Search Results (1,973)

The aim of this study was to develop a mathematical model to predict the body weight of tilapia fingerlings using variables obtained through computer vision. A total of 2092 tilapia fingerlings and juveniles, weighing between 10 and 100 g, were fasted for 12 h, anesthetized, weighed, and photographed using an iPhone 12 Pro Max at 33 cm height in a closed container with different bottom colors. Images were segmented using Roboflow’s instance segmentation model, achieving 99.5% mean average precision, 99.9% precision, and 100% recall. From the segmented images, area, perimeter, major axis (MA), minor axis (SA), X and Y centroids, compactness, eccentricity, and the MA/SA ratio were extracted. Seventy percent of the data was used to build the model, and 30% for validation. Stepwise multiple regression (backward selection) was performed, using body weight as the dependent variable. The prediction model was: −17.7677 + 0.0007539(area) – 0.0848303 (MA) – 0.108338(SA) + 0.0034496(CX). The validation model showed similar coefficients and R² = 0.99. The second validation, using observed versus predicted values, also yielded an R² of 0.99 and a mean absolute error of 1.57 g. Correlation and principal component analyses revealed strong positive associations among body weight, area, axes, and predicted values. Computer vision proved effective for predicting tilapia fingerlings’ weight. Full article

In this paper, we propose noncontact heart rate (HR), oxygen saturation (SpO2), and respiratory rate (RR) detection methods using a smartphone camera. HR frequency is detected through filtering after obtaining a remote PPG (rPPG) signal and its power spectral density (PSD) is detected using color difference signal amplification and the plane-orthogonal-to-the-skin method. Additionally, the SpO2 is detected using the HR frequency and the absorption ratio of the G and B color channels based on oxyhemoglobin absorption and reflectance theory. After this, the respiratory frequency is detected using the PSD of rPPG through respiratory frequency band filtering. For the image sequences recorded under various imaging conditions, the proposed method demonstrated superior HR detection accuracy compared to existing methods. The confidence intervals for HR and SpO2 detection were analyzed using Bland–Altman plots. Furthermore, the proposed RR detection method was also verified to be reliable. Full article

One of the persistent challenges in multispectral image analysis is the interference caused by dense cloud cover and its resulting shadows, which can significantly obscure surface features. This becomes especially problematic when attempting to monitor surface changes over time using satellite imagery, such as from Landsat-8. In this study, rather than simply masking visual obstructions, we aimed to investigate the role and influence of clouds within the spectral data itself. To achieve this, we employed Independent Component Analysis (ICA), a statistical method capable of decomposing mixed signals into independent source components. By applying ICA to selected Landsat-8 bands and analyzing each component individually, we assessed the extent to which cloud signatures are entangled with surface data. This process revealed that clouds contribute to multiple ICA components simultaneously, indicating their broad spectral influence. With this influence on multiple wavebands, we managed to configure a set of components that could perfectly delineate the extent and location of clouds. Moreover, because Landsat-8 lacks cloud-penetrating wavebands, such as those in the microwave range (e.g., SAR), the surface information beneath dense cloud cover is not captured at all, making it physically impossible for ICA to recover what is not sensed in the first place. Despite these limitations, ICA proved effective in isolating and delineating cloud structures, allowing us to selectively suppress them in reconstructed images. Additionally, the technique successfully highlighted features such as water bodies, vegetation, and color-based land cover differences. These findings suggest that while ICA is a powerful tool for signal separation and cloud-related artifact suppression, its performance is ultimately constrained by the spectral and spatial properties of the input data. Future improvements could be realized by integrating data from complementary sensors—especially those operating in cloud-penetrating wavelengths—or by using higher spectral resolution imagery with narrower bands. Full article

Image binarization algorithms reduce the original color space to only two values, black and white. They are an important preprocessing step in many computer vision applications. Image binarization is typically performed using a threshold value by classifying the pixels into two categories: lower and higher than the threshold. Global thresholding uses a single threshold value for the entire image, whereas local thresholding uses different values for the different pixels. Although slower and more complex than global thresholding, local thresholding can better classify pixels in noisy areas of an image by considering not only the pixel’s value, but also its surrounding neighborhood. This study introduces a local thresholding method that uses the results of several local thresholding algorithms and other image statistics to train a decision tree ensemble. Through cross-validation, we demonstrate that the model is robust and performs well on new data. We compare the results with state-of-the-art solutions and reveal significant improvements in the average F-measure for all DIBCO datasets, obtaining an F-measure of 95.8%, whereas the previous high score was 93.1%. The proposed solution significantly outperformed the previous state-of-the-art algorithms on the DIBCO 2019 dataset, obtaining an F-measure of 95.8%, whereas the previous high score was 73.8%. Full article

To overcome the complexity of yarn color measurement using spectrophotometry with yarn winding techniques and to enhance consistency with human visual perception, a yarn color measurement method based on digital photography is proposed. This study employs a photographic colorimetry system to capture digital images of single yarns. The yarn and background are segmented using the K-means clustering algorithm, and the centerline of the yarn is extracted using a skeletonization algorithm. Spectral reconstruction and colorimetric principles are then applied to calculate the color values of pixels along the centerline. Considering the nonlinear characteristics of human brightness perception, the final yarn color is obtained through a nonlinear texture-adaptive weighted computation. The method is validated through psychophysical experiments using six yarns of different colors and compared with spectrophotometry and five other photographic measurement methods. Results indicate that among the seven yarn color measurement methods, including spectrophotometry, the proposed method—based on centerline extraction and nonlinear texture-adaptive weighting—yields results that more closely align with actual visual perception. Furthermore, among the six photographic measurement methods, the proposed method produces most similar to those obtained using spectrophotometry. This study demonstrates the inconsistency between spectrophotometric measurements and human visual perception of yarn color and provides methodological support for developing visually consistent color measurement methods for textured textiles. Full article

In this paper, we aim to develop an efficient algorithm for the solving Tensor Robust Principal Component Analysis (TRPCA) problem, which focuses on obtaining a low-rank approximation of a tensor by separating sparse and impulse noise. A common approach is to minimize the convex surrogate of the tensor rank by shrinking its singular values. Due to the existence of various definitions of tensor ranks and their corresponding convex surrogates, numerous studies have explored optimal solutions under different formulations. However, many of these approaches suffer from computational inefficiency primarily due to the repeated use of tensor singular value decomposition in each iteration. To address this issue, we propose a novel TRPCA algorithm that introduces a new convex relaxation for the tensor norm and computes low-rank approximation more efficiently. Specifically, we adopt the tensor average rank and tensor nuclear norm, and further relax the tensor nuclear norm into a sum of the tensor Frobenius norms of the factor tensors. By alternating updates of the truncated factor tensors, our algorithm achieves efficient use of computational resources. Experimental results demonstrate that our algorithm achieves significantly faster performance than existing reference methods known for efficient computation while maintaining high accuracy in recovering low-rank tensors for applications such as color image recovery and background subtraction. Full article

Background/Objectives: Discrepancies in diabetic retinopathy (DR) grading are well-documented, with retinal non-perfusion (RNP) quantification posing greater challenges. This study assessed intergrader agreement in DR evaluation, focusing on qualitative severity grading and quantitative RNP measurement. We aimed to improve agreement through structured consensus meetings. Methods: A retrospective analysis of 100 comparisons from 50 eyes (36 patients) was conducted. Two paired medical retina fellows graded ultra-widefield color fundus photographs (CFP) and fundus fluorescein angiography (FFA) images. CFP assessments included DR severity using the International Clinical Diabetic Retinopathy (ICDR) grading system, DR Severity Scale (DRSS), and predominantly peripheral lesions (PPL). FFA-based RNP was defined as capillary loss with grayscale matching the foveal avascular zone. Weekly adjudication by a senior specialist resolved discrepancies. Intergrader agreement was evaluated using Cohen’s kappa (qualitative DRSS) and intraclass correlation coefficients (ICC) (quantitative RNP). Bland–Altman analysis assessed bias and variability. Results: After eight consensus meetings, CFP grading agreement improved to excellent: kappa = 91% (ICDR DR severity), 89% (DRSS), and 89% (PPL). FFA-based PPL agreement reached 100%. For RNP, the non-perfusion index (NPI) showed moderate overall ICC (0.49), with regional ICCs ranging from 0.40 to 0.57 (highest in the nasal region, ICC = 0.57). Bland–Altman analysis revealed a mean NPI difference of 0.12 (limits: −0.11 to 0.35), indicating acceptable variability despite outliers. Conclusions: Structured consensus training achieved excellent intergrader agreement for DR severity and PPL grading, supporting the clinical reliability of ultra-widefield imaging. However, RNP measurement variability underscores the need for standardized protocols and automated tools to enhance reproducibility. This process is critical for developing robust AI-based screening systems. Full article

Image-assisted digital shade guides are increasingly popular for shade matching; however, research on their accuracy remains limited. This study aimed to compare the accuracy and reliability of color coordination in image-assisted digital shade guides constructed using calibrated images of their shade tabs captured by a mirrorless camera (Canon, Tokyo, Japan) (MC-DSG) and a smartphone camera (Samsung, Seoul, Korea) (SC-DSG), using a spectrophotometer as the reference standard. Twenty-nine VITA Linearguide 3D-Master shade tabs were photographed under controlled settings with both cameras equipped with cross-polarizing filters. Images were calibrated using Adobe Photoshop (Adobe Inc., San Jose, CA, USA). The L* (lightness), a* (red-green chromaticity), and b* (yellow-blue chromaticity) values, which represent the color attributes in the CIELAB color space, were computed at the middle third of each shade tab using Adobe Photoshop. Specifically, L* indicates the brightness of a color (ranging from black [0] to white [100]), a* denotes the position between red (+a*) and green (–a*), and b* represents the position between yellow (+b*) and blue (–b*). These values were used to quantify tooth shade and compare them to reference measurements obtained from a spectrophotometer (VITA Easyshade V, VITA Zahnfabrik, Bad Säckingen, Germany). Mean color differences (∆E₀₀) between MC-DSG and SC-DSG, relative to the spectrophotometer, were compared using a independent t-test. The ∆E₀₀ values were also evaluated against perceptibility (PT = 0.8) and acceptability (AT = 1.8) thresholds. Reliability was evaluated using intraclass correlation coefficients (ICC), and group differences were analyzed via one-way ANOVA and Bonferroni post hoc tests (α = 0.05). SC-DSG showed significantly lower ΔE₀₀ deviations than MC-DSG (p < 0.001), falling within acceptable clinical AT. The L* values from MC-DSG were significantly higher than SC-DSG (p = 0.024). All methods showed excellent reliability (ICC > 0.9). The findings support the potential of smartphone image-assisted digital shade guides for accurate and reliable tooth shade assessment. Full article

The transport of intensity equation enables quantitative phase imaging from only two axially displaced intensity images, facilitating the characterization of low-contrast samples like cells and microorganisms. However, the rapid selection of the correct defocused planes, crucial for real-time phase imaging of dynamic events, remains challenging. Additionally, the different images are normally acquired sequentially, further limiting phase-reconstruction speed. Here, we report on a system that addresses these issues and enables user-tuned defocusing with snapshot phase retrieval. Our approach is based on combining multi-color pulsed illumination with acousto-optic defocusing for microsecond-scale chromatic aberration control. By illuminating each plane with a different color and using a color camera, the information to reconstruct a phase map can be gathered in a single acquisition. We detail the fundamentals of our method, characterize its performance, and demonstrate live phase imaging of a freely moving microorganism at speeds of 150 phase reconstructions per second, limited only by the camera’s frame rate. Full article

Insects’ body coloration may be indirectly influenced by their host plants. Perina nuda (Lepidoptera: Lymantriidae), commonly known as the Banyan Tussock Moth and a serious pest of banyan trees (Ficus spp.) in southern China, exhibits light body coloration during its first- to third-instar stages, with its coloration progressively darkening as it matures, but little is known of the relationship between larval body coloration and host plants. To address this gap, we examined the R (red), G (green), B (blue), and L (lightness) values of the head, dorsal thorax and abdomen, stripe, dorsal mid-line, and tail of larvae fed on different hosts and host endogenous substance by using quantitative image analysis and chemical determination. Our results revealed that larval body coloration exhibited conserved ontogenetic patterns but varied significantly with host species, developmental age, and anatomical region. Redundancy analysis identified chlorophyll-b as the dominant driver, strongly associating with dorsal thorax–abdomen pigmentation. Flavonoids exhibited subthreshold significance, correlating with darker dorsal mid-line coloration, while nutrients (sugars, proteins) showed negligible effects. Linear regression revealed weak but significant links between leaf and larval body coloration in specific body regions. These findings demonstrate that host plant endogenous substances play a critical role in shaping larval body coloration. This study provides a foundation for understanding the ecological and biochemical mechanisms underlying insect pigmentation, with implications for adaptive evolution and pest management strategies. Full article

As demand for plant-based beverages grows, analytical tools are needed to classify and understand their structural and compositional diversity. This study applied a multi-analytical approach to characterize 41 commercial almond-, oat-, rice- and soy-based beverages, evaluating attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy, protein secondary structure proportions, colorimetry, and microscopic image texture analysis. A total of 26 variables, derived from ATR-FTIR and protein secondary structure assessment, were employed in multivariate models, using partial least squares discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA) to evaluate classification performance. The results indicated clear group separation, with soy and rice beverages forming distinct clusters while almond and oat samples showing partial overlap. Variable importance in projection (VIP) scores revealed that β-turn and α-helix protein structures, along with carbohydrate-associated spectral bands, were the key features for beverages’ classification. Textural features derived from microscopy images correlated with sugar and carbohydrate content and color parameters were also employed to describe beverages’ differences related to sugar content and visual appearance in terms of homogeneity. These findings demonstrate that combining ATR-FTIR spectral data with protein secondary structure data enables the effective classification of plant-based beverages, while microscopic image textural and color parameters offer additional extended product characterization. Full article

The precise identification of hydrocarbon-rich zones is crucial for optimizing exploration and production processes in the oil industry. Magnetotelluric (MT) surveys play a fundamental role in mapping subsurface geological structures. This study presents a novel methodology for automatically delineating resistivity contrasts in MT models by employing advanced machine learning and computer vision techniques. This approach commences with data augmentation to enhance the diversity and volume of resistivity data. Subsequently, a bilateral filter was applied to reduce noise while preserving edge details within the resistivity images. To further improve image contrast and highlight significant resistivity variations, contrast-limited adaptive histogram equalization (CLAHE) was employed. Finally, k-means clustering was utilized to segment the resistivity data into distinct groups based on resistivity values, enabling the identification of color features in different centroids. This facilitated the detection of regions with significant resistivity contrasts in the reservoir. From the clustered images, color masks were generated to visually differentiate the groups and calculate the area and proportion of each group within the pictures. Key features extracted from resistivity profiles were used to train unsupervised learning models capable of generalizing across different geological settings. The proposed methodology improves the accuracy of detecting zones with oil potential and offers scalable applicability to different datasets with minimal retraining, applicable to different subsurface environments. Ultimately, this study seeks to improve the efficiency of petroleum exploration by providing a high-precision automated framework with segmentation and contrast delineation for resistivity analysis, integrating advanced image processing and machine learning techniques. During initial analyses using only k-means, the resulting optimal value of the silhouette coefficient K was 2. After using bilateral filtering together with contrast-limited adaptive histogram equalization (CLAHE) and validation by an expert, the results were more representative, and six clusters were identified. Ultimately, this study seeks to improve the efficiency of petroleum exploration by providing a high-precision automated framework with segmentation and contrast delineation for resistivity analysis, integrating advanced image processing and machine learning techniques. Full article

Objectives: To develop a microfluidic paper-based analytical device (μPAD) that identifies myeloperoxidase (MPO) levels in the saliva of healthy patients and those with periodontal disease. Materials and Methods: A platform similar to a 96-well plate was printed on Watman^® chromatography paper to run the experimental analysis with unstimulated saliva samples were collected from two groups of patients: those with periodontal health (H, n = 15) and established periodontitis (PD, n = 15). Then, three types of chromophore substrates were pipetted into the wells of the prototype: (1) Guaiacol; (2) Guaiacol, 4,4 ′-diaminodifenilsulfon (DAB) and hydrogen peroxide in Tris-HCl buffer; and (3) 3,3′,5,5′-Tetramethylbenzidine (TMB), followed by saliva samples. The reaction images were analyzed by numbering according to the intensity scale. Results: The comparative results of the reactions using μPAD demonstrated that both the H and PD groups were compatible with each other without differences among the chromophore substrates (p > 0.05). However, the protocol with TMB showed a faster reaction and better color difference when comparing 15.62 ng/mL and 7.81 ng/mL of MPO in the plate embedded with Guaiacol; 1000 ng/mL and 62.5 ng/mL on the Guaiacol and DAB plate; and 62.5 ng/mL of TMB. The average detectable concentrations of MPO in saliva using TMB were H = 21.2 ± 10.4 ng/mL and PD = 28.9 ± 12.8 ng/mL (p = 0.08). Conclusions: The developed microfluidic paper-based analytical device has been tested for identifying the myeloperoxidase saliva levels of healthy patients and those with periodontal disease. This rapid test demonstrated its possible applicability mainly when associated with the TMB chromophore, but further studies are required with different biomarkers to explore this promising diagnostic platform. Full article

The objective of this study was to analyze the impact of solar and hybrid dryers on the physicochemical characteristics, fingerprints, and cognitive-sensory perceptions of Mexican consumers of traditional tostadas made with corn of different races. Corn tostadas from different native races were evaluated with solar and hybrid (solar-photovoltaic solar panels) dehydration methods. Proximal chemical quantification, instrumental analysis (color, texture), fingerprint by Fourier transform infrared spectroscopy (FTIR), and sensory-cognitive profile (emotions and memories) and its relationship with the level of pleasure were carried out. The data were evaluated using analysis of variance models, Cochran Q, and an external preference map (PREFMAP). The results showed that the drying method and corn race significantly (p < 0.05) affected only moisture content, lipids, carbohydrates, and water activity. Instrumental color was influenced by the corn race effect, and the dehydration type influenced the fracturability effect. FTIR fingerprinting results revealed that hybrid samples exhibited higher intensities, particularly associated with higher lime concentrations, indicating a greater exposure of glycosidic or protein structures. Race and dehydration type effects impacted the intensity of sensory attributes, emotions, and memories. PREFMAP vector model results revealed that consumers preferred tostadas from the Solar-Chiquito, Hybrid-Pepitilla, Hybrid-Cónico, and Hybrid-Chiquito races for their higher protein content, moisture, high fracturability, crunchiness, porousness, sweetness, doughy flavor, corn flavor, and burnt flavor, while images of these tostadas evoked positive emotions (tame, adventurous, free). In contrast, the Solar-Pepitilla tostada had a lower preference because it was perceived as sour and lime-flavored, and its tostada images evoked more negative emotions and memories (worried, accident, hurt, pain, wild) and fewer positive cognitive aspects (joyful, warm, rainy weather, summer, and interested). However, the tostadas of the Solar-Cónico race were the ones that were most rejected due to their high hardness and yellow to blue tones and for evoking negative emotions (nostalgic and bored). Full article

Background/Objectives: White light imaging (WLI) of colonoscopy has a 26% adenoma miss rate. We aimed to evaluate the effectiveness of an additional 30 s (Add-30s) observation of the right-sided colon using a novel system (EVIS X1; Olympus Co.) with texture and color enhancement imaging (TXI). Methods: We reviewed 515 patients who underwent colonoscopy with Add-30s TXI between February 2021 and December 2023 at three affiliated hospitals. After initial right-sided colon observation with WLI, the colonoscope was reinserted into the cecum, and the right-sided colon was re-observed with Add-30s TXI. Adenoma and sessile serrated lesion (SSL) detection rate (ASDR) and adenoma detection rate (ADR) were examined. Multivariate analysis identified factors influencing lesion detection using the Add-30s TXI. The difference in WLI and TXI between the novel and previous scopes was performed using propensity score matching (PSM). The efficacy of WLI with the novel system was compared to that of the previous system. Results: Among the 515 cases, Add-30s TXI observation increased right-sided ADR and ASDR by 7.4% and 9.5%, respectively. The multivariate analysis showed novel scope as an independent factor for adenoma and SSL detection (odds ratio: 2.41, p < 0.01). Right-sided ADR and ASDR for Add-30s TXI were significantly higher in the novel scope than the previous scope (ADR, 25.2% vs. 15.3%; p = 0.04; ASDR, 32.4% vs. 18.9%; p = 0.02). ASDR for WLI observation was significantly higher in the novel system than the previous system (34.8% vs. 25.9%; p < 0.01). Conclusions: Add-30s TXI significantly improved the detection of missed adenomas and SSLs in the right-sided colon. Full article