Hue, Saturation, Value Color Space Research Articles

Colorimetric detection of serum creatinine on a miniaturized platform using hue-saturation-value space analysis

Chronic kidney disease (CKD) is a widespread condition with considerable health and economic impacts globally. However, existing methodologies for serum creatinine assessment often involve prolonged wait times and sophisticated equipment, such as spectrometers, hindering real-time diagnosis and care. Innovative solutions like point-of-care (POC) devices are emerging to address these challenges. In this context, there is a recognized need for remote, regular, automated, and low-cost analysis of serum creatinine levels, given its role as a critical parameter for CKD diagnosis and management. This study introduces a miniaturized system with integrated heater elements designed for precise serum creatinine measurement. The system operates based on the Jaffe method and accurate serum creatinine measurement within a microreservoir chip. Smartphone-based image processing using the hue-saturation-value (HSV) color space was applied to captured images of microreservoirs. The creatinine analyses were conducted in serum with a limit of detection of ~ 0.4 mg/dL and limit of quantification of ~ 1.3 mg/dL. Smartphone-based image processing employing the HSV color space outperformed spectrometric analysis for creatinine measurement conducted in serum. This pioneering technology and smartphone-based processing offer the potential for decentralized renal function testing, which could significantly contribute to improved patient care. The miniaturized system offers a low-cost alternative ($87 per device), potentially reducing healthcare expenditures (~ $0.5 per test) associated with CKD diagnosis and management. This innovation could greatly improve access to diagnosis and monitoring of CKD, especially in regions where access to sophisticated laboratory equipment is limited.

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.

Enhancement of Mine Images through Reflectance Estimation of V Channel Using Retinex Theory

The dim lighting and excessive dust in underground mines often result in uneven illumination, blurriness, and loss of detail in surveillance images, which hinders subsequent intelligent image recognition. To address the limitations of the existing image enhancement algorithms in terms of generalization and accuracy, this paper proposes an unsupervised method for enhancing mine images in the hue–saturation–value (HSV) color space. Inspired by the HSV color space, the method first converts RGB images to the HSV space and integrates Retinex theory into the brightness (V channel). Additionally, a random perturbation technique is designed for the brightness. Within the same scene, a U-Net-based reflectance estimation network is constructed by enforcing consistency between the original reflectance and the perturbed reflectance, incorporating ResNeSt blocks and a multi-scale channel pixel attention module to improve accuracy. Finally, an enhanced image is obtained by recombining the original hue (H channel), brightness, and saturation (S channel), and converting back to the RGB space. Importantly, this image enhancement algorithm does not require any normally illuminated images during training. Extensive experiments demonstrated that the proposed method outperformed most existing unsupervised low-light image enhancement methods, qualitatively and quantitatively, achieving a competitive performance comparable to many supervised methods. Specifically, our method achieved the highest PSNR value of 22.18, indicating significant improvements compared to the other methods, and surpassing the second-best WCDM method by 10.3%. In terms of SSIM, our method also performed exceptionally well, achieving a value of 0.807, surpassing all other methods, and improving upon the second-place WCDM method by 19.5%. These results demonstrate that our proposed method significantly enhanced image quality and similarity, far exceeding the performance of the other algorithms.

Quantitative Analysis of Comprehensive Similarity in Restoration of Ancient Building Walls Using Hue–Saturation–Value Color Space and Circular Local Binary Pattern

Evaluating the effects of wall restoration on ancient buildings has been a difficult task, and it is important that the overall appearance of the restored walls of ancient buildings is similar, harmonious, and uniform. This paper used a hue–saturation–value (HSV) color space and Circular Local Binary Pattern (CLBP) to analyze the comprehensive similarity between a restored wall and the original walls in Qi Li Ancient Town. The results show that the values of the comprehensive similarity calculation of ancient buildings based on the color and texture were consistent with the actual situation. The method is suitable for evaluating the degree of matching between wall repair materials and the appearance of the original wall materials of ancient buildings, and it can also be used to assess the comprehensive similarity between the repair materials and the original building walls before carrying out the wall repair in order to select more suitable materials for wall repair and achieve the best repair effect. And it is flexible and objective compared to human judgement. Through the accurate restoration of ancient buildings, not only can we protect cultural heritage and continue the historical lineage, we can also enhance the aesthetic value of buildings and meet people’s needs for historical and cultural tracing.

Experimental Study and Effectiveness Evaluation on the Rapid Antiquing of Red Sandstone in Ancient Buildings Restoration

As there are few cases of red sandstone rapid antiquing in ancient buildings and as it is difficult to reproduce, this paper carried out an experimental study and effect evaluation assessment on red sandstone rapid antiquing in the restoration of ancient buildings, based on a restoration project of an ancient town in Ganzhou. The method and the implementation process of red sandstone rapid antiquing are proposed by starting from color antiquing and texture antiquing. By controlling the concentration of red mud, grass ash, and carbon black in color coatings as variables, using the HSV (hue, saturation, value) color space and Tamura texture features (roughness, contrast, orientation) to quantitatively analyze the antiquing effect, an analytical model for evaluating the red sandstone antiquing effect based on image processing was established. The results showed that among all the antiquing groups, the group that used white cement, green zeolite, imitation greenery, red clay, grass ash, and 5 mL/L carbon black liquid at the same time had the best effect, with a qualified rate of 90%. The analytical model can improve the evaluation efficiency of red sandstone antiquing and avoid errors caused by subjective factors. With feasibility and practicability, the model is conducive for new red sandstone to meet the requirements of ancient building restoration through rapid antiquing. It provides a scientific basis and technical reference for red sandstone antiquing in stone cultural relics and ancient building restoration.

Classification of wheat powdery mildew based on hyperspectral: From leaves to canopy

Powdery mildew is considered to be one of the important diseases that threaten wheat production. It can cause serious economic losses and damage food security. Therefore, early detection of powdery mildew infection is essential to limit its amplification in reproduction. The purpose of this study is to use the hyperspectral image (HSI) to detect wheat powdery mildew (WPM) in an objective and accurate way. A method is proposed to classify the infection degree of WPM by combining cascade interval selection sensitive band with support vector machine (SVM). Firstly, HSI of leaf and canopy scale are obtained, and the image was segmented based on HSV (hue, saturation, value) color space and OTSU algorithm (HSV + OTSU). The spectral reflectance data were extracted after removing the background region of interest (ROI), and then the multiple scattering correction (MSC) was performed. The spectral data after MSC was called RAW, and then the first derivative (D1) and second derivative (D2) operations were performed respectively. Subsequently, the method of backward interval partial least squares (BiPLS) was used to screen out the characteristic bands of the sensitive interval. Finally, the classification models of WPM disease degree at the leaf scale and the canopy scale (RAW-BiPLS-SVM, D1-BiPLS-SVM and D2-BiPLS-SVM) were established respectively. The results showed that the spectral data after derivative operation was enhanced, and the classification effect of D1-BiPLS-SVM model had advantages at both leaf and canopy scales, in which the overall accuracy (OA) at the leaf scale was 95.2 %, and the OA at canopy scale was 74.4 %. In order to increase the difference between diseases, the canopy-scale disease severity levels were integrated. The correct rates of health and disease were 96.9 % and 99.2 %, respectively, and the OA of disease grade recombination was 87.9 %. Therefore, the method proposed in this paper can not only accurately identified healthy and infected wheat, but also realized the classification of different degrees of WPM, and provide technical support for the prevention and control of WPM.

A New Remote Sensing Desert Vegetation Detection Index

Land desertification is a key environmental problem in China, especially in Northwest China, where it seriously affects the sustainable development of natural resources. In this paper, we combine high-resolution satellite remote sensing images and UAV (unmanned aerial vehicle) visible light images to extract desert vegetation data and quickly locate and accurately monitor land desertification in relevant areas according to changes in vegetation coverage. Due to the strong light and dry climate of deserts in Northwest China, which results in deeper vegetation shadow texture and mostly dry shrubs with fewer stems and leaves, the accuracy of the vegetation index commonly used in visible remote sensing image classification is not able to meet the requirements for monitoring and evaluating land desertification. For this reason, in this paper, we took the Hangjin Banner in Bayannur as an example and constructed a new vegetation index, the HSVGVI (hue–saturation–value green enhancement vegetation index), based on the HSV (hue–saturation–value) color space using channel enhancement that can improve the extraction accuracy of desert vegetation and reduce misclassification. In addition, in order to further test the extraction accuracy, samples of densely vegetated and multi-shaded areas were divided in the study area according to the accuracy-influencing factors. At the same time, the HSVGVI was compared with the vegetation indices EXG (excess green index), RGBVI (red–green–blue vegetation index), MGRVI (modified green–red vegetation index), NGBDI (normalized green–red discrepancy index), and VDVI (visible-band discrepancy vegetation index) constructed based on the RGB (red–green–blue) color space. The experimental results show that the extraction accuracy of the EXG and other vegetation indices constructed in RGB color space can only reach 70%, while the extraction accuracy of the HSVGVI can reach more than 95%. In summary, the HSVGVI proposed in this paper can better realize the extraction of desert vegetation data and can provide a reliable technical tool for monitoring and evaluating land desertification.

A Hue-Value method for semi-automated assessment of Lid Wiper Epitheliopathy

BackgroundLid wiper epitheliopathy (LWE) is a marker of an abnormal lid/cornea interaction. This study proposes an automated Hue-Value grading algorithm of LWE staining following manual selection of the region of interest. MethodsImages of LWE staining were processed using Hue and Value from HSV (Hue-Saturation-Value) color space with a custom MATLAB program. Thirty-one images were successfully analyzed. Examiners analyzed images in random order twice, separated by more than a week. Bland Altman and Intraclass Correlation Coefficients (ICC) were performed. ResultsThere was no difference (p > 0.05) between upper (UL) and lower (LL) eyelids for LWE height (UL: 0.12 ± 0.12 mm, LL: 0.12 ± 0.07 mm), width (UL: 10.70 ± 3.84 mm, LL: 10.26 ± 3.49 mm), or area (UL: 2.85 ± 2.67 mm2, LL: 2.63 ± 1.71 mm2). There was no between examiner difference for all eyelid LWE height or area (p > 0.05), but a difference in LWE width (0.16 mm; p = 0.031). ICC for LWE height, width and area were 0.996 (95% CI: 0.993 to 0.998), 0.997 (95% CI: 0.992 to 0.998) and 0.999 (95% CI: 0.998 to 0.999). There was no between examiner difference for height or area (p > 0.05) for UL, but a difference in LWE width (0.28 mm; p = 0.026). ICC for height, width and area were 0.999 (95% CI: 0.996 to 1.00), 0.995 (95% CI: 0.982 to 0.999) and 1.00 (95% CI: 0.999 to 1.00). There was no difference in LWE height, width or area for LL (all p > 0.05). ICC were 0.991 (95% CI: 0.973 to 0.997) for height, 0.998 (95% CI: 0.995 to 0.999) for width and 0.997 (95% CI: 0.990 to 0.999) for area. ConclusionsThis novel method results in highly repeatable interexaminer measures of LWE staining after general lid region delineation. Small differences in LWE width were observed between examiners.

Detect Lane Line for Self-Driving Car Using Hue Saturation Lightness and Hue Saturation Value Color Transformation

Self-driving vehicles require the ability to perceive and understand their surroundings, just like human drivers. It entails navigating efficiently on roads, obeying traffic signs and signals, and avoiding collisions with other vehicles and pedestrians. To address the challenges associated with object detection in self-driving cars, an effort was made to demonstrate lane detection using the OpenCV library. To achieve this goal, the well-established probabilistic Hough transform technique is used for line detection. Before applying Hough transforms, several pre-processing techniques are used, including converting the image to grayscale, camera calibration, and implementing a masking filter. In addition, edge detection is performed using the edge detection method. The study also indicates a preference for the use of HSL (Hue, Saturation, and Lightness) and HSV (Hue, Saturation, Value) color spaces. When HSL is applied, white lines appear purer and brighter, resulting in superior performance compared to using HSV specifically to detect white. This algorithm proved particularly effective in detecting straight lanes, which achieved an accuracy ratio of 96.06%. By incorporating these methodologies, the lane detection algorithm implemented with the OpenCV library addresses the challenges of self-driving vehicles, providing them with improved perception capabilities similar to human drivers.

Development of Seeding Rate Monitoring System Applicable to a Mechanical Pot-Seeding Machine

In this study, we developed a monitoring system to accurately track the seeding rate and to identify the locations where the mechanical pot-seeding machine failed to sow seeds correctly. The monitoring system employs diverse image processing techniques, including the Hough transform, hue–saturation–value color space conversion, image morphology techniques, and Gaussian blur, to accurately pinpoint the seeding rate and the locations where seeds are missing. To determine the optimal operating conditions for the seeding rate monitoring system, a factorial experiment was conducted by varying the brightness and saturation values of the image data. When the derived optimal operating conditions were applied, the system consistently achieved a 100% seed recognition rate across various seeding conditions. The monitoring system developed in this study has the potential to significantly reduce the labor required for supplementary planting by enabling the real-time identification of locations where seeds were not sown during pot-seeding operations.

Bolt looseness detection based on Canny edge detection algorithm

SummaryAiming at current serious problems that the bolt looseness can cause heavy damages and the manual bolt looseness detection is ineffective, this study presents a bolt looseness detection method based on Canny edge detector. First, HSV (hue, saturation, value) color space is used to extract the area of mark bar. Then, after expanding, eroding and median filtering the image, Canny edge detector is used to segment the marker area to extract its edge. Last, the fitted slope of edge is used to judge whether the bolt is loose. The experimental results show that the proposed method has F1 score of 88.37%, contributing to the automated detection of bolt looseness.

Compressive Sensing of Medical Images Based on HSV Color Space.

Recently, compressive sensing (CS) schemes have been studied as a new compression modality that exploits the sensing matrix in the measurement scheme and the reconstruction scheme to recover the compressed signal. In addition, CS is exploited in medical imaging (MI) to support efficient sampling, compression, transmission, and storage of a large amount of MI. Although CS of MI has been extensively investigated, the effect of color space in CS of MI has not yet been studied in the literature. To fulfill these requirements, this article proposes a novel CS of MI based on hue-saturation value (HSV), using spread spectrum Fourier sampling (SSFS) and sparsity averaging with reweighted analysis (SARA). An HSV loop that performs SSFS is proposed to obtain a compressed signal. Next, HSV-SARA is proposed to reconstruct MI from the compressed signal. A set of color MIs is investigated, such as colonoscopy, magnetic resonance imaging of the brain and eye, and wireless capsule endoscopy images. Experiments were performed to show the superiority of HSV-SARA over benchmark methods in terms of signal-to-noise ratio (SNR), structural similarity (SSIM) index, and measurement rate (MR). The experiments showed that a color MI, with a resolution of 256×256 pixels, could be compressed by the proposed CS at MR of 0.1, and could be improved in terms of SNR being 15.17% and SSIM being 2.53%. The proposed HSV-SARA can be a solution for color medical image compression and sampling to improve the image acquisition of medical devices.

An automated pipeline for supervised classification of petal color from citizen science photographs.

Petal color is an ecologically important trait, and uncovering color variation over a geographic range, particularly in species with large distributions and/or short bloom times, requires extensive fieldwork. We have developed an alternativemethod that segments images from citizen science repositories using Python and k-means clustering in the hue-saturation-value (HSV) color space. Our method uses k-means clustering to aggregate like-color pixels in sample images to generate the HSV color space encapsulating the color range of petals. Using the HSV values, our method isolates photographs containing clusters in that range and bins them into a classification scheme based on user-defined categories. We demonstrate the application of this method using two species: one with a continuous range of variation of pink-purple petals in Geranium maculatum, and one with a binary classification of white versus blue in Linanthus parryae. We demonstrate results that are repeatable and accurate. This method provides a flexible, robust, and easily adjustable approach for the classification of color images from citizen science repositories. By using color to classify images, this pipeline sidesteps many of the issues encountered using more traditional computer vision applications. This approach provides a tool for making use of large citizen scientist data sets.

Classification of tomato (Lycoersicon Esculentum Miil) ripeness levels based on HSV color using digital image processing

Tomato is a fruit that undergoes a rapid ripening process. Classification of tomatoes based on the level of ripeness is very important during the distribution process of tomatoes in various regions. In general, farmers classify the ripeness level of tomatoes based on fruit color, because this is easy to do. Generally, the classification of the ripeness level of tomatoes is done manually by looking directly with the eyes and based on the color of the fruit. The problem is, manually identifying the level of ripeness of tomatoes based on color still has many shortcomings and the results are less than optimal. This is because humans have many limitations such as differences in perceptions by farmers, fatigue, lack of focus, relatively long time required and produce a variety of products due to human visual limitations and differences in perceptions about the quality of tomatoes. One alternative technology that can be used to reduce this problem is to use digital image processing with color spaces such as hue, saturation, value (HSV). HSV color has a tendency to detect color with a degree of dominance to get accurate object recognition results. Using digital image processing techniques, information is obtained that can be processed by a computer and the results are more precise, fast and automatic. Based on the results of research using image processing with HSV color space can identify the maturity level of tomatoes at three levels of ripeness, unripe, half-ripe and ripe.

Tongue diagnosis based on hue-saturation value color space: controlled study of tongue appearance in patients treated with percutaneous coronary intervention for coronary heart disease

ObjectiveTo analyze the characteristics of tongue imaging color parameters in patients treated with percutaneous coronary intervention (PCI) and non-PCI for coronary atherosclerotic heart disease (CHD), and to observe the effects of PCI on the tongue images of patients as a basis for the clinical diagnosis and treatment of patients with CHD. MethodsThis study used a retrospective cross-sectional survey to analyze tongue photographs and medical history information from 204 patients with CHD between November 2018 and July 2020. Tongue images of each subject were obtained using the Z-BOX Series traditional Chinese medicine (TCM) intelligent diagnosis instruments, the SMX System 2.0 was used to transform the image data into parameters in the HSV color space, and finally the parameters of the tongue image between patients in the PCI-treated and non-PCI-treated groups for CHD were analyzed. ResultsAmong the 204 patients, 112 were in the non-PCI treatment group (38 men and 74 women; average age of (68.76 ± 9.49) years), 92 were in the PCI treatment group (66 men and 26 women; average age of (66.02 ± 10.22) years). In the PCI treatment group, the H values of the middle and tip of the tongue and the overall coating of the tongue were lower (P < 0.05), while the V values of the middle, tip, both sides of the tongue, the whole tongue and the overall coating of the tongue were higher (P < 0.05). ConclusionThe color parameters of the tongue image could reflect the physical state of patients treated with PCI, which may provide a basis for the clinical diagnosis and treatment of patients with CHD.

Skin color classification of Koreans using clustering.

Skin color is used as an index for diagnosing and predicting skin irritation, dermatitis, and skin conditions because skin color changes based on various factors. Therefore, a new method for consistently and accurately evaluating skin color while overcoming the limitations of the existing skin color evaluation method was proposed, and its usefulness was demonstrated. Skin color was quantified using the RGB (Red, Green, Blue), HSV (Hue Saturation Value), CIELab, and YCbCr color spaces in the acquired Korean skin images, which were classified through clustering. In addition, the classification performances of the existing visual scoring method and the proposed skin color classification method were compared and analyzed using multinomial logistic regression, support vector machine, K-nearest neighbor, and random forest. After quantifying the skin color through the color space conversion of the skin image, the skin color classification performance according to the number of quantified features and the classifier was verified. In addition, the usefulness of the proposed classification method was confirmed by comparing its classification performance with that of the existing skin color classification method. In this study, a method was proposed to objectively classify skin color values quantified from skin images of Koreans acquired using a digital camera through clustering. To verify the proposed method, its classification performance was compared with that of the existing classification method, and an optimized classification method was presented for the classification of Korean skin color. Thus, the proposed method can objectively classify skin color and can be used as a cornerstone in research to quantify skin color and establish objective classification criteria.

Comparison of K-Means Clustering and Otsu Thresholding Methods in the Detection of Tuberculosis Extra Pulmonary Bacilli in the HSV Color Space

Tuberculosis Extra Pulmonary (TBEP) is an infectious disease caused by the bacterium Mycobacterium tuberculosis and can cause death. Patients suffering from this disease must be treated quickly without waiting long. Currently, anyone who will be detected caused by this bacterium takes a long time and costs a lot. The biopsy is one of the techniques used to take the patient's lung fluid and give Ziehl Neelsen chemical dye and then observe using a microscope to determine this TBEP disease. This research aims to help detect bacteria quickly and precisely by performing computer-aided image processing by creating an application system. The technique used is to develop the segmentation method. The segmentation process is to develop a Hue Saturation Value (HSV) color space transformation technique with the K-Means and Otsu Thresholding techniques. From the results of the two methods used, it turns out that the Otsu Thresholding method can detect TBEP results with more accuracy than the K-Means method. So the method developed is beneficial in accelerating and minimizing costs for detecting TBEP.

EVALUATION OF MACHINE LEARNING MODELS TO IDENTIFY PEACH VARIETIES BASED ON LEAF COLOR

Machine learning and deep learning approaches are applied in different areas of the agricultural sector, particularly in the digital image-based identification of characteristics of interest in crops. In this research, the performance of three machine learning classifiers was evaluated: support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP). The aim was to identify four varieties of peach (Prunus persica L. Batsch) (CP-03-06, Oro Azteca, Oro San Juan, and Cardenal), based on the color of digital images of the upper and lower side of leaves, represented by two color spaces: RGB (red, green, blue) and HSV (hue, saturation, value). The classifiers were trained and evaluated based on six data input scenarios, defined by the combinations of the upper, lower, and both sides of the leaf with the RGB and HSV color spaces. The three machine learning classifiers (SVM, RF, and MLP) achieved their best prediction performance when they examined the color characteristics of the upper side of leaves transformed to the HSV color space. The SVM classifier outperformed RF and MLP. SVM achieved a global average correct classification accuracy of 84.1 %, F1 macro of 83.7 %, and area under the ROC curve (AUC macro) of 0.93. The Oro Azteca variety reached the highest classification rate with a F1 score of 87.9 % and the Oro San Juan variety obtained the lowest rate with a F1 of 71.3 %.

Mapping Motion Direction to Color in Ground-Motion Visualizations—A Tool with Potential Applications in Different Settings

Abstract The increase in seismometer density on a continental scale since the start of the EarthScope project has enabled the community to produce visualizations of the propagating wavefield from local and teleseismic events. Previous work has shown that these animations generate much interest yet have limitations and can be confusing to novices. Here, we present a new type of visualization in which the color, position, and size of the symbols representing each seismometer are time dependent and determined by the instantaneous proportion of motion in the Z, R, and T components. This color-mapping scheme has the advantage of automatically producing different colors for different wave types and results in vibrant animations. The color mapping is based on transforming the value of the envelopes of the filtered velocity traces to red–green–blue values and subsequently boosting the color saturation in the hue–saturation–value color space. Animations in map view and along great-circle paths have been produced and used in a formal classroom setting and have also been shared with the broader public through social media channels. In both formal and informal settings, the animations have succeeded in garnering attention and stimulating discussion.

Stateful-Service-Based Pupil Recognition in Natural Light Environments.

Smartphones are currently extensively used worldwide, and advances in hardware quality have engendered improvements in smartphone image quality, which is occasionally comparable to the quality of medical imaging systems. This paper proposes two algorithms for pupil recognition: a stateful-service-based pupil recognition mechanism and color component low-pass filtering algorithm. The PRSSM algorithm can determine pupil diameters in images captured in indoor natural light environments, and the CCLPF algorithm can determine pupil diameters in those captured outdoors under sunlight. The PRSSM algorithm converts RGB colors into the hue saturation value color space and performs adaptive thresholding, morphological operations, and contour detection for effectively analyzing the diameter of the pupil. The CCLPF algorithm derives the average matrix for the red components of eye images captured in outdoor environments. It also performs low-pass filtering, morphological and contour detection operations, and rule-of-thumb correction. This algorithm can effectively analyze pupil diameter in outdoor natural light. Traditional ruler-based measurements of pupil diameter were used as the reference to verify the accuracy of the PRSSM and CCLPF algorithms and to compare their accuracy with that of the other algorithm. The errors in pupil diameter data were smaller for the PRSSM and CCLPF algorithms than for the other algorithm.