strains, i.e., FFRC No. 2 strain (Fr) and red FFRC strain (Hfr), and then SNP markers to resolve genetic information on the population structure and skin color formation of these strains. We found that most individuals of eight strains were clearly divided into eight clades. Genetic diversity within Fr, Sch and Ws is relatively high, while it is low within Dz, Hb and Hfr. A comparison between the populations revealed that Hfr and Ws have the highest degree of genetic differences, while Hb and Dz have the lowest. The GWAS analysis for two strategies identified several SNPs that are significantly related to skin color variation and affect melanin synthesis (ephrinb2, slc22a23, herc2, ephb2), carotenoid metabolism (oca2, bcmo1, bco2) and circadian rhythm(cipc). To summarize, our study provides some information on the genetic structure and diversity of Japanese koi and Chinese FFRC carp. Furthermore, based on the data obtained on relevant genes related to skin color differentiation and pigmentation, it can provide a theoretical basis for the subsequent integration of dominant traits and precision breeding in carps.

Damage detection on cooling tower shell based on model textures

Ensuring the structural integrity of cooling towers is paramount for safety and efficient operation. This paper presents a novel approach for detecting damage on cooling tower shells, utilising textures derived from laser scanning and close-range photogrammetry. The proposed method delves beyond the limitations of solely relying on colour information by harnessing the rich details embedded in various textures, including diffuse, normal, displacement, and occlusion. The study demonstrates the efficacy of this approach for identifying significant concrete damage. A Convolutional Neural Network (CNN) trained on diffuse textures successfully detects high damage instances with minimal misdetection. However, accurately pinpointing low damage, often manifesting as subtle cracks, and mimicking other patterns like air pores, ribbing, and colour variations, presents a formidable challenge. To tackle this challenge, the authors introduce a novel "composed raster layer" that merges information from multiple textures. This pre-processed layer amplifies the visual cues associated with low damage, facilitating its differentiation from similar patterns. While the current implementation employing multi-resolution segmentation and rule-based classification exhibits promising results, further optimization is acknowledged to refine the accuracy of low damage detection. The successful application of textures commonly used in rendering techniques underscores their remarkable potential for enhancing damage detection in civil engineering applications. While acknowledging limitations such as the analysis of a single cooling tower and the reliance on specific software for damage detection, the study proposes future research directions. This research holds significant implications for the field of civil engineering by offering a promising approach for automated and efficient damage detection on cooling tower shells.

Theoretical Study of Cyanidin-Resveratrol Copigmentation by the Functional Density Theory.

Anthocyanins are colored water-soluble plant pigments. Upon consumption, anthocyanins are quickly absorbed and can penetrate the blood-brain barrier (BBB). Research based on population studies suggests that including anthocyanin-rich sources in the diet lowers the risk of neurodegenerative diseases. The copigmentation caused by copigments is considered an effective way to stabilize anthocyanins against adverse environmental conditions. This is attributed to the covalent and noncovalent interactions between colored forms of anthocyanins (flavylium ions and quinoidal bases) and colorless or pale-yellow organic molecules (copigments). The present work carried out a theoretical study of the copigmentation process between cyanidin and resveratrol (CINRES). We used three levels of density functional theory: M06-2x/6-31g+(d,p) (d3bj); ωB97X-D/6-31+(d,p); APFD/6-31+(d,p), implemented in the Gaussian16W package. In a vacuum, the CINRES was found at a copigmentation distance of 3.54 Å between cyanidin and resveratrol. In water, a binding free energy ∆G was calculated, rendering -3.31, -1.68, and -6.91 kcal/mol, at M06-2x/6-31g+(d,p) (d3bj), ωB97X-D/6-31+(d,p), and APFD/6-31+(d,p) levels of theory, respectively. A time-dependent density functional theory (TD-DFT) was used to calculate the UV spectra of the complexes and then compared to its parent molecules, resulting in a lower energy gap at forming complexes. Excited states' properties were analyzed with the ωB97X-D functional. Finally, Shannon aromaticity indices were calculated and isosurfaces of non-covalent interactions were evaluated.

High Fidelity Full-Color Optical Sectioning Structured Illumination Microscopy by Fourier Domain Based Reconstruction

The natural color of biological specimens plays a crucial role in body protection, signaling, physiological adaptations, etc. Full-color optical sectioning structured illumination microscopy (OS-SIM) color is a promising approach that can reconstruct biological specimens in three-dimension meanwhile maintaining their natural color. Full-color OS-SIM takes the advantages of rapid imaging speed, compatibility with fluorescence and non-fluorescence samples, compact configuration, and low cost. However, the commonly used HSV-RMS reconstruction algorithm for full-color OS-SIM faces two issues to be improved. One is the RMS (root-mean-square) OS reconstruction algorithm is prone to background noise, and the other is the reconstruction is bound in RGB and HSV color spaces, consuming more reconstructing time. In this paper, we propose a full-color Fourier-OS-SIM method that allows for the OS reconstruction using the high-frequency spectrum of the sample and thus is immune to the low-frequency background noise. The full-color Fourier-OS-SIM directly runs in the RGB color space, providing an easy way to restore the color information. Simulation and experiments with various samples (pollen grains and tiny animals) demonstrate that the full-color Fourier-OS-SIM method is superior to the HSV-RMS method regarding background noise suppression. Moreover, benefiting from the background noise suppression merit, the quantitative morphological height map analysis with the full-color Fourier-OS-SIM method is more accurate. The proposed full-color Fourier-OS-SIM method is expected to find broad applications in biological and industrial fields where the 3D morphology and the color information of objects both need to be recovered.

Improving Spatial Resolution Of Satellite Image Using Data Fusion Method

While many remote sensing and GIS applications require both the spatialresolution and spectral resolution be high, image fusion, or in other words, imagesharpening, is a useful technique. To date, numerous image fusion techniques havebeen developed to combine the clear geometric features of the panchromatic imageand the color information of the multispectral image. This paper compares theresults of four different pixel based fusion techniques, HLS, Broveytransformation,Gram–Schmidt and HSV techniques used to merge the ETM+multispectral image with (28.5 m)spatial resolution and panchromatic imagecaptured by SPOT satellite with (10m) spatial resolution, correlation coefficient ,root mean square error and peak signal to noise ratio criteria have been used toachieve the comparison among the using techniques

Build Deep Neural Network Models to Detect Common Edible Nuts from Photos and Estimate Nutrient Portfolio.

Nuts are nutrient-dense foods and can be incorporated into a healthy diet. Artificial intelligence-powered diet-tracking apps may promote nut consumption by providing real-time, accurate nutrition information but depend on data and model availability. Our team developed a dataset comprising 1380 photographs, each in RGB color format and with a resolution of 4032 × 3024 pixels. These images feature 11 types of nuts that are commonly consumed. Each photo includes three nut types; each type consists of 2-4 nuts, so 6-9 nuts are in each image. Rectangular bounding boxes were drawn using a visual geometry group (VGG) image annotator to facilitate the identification of each nut, delineating their locations within the images. This approach renders the dataset an excellent resource for training models capable of multi-label classification and object detection, as it was meticulously divided into training, validation, and test subsets. Utilizing transfer learning in Python with the IceVision framework, deep neural network models were adeptly trained to recognize and pinpoint the nuts depicted in the photographs. The ultimate model exhibited a mean average precision of 0.7596 in identifying various nut types within the validation subset and demonstrated a 97.9% accuracy rate in determining the number and kinds of nuts present in the test subset. By integrating specific nutritional data for each type of nut, the model can precisely (with error margins ranging from 0.8 to 2.6%) calculate the combined nutritional content-encompassing total energy, proteins, carbohydrates, fats (total and saturated), fiber, vitamin E, and essential minerals like magnesium, phosphorus, copper, manganese, and selenium-of the nuts shown in a photograph. Both the dataset and the model have been made publicly available to foster data exchange and the spread of knowledge. Our research underscores the potential of leveraging photographs for automated nut calorie and nutritional content estimation, paving the way for the creation of dietary tracking applications that offer real-time, precise nutritional insights to encourage nut consumption.

Color subspace exploring for natural image matting

AbstractDeep neural networks have seen a surge of successful methods in natural image matting. However, the overlap of foreground and background color distributions in an image is still troubling in matting. It is observed that the three color channels contain different contrast information of an image: some color channels may provide clearer contrast information for separating the foreground from the image, while the foreground and background color distributions in other channels may heavily overlap, resulting in blurred foreground‐background boundaries. Motivated by this observation, the Color Subspace Exploring Network (CSEMat) is proposed to extract the foreground object from an image by exploring high‐contrast appearance information in individual color spaces. Specifically, a 4‐branch encoder is constructed, with one branch for the RGB image and three branches for subdividing the color space. Each color channel is individually processed by a sub‐encoder. Additionally, the trimap‐based color information aggregation module (CIA) is introduced to integrate the feature maps from the independent sub‐encoders, facilitating the transfer of optimized features to the decoder. Extensive experiments demonstrate that the proposed CSEMat achieves favorable performance on publicly available matting datasets.

Deep learning-based three-dimensional crack damage detection method using point clouds without color information

Automated high-precision crack detection on building structures under poor lighting conditions poses a significant challenge for traditional image-based methods. Overcoming this challenge is crucial to enhance the practical applicability of structural health monitoring and rapid damage assessment, especially in post-disaster scenarios like earthquakes. To address this challenge, this paper presents a deep learning-based three-dimensional crack detection method that utilizes light detection and ranging (LiDAR) point cloud data. Our method is specifically designed to address crack detection without relying on color information input, resulting in high-precision and robust apparent damage detection. The key contribution of this paper is the NL-3DCrack model, which enables automated three-dimensional crack semantic segmentation. This model comprises a feature embedding module, an incomplete neighbor feature extraction module, a decoder, and morphological filtering. Notably, we introduce an innovative incomplete neighbor mechanism to effectively mitigate the impact of outliers. To validate the effectiveness of our proposed method, we establish two three-dimensional crack detection datasets, namely the Luding dataset and the terrestrial laser scanner dataset, which are based on earthquake disasters. Experimental results demonstrate that our method achieves remarkable performance, with an intersection-over-union of 39.62% and 51.33% on the respective test sets, surpassing existing point cloud-based semantic segmentation models. Ablation experiments further confirm the effectiveness of our approach. In summary, our method showcases exceptional crack detection performance on LiDAR data using only XYZI channels. With its high precision and reliable results, it offers significant utility in real-world applications, contributing to improved structural health monitoring and rapid damage assessment after disasters, particularly in post-earthquake scenarios.

Transcriptome and Pigment Analyses Provide Insights into Carotenoids and Flavonoids Biosynthesis in Camellia nitidissima Stamens

Camellia nitidissima is famous for its golden flowers. Its flowers are rich in secondary metabolites, and they have ornamental, medicinal, and edible value. Pigment composition and regulation has been studied in the golden petals, but there has been little research on pigment composition or the molecular mechanism underlying yellow stamens in C. nitidissima. To explore the molecular mechanism of yellow stamen formation, three developmental stages (S0, S1, and S2) were used for transcriptome and pigment analyses. Pigment analysis showed that the flavonoid content increased sharply from the S0 to S1 stage and decreased from the S1 to S2 stage, and the carotenoid content increased sharply during yellow stamen formation (from the S1 to S2 stage). RNA-seq analysis showed that a total of 20,483 differentially expressed genes (DEGs) were identified. KEGG and heatmap analyses showed that flavonoid and carotenoid biosynthesis pathways were enriched, and we identified 14 structural genes involved in flavonoid biosynthesis and 13 genes involved in carotenoid biosynthesis and degradation. In addition, the expression of carotenoid- and flavonoid-related genes was consistent with carotenoid and flavonoid content. In addition, correlation network analysis indicated that the WARYK, MYB, bHLH, and AP2/ERF transcription factor families were screened for involvement in the biosynthesis of flavonoids and carotenoids. In this study, we describe the pathway associated with color formation in the stamens of C. nitidissima.