Value Color Space Research Articles

Effect of Enamel Surface Coating on Staining Capability Due to Iron-Containing Supplements on Primary Teeth: An in Vitro Study

ABSTRACT Background: The staining of primary teeth due to iron-containing supplements is a common concern among parents and healthcare providers. Materials and Methods: Forty extracted primary teeth were divided into two groups of 20 each. Group A served as the control and received no enamel coating, while Group B was treated with a commercially available enamel surface coating. Both groups were then exposed to iron-containing supplements for 7 days. The teeth were assessed for color changes using a spectrophotometer before and after exposure. The degree of staining was quantified using the CIELAB color space values, specifically the ΔE* value, which indicates the change in color. Results: The mean ΔE* value in Group A (control) was 12.5 ± 1.8, indicating significant staining. In contrast, Group B (coated) showed a mean ΔE* value of 4.2 ± 1.1, reflecting minimal staining. The difference between the two groups was statistically significant (P < 0.001), demonstrating the effectiveness of the enamel surface coating in reducing staining caused by iron supplements. Conclusion: Enamel surface coatings significantly reduce the staining effect of iron-containing supplements on primary teeth.

Dynamic assessment of visual fatigue during video watching: Validation of dynamic rating based on post-task ratings and video features

People watching video displays for long durations experience visual fatigue and other symptoms associated with visual discomfort. Fatigue-reduction techniques are often applied but may potentially degrade the immersive experience. To appropriately adjust fatigue-reduction techniques, the changes in visual fatigue over time should be analyzed which is crucial for the appropriate adjustment of fatigue-reduction techniques. However, conventional methods used for assessing visual fatigue are inadequate because they rely entirely on post-task surveys, which cannot easily determine dynamic changes. This study employed a dynamic assessment method for evaluating visual fatigue in real-time. Using a joystick, participants continuously evaluated subjective fatigue whenever they perceived changes. A Simulator Sickness Questionnaire (SSQ) validated the results, which indicated significant correlations between dynamic assessments and the SSQ across five items associated with symptoms associated with visual discomfort. Furthermore, we explored the potential relationship between dynamic visual fatigue and objective video features, e.g., optical flow and the V-values of the hue/saturation value (HSV) color space, which represent the motion and brightness of the video. The results revealed that dynamic visual fatigue significantly correlated with both the optical flow and the V-value. Moreover, based on machine learning models, we determined that the changes in visual fatigue can be predicted based on the optical flow and V-value. Overall, the results validate that dynamic assessment methods can form a reliable baseline for real-time prediction of visual fatigue.

Effect of the addition of different forms of rosemary (Rosmarinus officinalis L.) on the quality of vacuum-packed minced pork

Abstract Introduction Apart from their antioxidant activity, plant-derived bioactive compounds can also positively affect the quality of meat and meat products by improving their sensory and microbiological properties and preventing discolouration. The aim of this study was to determine how the addition of different forms of rosemary improved the quality of pork. Material and Methods Minced pork samples were divided into a control sample without additives (C) and three experimental samples with certified additives (15 mg/kg of meat each), i.e. rosemary oleoresin (ROL), extract (REX) and essential oil (REO). Each was further divided into three subsamples; the first was evaluated before storage, and the second and third were evaluated after respective 7- and 14-day vacuum-packed storage at 2°C. The TBARS value was expressed as mg of malondialdehyde (MDA) per kg of meat. Colour was determined based on the values of the standard colour space values of L* (lightness), a* (redness) and b* (yellowness) as well as C* (chroma) and h° (hue angle). Sensory attributes of the samples were evaluated on a nine-point scale. The pour-plating procedure was used for the enumeration of Pseudomonas, mesophilic lactic acid bacteria, psychrotrophic bacteria and rods of the Enterobacteriaceae family. Within each bacterial group, the most common colonies were identified by matrix-assisted laser desorption and ionization (MALDI). Results Lipid oxidation was most effectively inhibited by REO. The addition of ROL and REO to pork lightened its colour. Meat with REO had stronger redness, whereas meat with ROL had stronger yellowness. The addition of REX affected the sensory properties of pork most beneficially. Neither Enterobacteriaceae nor Pseudomonas spp. were detected in REO pork, which also contained lower counts of lactic acid bacteria than group C pork. Conclusion The results of this study indicate that rosemary has antioxidant and antimicrobial properties, and may improve the colour and sensory attributes of pork. The effect exerted by rosemary on meat quality may vary depending on the physical form of the additive.

Terminal sequence consistency verification method for small diameter abreast optical fibers based on computer vision

In a kind of precision industrial equipment, small diameter abreast optical fibers are used for high-speed communication among functional nodes. The arrangement order at both terminals of the abreast optical fibers need to comply with communication protocols. In this paper, we propose an automatic terminal sequence consistency verification method based on computer vision. The Hue Saturation Value (HSV) color space is used for improving the image feature extraction capability. An abreast optical fiber sequence dictionary which converts the protocol logic into an input-output mapping table is provided to follow protocol confidentiality and improve inspecting speed. A light control baffle position adaptive algorithm is designed for improving the accuracy of optical fiber incident light control. The experimental results show that the method can achieve the conductivity inspection of 1 optical fiber every 50 seconds, and the inspection accuracy is over 96.5%, which generally improves the inspection efficiency by 45% compared with manual inspection.

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.

Assessment of Tooth Shade Measurement Precision and Consistency with Digital Photography Calibration System.

To ascertain the degree of precision and repeatability inherent in the "capture to edit" digital imaging system, namely the ColorChecker Passport Photo (X-Rite, MI, USA), and to juxtapose its performance against spectrophotometric assessments utilizing the Vita Easyshade® (Vita Zahnfabrik, H Rauter GmbH & Co. KG., Bad Sackingen, Germany) for the same set of teeth. Eighty participants aged 19-25 were included in the study; all of them were Croatian students at the School of Dental Medicine, University of Zagreb. Color measurements of the maxillary right central incisors were performed in controlled, standardized laboratory conditions. The measurements were performed on calibrated digital photographs by an experienced clinician three times in a row, and L, a, and b of CIELAB color space values were recorded. The same procedure was performed using the Vita Easyshade 5.0 spectrophotometer (Vita Zahnfabrik, Bad Säckingen, Germany) in "Tooth single" mode. The Cronbach's alpha was used as a repeatability measurement, thus revealing high consistency for both methods (α=0.94-1). Correlations were moderate (Pearson r=0.44-0.66 p<0.05). Testing confirmed that digital photography is inaccurate as spectrophotometry (p<0.05). An analysis of errors of means and their standard deviations revealed consistent discrepancies with minor deviations. The "capture to edit" method showed high consistency (Cronbach's α=0.94-0.96), and it exhibits comparability in terms of variability, reliability, and repeatability when compared to spectrophotometric measurement (VitaShade 5, VitaZahnfabrik). Disparities between the methods are evident and anticipated, yet they do not follow a consistently linear pattern. The combination of repeatability and consistency renders the "capture to edit" method a valuable tool for enhanced color mapping, thus facilitating comparisons between digital images.

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.

Multi-species weed detection and variable spraying system for farmland based on W-YOLOv5

Weed infestations have the potential to cause significant economic losses for farmers as a result of diminished crop yields and escalated labor and input costs linked with weed management. Traditional weed control methods often entail indiscriminate application of herbicides across entire fields, irrespective of weed density or species composition. In this investigation, we propose a hierarchical detection algorithm for identifying multi-species weeds and have devised a variable spraying system in agricultural settings. The weed classification detection algorithm comprises two components: crop seedling target detection and weed detection. The enhanced YOLOv5 algorithm was initially merged with Vision Transformer (ViT) to introduce the W-YOLOv5 crop seedling target detection algorithm. Experimental validation results indicate that the mean average precision (mAP) of the proposed W-YOLOv5 stands at 87.6%, representing a 3.2% increase over the original YOLOv5. Compared with YOLOv7, this method demonstrates a 4.4% improvement in mAP, accompanied by an 80.14% reduction in floating point operations (FLOPs). These findings underscore the effectiveness of the proposed crop seedling target detection algorithm in ensuring detection accuracy while minimizing model FLOPs. Subsequently, following the detection of crop seedlings, the Hue, Saturation, and Value (HSV) color space filtering algorithm was employed to identify the locations of all weeds in the image, thereby facilitating the detection of weeds among wheat (Triticum aestivum L.), radish (Raphanus sativus L.), cucumber (Cucumis sativus L.), soybean (Glycine max (L.) Merr.), and corn (Zea mays L.) seedlings. Finally, the severity of weed infestation in farmland was classified into five levels. Leveraging these severity levels, we developed a variable spraying system and integrated the developed algorithm into the system. Field validation experiments demonstrate that at a speed of 4 km/h, the spraying accuracy of the system can reach 90.32%, effectively enabling precise variable spraying.

Effect of Various Types of Sugars on Antioxidant Activity and Physico-chemical Properties of Kombucha Fermented Whey

Kombucha or known as SCOBY (Symbiotic Culture of Bacteria and Yeast) is a fermented tea beverage with the addition of sugar then fermented for 7-14 days. Recent studies showed kombucha conceivably used for milk fermentation, such as fermented whey, yogurt, and fermented milks. Sugar is added to kombucha as carbon source for microorganisms in kombucha. Type of sugar, sugar concentration, tea concentration, temperature and incubation time are factors that affect kombucha fermentation. The aims of this study were to determine the effect of various types of sugars on antioxidant activity (2,2-diphenyl-1-picrylhydrazyl (DPPH)) and physicochemical properties (pH, titratable acidity, total dissolved solid, and CIE L* a* b* color space value). The various types of sugars were granulated white sugar (G1), coconut sugar (G2), palm sugar (G3) and no sugar addition as control (G0). Fermented whey using 10% kombucha liquor, 3% kombucha cellulose and 10% sugar addition, whey then fermented at 37oC for 14 hours. Result demonstrated that using of various types of sugars significantly affected antioxidant activity, CIE L*a*b* color space value, pH, titratable acidity, total dissolved solid and vitamin C content. The highest vitamin C content was G3 (0.91±0.05 mg/100ml), kombucha fermented whey using palm sugar. Any type of sugar could be used on kombucha fermented whey, and the highest antioxidant activity (DPPH) was fermented whey kombucha with the addition of coconut sugar (G2) (86.79±10.94%).

Comparison between clinical and computerized methods for assessing gingival pigmentation.

Digital computerized assessment can provide objective values for the measurement of gingival pigmentation. This study aims to compare the Commission Internationale de l'Eclairage Lab color space (CIELAB) values and the computerized intensity values (CIVs) from digital imaging with clinical evaluations using the Dummett-Gupta Oral Pigmentation Index (DOPI) for assessing gingival pigmentation in a multi-ethnic population. Digital photographs of 188 participants were taken using standardized parameters. The buccal gingival pigmentation was evaluated using three methods (a) a clinical evaluation by two independent assessors using the DOPI, (b) the CIELAB values using the Adobe Photoshop® software (Version 23.1.1) and (c) the CIV calculated using the ImageJ software (Version 1.53k). A hierarchical clustering analysis was used to identify colour groups that clustered together. Agreement between the clinical and digital categorization of the pigmentation was carried out using weighted kappa analysis. Agreements between CIELAB and CIV were compared using intra-class correlation coefficient. There was a statistically significant difference in the DOPI, the L*, a*, and b* coordinates, and the CIV between the different ethnic groups of the participants. Cluster analysis for the CIELAB and CIV both identified four clusters. The gingival pigmentation categorization using the L*, a*, and b* values moderately agreed with the clinical evaluation using the DOPI index while the categorization with the CIV was in slight agreement with the clinical evaluations. This study identified four clusters of gingival pigmentation in 188 multi-ethnic participants. The clusters, determined by CIELAB values, align with the clinical assessment of gingival pigmentation. Digital measurements derived from clinical photographs can serve as an effective means of pigmentation measurement in dental clinics.

Analysis of Skin Color Variation using CIELAB Index: An Empirical Study from Delhi, India

Skin color is a conspicuous trait regulated by complex metabolic processes and its significant diversity among Indian populations is quite remarkable making it an ideal choice for dermatological investigation. To elucidate skin pigment variation with respect to age, we studied 714 healthy individuals (aged 20-70 years) using CIELAB system. Skin reflectance was measured from the volar surface of upper arm using DSM II ColorMeter (Cortex Technology, Hadsund, Denmark) among population of Delhi, India to provide CIELAB color space values, L* (light/dark), a* (red/green), and b* (yellow/blue) followed by statistical analysis to examine the correlation between age and gender on skin on constitutive skin pigmentation. The studied parameters were observed to vary widely {L* (Range: 24.98-51.62, M=38.66, SD=4.41), a* (Range: 4.61-17.16, M=10.18, SD=2.03), and b* (Range: 20.16-34.83, M=28.93, SD=2.30)} across all age groups. ANOVA results suggest a statistically significant effect of age on skin lightness {F(4,709)=124.1332, p&lt;0.001}, redness {F(4,709)=20.0594, p&lt;0.001} and yellowness {F(4,709)=95.1434, p&lt;0.001}. Positive correlation was observed between age and both hue (ho) {r(712)=0.9027, p&lt;0.001} and chroma (C) {r(712)=0.9224, p&lt;0.001}. Significant effects of gender were noticed on skin lightness among all age groups, along with skin redness below the age of 40 years (p&lt;0.001). Notable color differences (∆Ε∗ab) were witnessed among males and females across all age groups in the studied population. The age group 21-30 years was found to have the highest ∆Ε∗ab value (3.5987), followed by 51-60 years (2.98), 31-40 years (2.5579), 61-70 years (2.1837), and 41-50 years (1.6639). We would like to highlight that skin lightness, redness, and yellowness differs significantly with age. Females were observed to be lighter in color as compared to males across all age groups. The findings of the current study would provide better understanding of skin color variation among Indian population.

Determining abrasion resistance of decorative coated wood-based panels using retinex model

An Image Processing Based Scrub Tester (IPBST) was used to imitate the effect of household chemicals on furniture and decoration elements. For this purpose, 8 mm-thick, bright white, acrylic coated medium density fiberboard (MDF), polyvinyl chloride coated MDF, MDF lam ready-to-use sheets, and cellulosic, polyurethane, acrylic, and water-based paint applied MDF sheets were used. Carbon fiber patterned decorative coating was applied to the prepared sample surfaces using the water transfer printing and ultraviolet printing methods. The surfaces of the samples were scrubbed with various household chemicals in accordance with the Turkish Standard TS EN ISO 11998. In the image processing phase, the images before and after scrubbing were first converted to hue, saturation, and value color space. The relationship between the abrasion measurement method of the proposed IPBST and the abrasion data obtained from the stereo microscope device was examined using the Pearson Correlation analysis. The relationship between both abrasion test methods was positive, very strong, and significant (0.81). Thus, the IPBST can be used as an alternative to industrial test devices as it obtains similar data.

A detection system for serum cholesterol based on the fluorescence color detection of beta-cyclodextrin-capped gold nanoclusters

Cholesterol is one of the major markers for cardiovascular diseases. Herein, a portable cholesterol measurement system based on fluorescence color detection was constructed by combining the high sensitivity of fluorescence analysis with the ease of color sensing to determine low levels of serum cholesterol. Cyclodextrin capping gold nanoclusters with blue-green emission were used as fluorescent probes because cholesterol exposure induced fluorescence enhancement of the probe due to the host–guest inclusion interaction between cholesterol and the cavity of cyclodextrin. The integrated sensing system consisted of modules including a microprocessor, a power supply, an LED light with a constant current source, an RGB color sensor, a display, and a darkroom. All the modules except the display screen were placed in a 3D printing darkroom to avoid interference from ambient light. An RGB color sensor TCS230 was applied to capture the RGB signals of the fluorescent color of the probe solution before and after cholesterol addition. Then the obtained RGB signals were converted into the signals in Hue, Saturation, and Value (HSV) color space with a central control chip STM32F407. The Hue value of the fluorescent color of the solution can discriminate the concentration change of cholesterol. Experimental results demonstrate that the system responds linearly to cholesterol in the concentration range of 20.00 ∼ 150.00 μmol·L-1 with a detection limit of 16.07 μmol·L-1 (3σ, n = 3). The detection of the system has good consistency and accuracy compared with the standard instrument, showing potential for the detection of low levels of serum cholesterol.

Machine Learning-Based Image Processing for Ice Concentration during Chukchi and Beaufort Sea Trials

Growing interest in finding the optimal route through the arctic ocean, and sea ice concentration is also emerging as a factor to be considered. In this paper, an algorithm to calculate the sea ice concentration was developed based on the images taken during the Arctic voyage of the Korean icebreaker ARAON in July 2019. A sea ice concentration calculation program was developed using the image processing functions in open-source image processing library, called OpenCV. To develop the algorithm, parameter studies were conducted on red, green, blue (RGB) color space and hue, saturation, value (HSV) color space, and k-means clustering. To verify the algorithm for sea ice concentration calculation, it was applied to images taken during Araon’s Arctic voyages. Lens curvature and view point were corrected through camera calibration. To improve the accuracy of sea ice concentration calculation, a binarization model based on random forest was proposed. A parameter study for training image numbers and tree numbers was conducted to establish the random forest model. The calculated sea ice concentrations by random forest and k-means clustering were compared and discussed.

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.

A smartphone-integrated imaging device for measuring nitrate and phosphate in soil and water samples

This research presents a smartphone-integrated imaging device to detect nitrate (NO3–) and phosphate (PO43-) in soil and water samples for agricultural input management and environmental pollution monitoring. This low-cost and field-deployable device uses a smartphone with a digital camera to take a color photograph of the extracted solution under test. The sensing principle is based on the standard colorimetric phenoldisulfonic acid and ascorbic acid methods for NO3– and PO43- in soil and water samples, respectively. A 3D printed device that houses the sensor's optical source and other optical components is coupled to the phone's built-in rear camera and turned into a compact handheld system. An android application, 'SMART NP,' is also developed to quantify the concentrations of NO3– and PO43- in soil and water samples using the Value (V) component of the Hue Saturation and Value (HSV) color space model. The results obtained with the proposed device were comparable to laboratory-grade spectrophotometer data. The limit of detection of the device for NO3– in soil, NO3– in water, PO43- in soil, and PO43- in water were calculated as 0.1 mg L-1, 0.07 mg L-1, 0.001 mg L-1, and 0.02 mg L-1, respectively, with good accuracy (≤1% bias) and precision (∼2% residual standard deviation). The device exhibited a sensitivity of 0.17 mg L-1, 0.12 mg L-1, 0.026 mg L-1, and 0.49 mg L-1 for NO3– in soil, NO3– in water, PO43- in soil, and PO43- in water, respectively. Overall, this sensing device has the potential to assist farmers or scientists in easily measuring NO3– and PO43- concentrations in soil and water media, without the need for laboratory equipment.

Multiclass Classification of Paddy Leaf Diseases Using Random Forest Classifier

With increase in population, improving the quality and quantity of food is essential. Paddy is a vital food crop serving numerous people in various continents of the world. The yield of paddy is affected by numerous factors. Early diagnosis of disease is needed to prevent the plants from successive stage of disease. Manual diagnosis by naked eye is the traditional method widely adopted by farmers to identify leaf diseases. However, when the task involves manual disease diagnosis, problems like the hiring of domain experts, time consumption, and inaccurate results will arise. Inconsistent results may lead to improper treatment of plants. To overcome this problem, automatic disease diagnosis is proposed by researchers. This will help the farmers to accurately diagnose the disease swiftly without the need for expert. This manuscript develops model to classify four types of paddy leaf diseases bacterial blight, blast, tungro and brown spot. To begin with, the image is preprocessed by resizing and conversion to RGB Red, Green and Blue (RGB) and Hue, Saturation and Value (HSV) color space. Segmentation is done. Global features namely: hu moments, Haralick and color histogram are extracted and concatenated. Data is split in to training part and testing part in 70:30 ratios. Images are trained using multiple classifiers like Logistic Regression, Random Forest Classifier, Decision Tree Classifier, K-Nearest Neighbor (KNN) Classifier, Linear Discriminant Analysis (LDA),Support Vector Machine (SVM) and Gaussian Naive Bayes. This study reports Random Forest classifier as the best classifier. The Accuracy of the proposed model gained 92.84% after validation and 97.62% after testing using paddy disordered samples. 10 fold cross validation is performed. Performance of classification algorithms is measured using confusion matrix with precision, recall, F1- score and support as parameters.

The bleaching effect of office bleaching agents containing S-PRG filler evaluated by pH value and electron spin resonance.

Surface reaction-type pre-reacted glass-ionomer (S-PRG) filler releases six types of ions with a neutralizing ability. This study evaluated the effect of S-PRG filler incorporation in an H2O2-based bleaching material on the bleaching efficacy, pH, and reaction state. The experimental bleaching material was formulated by the addition of 5% or 10% S-PRG fillers to the powder part. The stained bovine teeth were treated with the prepared bleaching paste. Commission internationale de l'éclairage (CIE) L*a*b* color space values were recorded before and after bleaching, and the color difference (ΔE) and whiteness index (WID) were calculated. Moreover, the used bleaching formulations were assessed for their pH values and reaction state by evaluating the oxidation state of manganese (Mn+2) using electron spin resonance (ESR). The results of ΔE and WID showed that the addition of S-PRG filler increased the bleaching effect, but there was no significant difference between the groups with 5% and 10% S-PRG filler. A significant increase in pH in S-PRG filler groups (5% pH 6.7, 10% pH 6.8) was found compared to the 0% group (pH 4.8). ESR measurements showed that the signal emitted from Mn+2 decreased over time. The S-PRG filler groups showed a significantly higher reduction in Mn+2 compared to the 0% group, with no significant difference between the 5% and 10% S-PRG groups. S-PRG filler addition resulted in improved bleaching efficacy, higher reaction rate, and pH values that are close to neutral. S-PRG filler addition may be effective on thebleachingoutcome of H2O2-based materials.

A novel coal-gangue recognition method in underground coal mine based on image processing

ABSTRACT This study proposes a coal-gangue recognition method based on Retinex and extreme learning machine. The traditional Retinex has many limitations, including halo artifacts, excessive enhancement, and noise amplification in dark areas. To handle these, first, a modified multi-scale Retinex image enhancement algorithm based on hue, saturation, value color space, and bidimensional empirical mode decomposition named modified multi-scale Retinex algorithm with color restoration (MMSRCR) is proposed herein. On-site images are used to verify the effectiveness of the MMSRCR. Second, a preliminary segmentation method for coal-gangue images based on the optimal entropy threshold optimized by the improved quantum genetic algorithm is designed. The proposed segmentation method performs excellent stability compared with other traditional methods. Third, the adhesion part of the ore is further segmented based on the morphology opening operation and watershed algorithm. Then, the feature space of each enclosed area is extracted using the chain code and line segment tables. The improved extreme learning machine completes the classification of these areas, and the coal-gangue visual ratio recognition method is proposed. Finally, an experimental platform for visual ratio recognition is built to verify the proposed method. The experimental results show that the proposed method can accurately identify the visual ratio of coal-gangue.