Color Space Research Articles

Hierarchical mixture of discriminative Generalized Dirichlet classifiers

This paper presents a discriminative classifier for compositional data. This classifier is based on the posterior distribution of the Generalized Dirichlet which is the discriminative counterpart of Generalized Dirichlet mixture model. Moreover, following the mixture of experts paradigm, we proposed a hierarchical mixture of this classifier. In order to learn the models parameters, we use a variational approximation by deriving an upper-bound for the Generalized Dirichlet mixture. To the best of our knownledge, this is the first time this bound is proposed in the literature. Experimental results are presented for spam detection and color space identification.

Vision-Based Algorithm for Precise Traffic Sign and Lane Line Matching in Multi-Lane Scenarios

With the rapid development of intelligent transportation systems, lane detection and traffic sign recognition have become critical technologies for achieving full autonomous driving. These technologies offer crucial real-time insights into road conditions, with their precision and resilience being paramount to the safety and dependability of autonomous vehicles. This paper introduces an innovative method for detecting and recognizing multi-lane lines and intersection stop lines using computer vision technology, which is integrated with traffic signs. In the image preprocessing phase, the Sobel edge detection algorithm and weighted filtering are employed to eliminate noise and interference information in the image. For multi-lane lines and intersection stop lines, detection and recognition are implemented using a multi-directional and unilateral sliding window search, as well as polynomial fitting methods, from a bird’s-eye view. This approach enables the determination of both the lateral and longitudinal positioning on the current road, as well as the sequencing of the lane number for each lane. This paper utilizes convolutional neural networks to recognize multi-lane traffic signs. The required dataset of multi-lane traffic signs is created following specific experimental parameters, and the YOLO single-stage target detection algorithm is used for training the weights. In consideration of the impact of inadequate lighting conditions, the V channel within the HSV color space is employed to assess the intensity of light, and the SSR algorithm is utilized to process images that fail to meet the threshold criteria. In the detection and recognition stage, each lane sign on the traffic signal is identified and then matched with the corresponding lane on the ground. Finally, a visual module joint experiment is conducted to verify the effectiveness of the algorithm.

PVA/PEG/CuCo2O4/PANi/x wt% MWCNTs blended polymers: Improved the physical properties for energy storage devices and optoelectronic applications

The current investigation aims to enhance the optical and dielectric properties of poly (vinyl alcohol) (PVA)/polyethylene glycol (PEG) blended polymers via loading with copper cobaltite (CuCo2O4), polyaniline (PANi), and x wt% multi-walled carbon nanotubes (MWCNTs) for potential applications in optoelectronics and capacitive storage. The fabrication of PVA/PEG/CuCo2O4/PANi/x wt% MWCNTs blended polymers was carried out via casting and hydrothermal methods. Characterization of the blended polymer's structure and morphology was conducted using X-ray diffraction and scanning electron microscopy techniques. The impact of various fillers on the linear and nonlinear optical properties of the host blend was analyzed. The loaded blends demonstrated efficacy in blocking UVA, UVB, and UVC spectra, making them suitable absorbers for solar cells. The lowest values for direct and indirect optical band gap energy (Eg), of 5.34 eV and 4.44 eV were attained at x = 0.02. The blend with x = 0.01 displays the highest refractive index at λ = 600 nm. The optical conductivity exhibited nonmonotonically growth until peaking at x = 0.01. Analysis of chromaticity diagrams in CIE 1931 color space reveals that all blends emit blue-violet hues with slight variations in intensity. The fluorescence (FL) intensity of the doped blend is notably lower compared to the undoped counterparts. The doped blend (x = 0.03) showcases superior dielectric constants and ac conductivity values. The maximum energy density was achieved at 1 kHz for the host blend incorporating CuCo2O4/PANi. Overall, the observed characteristics indicate that PVA/PEG/CuCo2O4/PANi/x wt% MWCNTs blended polymers represent promising hybrid nanomaterials suitable for diverse applications.

Colorimetric Analyses of the Optic Nerve Head and Retina Indicate Increased Blood Flow After Vitrectomy.

The purpose of this study was to evaluate the impact of vitrectomy and posterior hyaloid (PH) peeling on color alteration of optic nerve head (ONH) and retina as a surrogate biomarker of induced perfusion changes. Masked morphometric and colorimetric analyses were conducted on preoperative (<1month) and postoperative (<18months) color fundus photographs of 54 patients undergoing vitrectomy, either with (44) or without (10) PH peeling and 31 years of age and gender-matched control eyes. Images were calibrated according to the hue and saturation values of the parapapillary venous blood column. Chromatic spectra of the retinal pigment epithelium and choroid were subtracted to avoid color aberrations. Red, green, and blue (RGB) bit values over the ONH and retina were plotted within the constructed RGB color space to analyze vitrectomy-induced color shift. Vitrectomy-induced parapapillary vein caliber changes were also computed morphometrically. A significant post-vitrectomy red hue shift was noted on the ONH (37.1degrees ± 10.9degrees vs. 4.1degrees ± 17.7degrees, P < 0.001), which indicates a 2.8-fold increase in blood perfusion compared to control (2.6 ± 1.9 vs. 0.9 ± 1.8, P < 0.001). A significant post-vitrectomy increase in the retinal vein diameter was also noticed (6.8 ± 6.4% vs. 0.1 ± 0.3%, P < 0.001), which was more pronounced with PH peeling (7.9 ± 6.6% vs. 3.1 ± 4.2%, P = 0.002). Vitrectomy and PH peeling increase ONH and retinal blood flow. Colorimetric and morphometric analyses offer valuable insights for future artificial intelligence and deep learning applications in this field. The methodology described herein can easily be applied in different clinical settings and may enlighten the beneficial effects of vitrectomy in several retinal vascular diseases.

Electroluminescent white quantum dot light-emitting diodes based on high pressure synthesis of graphitic C3N4 semiconductor

Graphitic carbon nitride (g-C3N4) has shown promising potentials for quantum dot light-emitting diodes (QLEDs), which might be an effective alternative of the mostly used Cd(Se,S)/Zn(S,Se) emitting layers and can achieve both low toxicity and high stability, meeting the goal of sustainable development. Herein, we for the first report a white electroluminescent QLED device with blue and orange dual-color emissions, which were attributed to the σ*-LP, π*- π, and π*-LP transitions, respectively, from a single-compound g-C3N4 quantum dots (QDs) by synthesizing the precursor at 0.8 Mpa high-pressure N2. By selectively exciting the δ and π bonds with X-ray, the luminescence mechanism was approved with the synchrotron radiation techniques of X-ray absorption near edge structure (XANES) and the X-ray excited optical luminescence (XEOL).The chromaticity coordinate changes from cayan color space with CIE (0.2034, 0.1939) to white color space with CIE (0.3009, 0.2697) as pressure increases from 0.06 to 0.8 Mpa, with the increased overlap of the δ* and π* orbital upon the contraction of crystal lattice. The breakthrough achieved in this work will promisingly promote the development of future white light sources as thin as paper for human home and office lighting by using direct current-driven electroluminescence of QDs, instead of photoluminescence of phosphors.

A novel deep CNN model with entropy coded sine cosine for corn disease classification

Corn diseases significantly impact crop yields, posing a major challenge to agricultural productivity. Early and accurate detection of these diseases is crucial for effective management and mitigation. Existing methods, mostly relying on analyzing corn leaves, often lack the precision to identify and classify a wide range of diseases under varying conditions. This study introduces a novel approach to detecting corn diseases using image processing and deep learning techniques, aiming to enhance detection accuracy through pre-processing, improved feature extraction and selection, and classification algorithms. A new deep Convolutional Neural Network (CNN) model named TreeNet, with 35 layers and 38 connections, is proposed. TreeNet is pre-trained using the Plant Village imaging dataset. For image pre-processing, the YCbCr color space is utilized to improve color representation and contrast. Feature extraction is performed using TreeNet and two pre-trained models, Darknet-53, and DenseNet-201, with features fused using a serial-based fusion method. The Entropy-coded Sine Cosine Algorithm is applied for feature selection, optimizing the feature set for classification. The selected features are used to train Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers, with extensive experiments conducted using both 5-fold and 10-fold cross-validation, and feature sizes ranging from 200 to 1150. The proposed method achieves classification accuracy, precision, recall, and F1-score of 99.8%, 99%, 100%, and 99%, respectively, surpassing existing benchmarks. The integration of TreeNet with Darknet-53 and DenseNet-201, along with robust pre-processing and feature selection, significantly improves corn disease detection, highlighting the potential of advanced CNN architectures in agriculture.

Color Feature Extraction for Grape Variety Identification: Naïve Bayes Approach

The problem addressed in this research is the lack of an efficient and accurate method for automatically identifying grape varieties. Accurate identification is crucial for quality control in the agricultural and food industries, impacting product labeling, pricing, and consumer trust. The aim of this research is to develop an automated system to classify green, black, and red grapes using digital image processing technology. This research method employs Naïve Bayes classification combined with color feature extraction. Testing was conducted under two scenarios: a database scenario with predefined grape image datasets and an out-of-database scenario with images resembling grape colors. Image processing includes resizing images to 200x200 pixels, Gamma Correction, Gaussian filtering, conversion to Lab* color space, K-Means Clustering for segmentation, followed by feature extraction and Naïve Bayes classification. The results of this research are that in the database scenario, the system achieved accuracies of 98.33% with an 80:20 data split and 98.89% with a 70:30 split. In the out-of-database scenario, accuracies were 96.67% with an 80:20 split and 97.78% with a 70:30 split. The conclusion of this research is the proposed method provides a reliable and efficient solution for automatic grape variety identification, benefiting quality control in agriculture and food industries.

Sperm Size Decreases With Increasing Pheomelanin Pigmentation but Not With the Amount of Glutathione in the Barn Swallow.

Pigment-based coloration is prevalent in animals, but its expression greatly varies across species, populations, and even among individuals in the same populations. Some animals are highly pigmented and thus have conspicuous coloration, whereas others are modestly pigmented and thus have drab coloration. A possible explanation for the variety in pigmentation is a resource-based tradeoff, in which resources invested in pigmentation are unavailable for other functional traits, and thus animals that need to invest in the latter have limited resources to invest in pigmentation. Resource-based tradeoff is plausible in theory, but direct tests are scarce, partially because of many components of pigment-based coloration (i.e., multiple pigments, integument microstructure, and stains) that affect coloration, preventing the use of coloration as an index of pigmentation. Here, using the barn swallow, Hirundo rustica, we examined the relationship between pheomelanin pigmentation in reddish throat patch (a precopulatory sexual trait) and total sperm length (a postcopulatory sexual trait), with particular attention to glutathione as the common resource. We predicted that pheomelanin, which is the predominant pigment in the reddish throat patch, should be negatively related to total sperm length, and that both sexual traits should be further negatively related to the amount of glutathione. As predicted, we found a negative relationship between pheomelanin pigmentation and total sperm length. However, the amount of glutathione in the blood showed no detectable relationship to them. The tradeoff between pheomelanin pigmentation and sperm size, as inferred from the current and previous results, might not be a simple glutathione-based tradeoff.

Linearity Study of Color Measurement Methods in Digital Image Colorimetry

Color metrology is steadily developing in the interests of fundamental and applied science, improving industrial production processes. The author considers the problem of ensuring metrological traceability of color measurements under natural conditions.Digital image colorimetry methods demonstrate good linearity and precision in relatively small application ranges in studies of same type and mainly non-luminous objects. For complex scenes with a wide range of brightnesses, nonlinear functional color rendering models are used that are focused on visual perception, peak brightness of the monitor and reference viewing conditions, which complicates the processing of measurement results and reduces a confidence interval.The described method of measuring the chromaticity coordinates of an object by its digital image is based on tensor calculus and consists of multiple registration of an object and sets of reference samples with different exposure times, building calibration dependencies forming a multidimensional colorimetric scale in color space and determining chromaticity coordinates as points of intersection of color tensors with the plane of the color locus. In order to ensure accuracy and reliability of the measurement results in the software and hardware environments, a validation study of the performance indicators of this method was carried out, one of which is linearity. An experiment based on comparing the developed method with the reference measurement method showed the effectiveness of the developed wide-range measurement method with good linearity (correlation coefficient R=0.97… 0.99, residual relative standard deviation s0≈1–2%) when adjusting from the subjective factor and display device.The experimental materials may be useful to metrologists solving problems of determining the metrological traceability of the color of digital images in media. The article will be of interest to a wide range of readers with professional interests related to the colorimetry of digital images.

Digging into Depth and Color Spaces: A Mapping Constraint Network for Depth Super-Resolution

Scene depth super-resolution (DSR) poses an inherently ill-posed problem due to the extremely large space of one-to-many mapping functions from a given low-resolution (LR) depth map, which possesses limited depth information, to multiple plausible high-resolution (HR) depth maps. This characteristic renders the task highly challenging, as identifying an optimal solution becomes significantly intricate amidst this multitude of potential mappings. While simplistic constraints have been proposed to address the DSR task, the relationship between LR and HR depth maps and the color image has not been thoroughly investigated. In this paper, we introduce a novel mapping constraint network (MCNet) that incorporates additional constraints derived from both LR depth maps and color images. This integration aims to optimize the space of mapping functions and enhance the performance of DSR. Specifically, alongside the primary DSR network (DSRNet) dedicated to learning LR-to-HR mapping, we have developed an auxiliary degradation network (ADNet) that operates in reverse, generating the LR depth map from the reconstructed HR depth map to obtain depth features in LR space. To enhance the learning process of DSRNet in LR-to-HR mapping, we introduce two mapping constraints in LR space: 1) the cycle-consistent constraint, which offers additional supervision by establishing a closed loop between LR-to-HR and HR-to-LR mappings, and 2) the region-level contrastive constraint, aimed at reinforcing region-specific HR representations by explicitly modeling the consistency between LR and HR spaces. To leverage the color image effectively, we introduce a feature screening module (FSM) to adaptively fuse color features at different layers, which can simultaneously maintain strong structural context and suppress texture distraction through subspace generation and image projection. Comprehensive experimental results across synthetic and real-world benchmark datasets unequivocally demonstrate the superiority of our proposed method over state-of-the-art DSR methods. Our MCNet achieves an average MAD reduction of 3.7% and 7.5% over state-of-the-art DSR method for \(\times\) 8 and \(\times\) 16 cases on Milddleburry dataset, respectively, without incurring additional costs during inference.

Shadow removal method for high-resolution aerial remote sensing images based on region group matching

Shadow removal is a critical step in preprocessing high-resolution aerial remote sensing images. The presence of shadows reduces the information in high-resolution aerial remote sensing images, lowers the image quality, and seriously interferes with the work of feature classification, object detection, and information extraction. Therefore, this paper proposes a high-resolution aerial remote sensing image shadow removal method based on region group matching. The method starts from both spatial information and color information, and is able to take into account the whole and the local, and recover the detailed features of different features inside the shadows. Firstly, this paper proposes an irregular region color transfer method based on three-dimensional color space, which can effectively restore the original color and texture information of the features in the shadow region. Secondly, considering the random and variable direction of feature distribution in remote sensing images, the direction of texture feature extraction cannot be set in advance. Therefore, in this paper, the texture details of the features are extracted based on the rotationally invariant light-independent texture feature extraction method to exclude the abnormal interference. Then, the shadow and light regions of the image are segmented internally and grouped according to the type of features to avoid subsequent matching anomalies due to the small size of the image blocks. Next, construct an average texture feature vector for each group of image blocks for grouping matching. The matched light groups are then used to guide the shadow groups for local feature information enhancement, which ensures that on top of the overall shadow removal, the detail recovery of different local features is enhanced. Finally, for the boundary effect after shadow removal, a boundary optimization method with dynamically assigned weights is proposed, which can ensure the natural coherence of the resultant image in the boundary part. The experimental results show that the proposed method balances both overall and local shadow removal effects and can effectively achieve high-resolution aerial remote sensing image shadow removal. Compared with existing methods, the proposed method achieves the best quality of shadow removal results and restores more realistic ground features in shadow region.

Colour difference detection algorithm for warp‐knitted fabric based on image colour appearance model

AbstractDetecting colour differences in warp‐knitting fabric is essential to ensure its quality. Machine vision algorithms are commonly used for automatic colour difference detection. However, the current algorithm directly extracts RGB (red, green, blue) colour stimulus values from the image without considering the image's colour attributes, leading to a significant error in colour difference detection. To solve this issue, a colour appearance model is introduced into the field of colour difference detection for warp‐knitted fabric. Initially, a colour appearance model based on CAM16 is established, and the critical parameters are calculated to obtain the colour appearance attribute of the fabric. The colour difference calculation formula is then constructed in the uniform colour space of CAM16‐UCS. A template selection algorithm based on principal component analysis was designed to select warp‐knitted cloth images with standard colours. Subsequently, a cloth colour difference detection algorithm was developed using the cloth colour profile model. The performance of the colour difference formula based on the colour profile model was evaluated using the PF/3 method. To compare the CIELab, CMC(2:1), and CIEDE2000 colour difference formulas, standardised residual sum of squares was used. The results indicated that the colour difference formula based on the colour‐appearance model is about 5.32% different from the visual colour difference perceived by the human eye. However, it can perform as well as the CMC(2:1) colour difference formula, which is widely used in the textile industry.

Phenolic Composition and Bioactivities of Invasive Ailanthus altissima (Mill.) Swingle Leaf Extracts Obtained by Two-Step Sequential Extraction.

Ailanthus altissima, a highly invasive species, contains valuable compounds in different plant parts, indicating great practical potential. This paper proposes the use of non-polar (n-hexane) and polar (ethanol) solvents for the extraction of antioxidant compounds from A. altissima (family Simaroubaceae) leaves in a sequential two-step process. Fresh and dried leaves were examined for their microstructure by scanning electron microscopy, and for color changes in the CIELAB color space co-ordinates. An investigation of the harvesting season, processing (freezing and drying), and solvent indicates ethanol can be used for the highly efficient extraction of phenolics, flavonoids, tannins, and carotenoids. Statistically significant differences were found between the autumn and summer samples for phenolic content, and between dried and frozen samples for tannin content. The HPLC phenolic profile indicates more phenolics (nine polyphenols) in dried leaves harvested in both seasons compared to those in frozen ones (five to six polyphenols). Frozen leaves showed a higher antioxidant activity in a ferric-reducing antioxidant power assay than that of the dried samples, which exhibited a higher antioxidant activity using the 1, 1-diphenyl-2-picryl-hydrazyl assay, but it was not statistically significant. The phenolic, flavonoid, and carotenoid contents significantly influenced the antioxidant activities. Among the ethanolic extracts, those from dried leaves showed better antibacterial activity, in particular, on Staphylococcus aureus and Enterococcus faecalis. The high bioactive content and activity of A. altissima leaves make them suitable natural raw materials for various applications.

Effects of Ripening Phase and Cultivar under Sustainable Management on Fruit Quality and Antioxidants of Sweet Cherry

Sweet cherry grown under sustainable management produces highly valuable fruits, whose quality shows important biochemical and morphological changes during ripening. Research was carried out in Iasi (Romania), with the aim to assess the quality characteristics of the sweet cherry fruits of three cultivars (Van, Andreiaș, Margonia), grown in an inner or outer position inside the tree crown, at the pre-ripening or full ripeness phase. In 2022, the colour component a* showed higher values in cv. Van and Andreias red fruits and in an inner position, whereas the components L* and b* at the full ripeness phase were highest in cv. Margonia. The dry matter and total soluble solids contents increased from the pre-ripening to the full ripeness phase and were highest in cv. Van sweet cherry fruits; the DM of fruit from the outer part of crown was higher than that of fruit from the inner part at the pre-ripening phase. The content of phenolics was the highest in cv. Margonia fruits at the pre-ripening stage and in cv. Van at the full ripeness phase and higher in the inner tree crown zones. The cultivar Margonia generally showed the highest vitamin C content in both years and development phases. The yellow fruit cv. Margonia mostly showed the highest values of chlorophyll a and b. The fruit’s content of carotene, lycopene, and anthocyanins was generally the highest in the red fruits of cv. Andreias. The examined sweet cherry cultivars showed a high variability in fruit nutritional quality and proved to be a rich source of bioactive compounds with antioxidant potential.

Dissecting the components of error in analogue report tasks

Over the last two decades, the analogue report task has become a standard method for measuring the fidelity of visual representations across research domains including perception, attention, and memory. Despite its widespread use, there has been no methodical investigation of the different task parameters that might contribute to response variability. To address this gap, we conducted two experiments manipulating components of a typical analogue report test of memory for colour hue. We found that human response errors were independently affected by changes in storage and maintenance requirements of the task, demonstrated by a strong effect of set size even in the absence of a memory delay. In contrast, response variability remained unaffected by physical size of the colour wheel, implying negligible contribution of motor noise to task performance, or by its chroma radius, highlighting non-uniformity of the standard colour space. Comparing analogue report to a matched forced-choice task, we found variation in adjustment criterion made a limited contribution to analogue report variability, becoming meaningful only with low representational noise. Our findings validate the analogue report task as a robust measure of representational fidelity for most purposes, while also quantifying non-representational sources of noise that would limit its reliability in specialized settings.

The Impact of Microwave Annealing on MoS2 Devices Assisted by Neural Network-Based Big Data Analysis.

Microwave annealing, an emerging annealing method known for its efficiency and low thermal budget, has established a foundational research base in the annealing of molybdenum disulfide (MoS2) devices. Typically, to obtain high-quality MoS2 devices, mechanical exfoliation is commonly employed. This method's challenge lies in achieving uniform film thickness, which limits the use of extensive data for studying the effects of microwave annealing on the MoS2 devices. In this experiment, we utilized a neural network approach based on the HSV (hue, saturation, value) color space to assist in distinguishing film thickness for the fabrication of numerous MoS2 devices with enhanced uniformity and consistency. This method allowed us to precisely assess the impact of microwave annealing on device performance. We discovered a relationship between the device's electrical performance and the annealing power. By analyzing the statistical data of these electrical parameters, we identified the optimal annealing power for MoS2 devices as 700 W, providing insights and guidance for the microwave annealing process of two-dimensional materials.

A Study on the Color Prediction of Ancient Chinese Architecture Paintings Based on a Digital Color Camera and the Color Design System

Color paintings such as painted facades and interiors are important decoration elements of ancient Chinese architectures. The color of the paintings usually fades over time due to exposure to strong light, high humidity, high temperatures, and other environmental factors. In order to restore or reproduce the color appearance of ancient architecture paintings correctly, it was necessary to study the color degradation process of such paintings. To meet the needs of on-site colorimetric measurement of the paintings on ancient Chinese architectures, we propose using a digital color camera and the CDS (Color Design System) to measure and evaluate the colors of such paintings. The CDS is a color order system recommended by the Chinese national technical committee for color standardization (SAC/TC 120) in 2017 (GB/Z 35473-2017). The current version of the CDS atlas contains about 2740 samples which were uniformly distributed on the whole color space, and can be used to set up the colorimetric characterization model for the digital camera. Particularly, the digital CDS lookup table contains over 400 thousand samples, and it can be used to express the color of paintings on ancient Chinese architectures. In the experiment, a digital color camera was used to capture the colors of the paintings on the ancient Chinese architectures of different years based on the CDS and polynomial transform method. Moreover, a linear interpolation method was proposed for calculating and predicting the color degradation of such paintings. The experimental results show that with the increase in years, the color hue of the paintings changes slowly, while the lightness and the chroma of them fade obviously. In the future, more ancient architectures of different years and from different places should be selected as experimental samples to improve the method and the results of the paper.

Enhancing of polyp image segmentation in colonoscopy images: a comprehensive approach using modified UNet, hybrid color space, and ensemble learning

Enhancing of polyp image segmentation in colonoscopy images: a comprehensive approach using modified UNet, hybrid color space, and ensemble learning

Robust vision detection of pipeline solder joints

PurposeSolder joint inspection plays a critical role in various industries, with a focus on integrated chip (IC) solder joints and metal surface welds. However, the detection of tubular solder joints has received relatively less attention. This paper aims to address the challenges of detecting small targets and complex environments by proposing a robust visual detection method for pipeline solder joints. The method is characterized by its simplicity, cost-effectiveness and ease of implementation.Design/methodology/approachA robust visual detection method based on the characteristics of pipeline solder joints is proposed. With the improved hue, saturation and value (HSV) color space, the method uses a multi-level template matching approach to first segment the pipeline from the background, and then match the endpoint of the pipeline to accurately locate the solder joint. The proposed method leverages the distinctive characteristics of pipeline solder joints and employs an enhanced HSV color space. A multi-level template matching approach is utilized to segment the pipeline from the background and accurately locate the solder joint by matching the pipeline endpoint.FindingsThe experimental results demonstrate the effectiveness of the proposed solder joint detection method in practical detection tasks. The average precision of pipeline weld joint localization exceeds 95%, while the average recall is greater than 90%. These findings highlight the applicability of the method to pipeline solder joint detection tasks, specifically in the context of production lines for refrigeration equipment.Research limitations/implicationsThe precision of the method is influenced by the placement angle and lighting conditions of the test specimen, which may pose challenges and impact the algorithm's performance. Potential avenues for improvement include exploring deep learning methods, incorporating additional features and contextual information for localization, and utilizing advanced image enhancement techniques to improve image quality.Originality/valueThe proposed pipeline solder joint detection method offers a novel and practical approach. The simplicity, cost-effectiveness and ease of implementation make it an attractive choice for detecting pipeline solder joints in different industrial applications.

Prediction model for laser marking colors based on color mixing.

The current techniques for coloring surfaces using lasers necessitate the identification of numerous laser marking parameters, which is a laborious process. Furthermore, the quantitative analysis of generating a wide variety of colors through fewer sets of laser marking parameters is a huge challenge. This work employs a nanosecond laser to generate mixed structural colors from micro-nano structures on the surface of stainless steel in order to address these issues. Additionally, the color mixing principle is investigated in relation to these micro-nano structures. On this basis, the spectral reflectance of the primary color is mapped to the linear mixed color space, and the linear mixed color space is constructed by minimizing the linear deviation function. In this space, a precise mathematical model for color prediction is developed, which effectively captures the correlation between the primary color and the resulting mixed color. Four primary colors are created using four sets of laser marking parameters. Mixing these primary colors in varying proportions can achieve more than 100 new tones with rich colors. The average color difference Δ E a b∗ and Δ E00∗ are 1.98 and 1.80, respectively. By utilizing this model to adjust the proportion of primary colors in each subgraph, an image with vibrant and rich colors is generated, thereby achieving the implementation of a structural color image based on mixed colors.