Search Results (11)

The rising incidence of skin cancer, particularly melanoma, underscores the need for improved diagnostic tools in dermatology. Accurate imaging plays a crucial role in early detection, yet challenges related to color accuracy, image distortion, and resolution persist, leading to diagnostic errors. This study addresses these issues by evaluating color reproduction accuracy across various imaging devices and lighting conditions. Using a ColorChecker test chart, color deviations were measured through Euclidean distances (ΔE*, ΔC*), and nonlinear color differences (ΔE00, ΔC00), while the color rendering index (CRI) and television lighting consistency index (TLCI) were used to evaluate the influence of light sources on image accuracy. Significant color discrepancies were identified among mobile phones, DSLRs, and mirrorless cameras, with inadequate dermatoscope lighting systems contributing to further inaccuracies. We demonstrate practical applications, including manual camera adjustments, grayscale reference cards, post-processing techniques, and optimized lighting conditions, to improve color accuracy. This study provides applicable solutions for enhancing color accuracy in dermatological imaging, emphasizing the need for standardized calibration techniques and imaging protocols to improve diagnostic reliability, support AI-assisted skin cancer detection, and contribute to high-quality image databases for clinical and automated analysis. Full article

We analyzed the main colorimetric characteristics of lights on Mars’ surface from 3139 total spectral irradiances provided by the COMIMART model (J. Space Weather Space Clim. 5, A33, 2015), modifying the parameters of ‘solar zenith angle’ and ‘opacity’, related to the time of day and the amount of dust in the atmosphere of Mars, respectively. Lights on Mars’ surface have chromaticities that are mainly located below the Planckian locus, correlated color temperature in the range of 2333 K–5868 K, and CIE 2017 color fidelity indices above 93. For the 24 samples in the X-Rite ColorChecker^® and an extreme dust opacity change from 0.1 to 8.1 in the atmosphere, the average color inconstancy generated by the change in Mars’ light using the chromatic adaptation transform CIECAT16 was about 5 and 8 CIELAB units for solar zenith angles of 0° and 72°, respectively. We propose a method to compute total spectral irradiances on the surface of Mars from a selected value of correlated color temperature in the range of 2333 K–5868 K. This method is analogous to the one currently adopted by the International Commission on Illumination to compute daylight illuminants on the surface of Earth (CIE 015:2018, clause 4.1.2). The average accuracy of 3139 reconstructed total spectral irradiances using the proposed method was 0.9999558 using GFC (J. Opt. Soc. Am. A 14, 1007–1014, 1997) and 0.0009 ΔE_u^′v^′ units, a value just below noticeable chromaticity differences perceptible by human observers at 50% probability. Total spectral irradiances proposed by Barnes for five correlated temperatures agreed with those obtained from the current proposed method: on the average, GFC = 0.9979521 and 0.0023 ΔE_^′v^′ units. Full article

Integrating deep learning for crop monitoring presents opportunities and challenges, particularly in object detection under varying environmental conditions. This study investigates the efficacy of image preprocessing methods for olive identification using mobile cameras under natural light. The research is grounded in the broader context of enhancing object detection accuracy in variable lighting, which is crucial for practical applications in precision agriculture. The study primarily employs the YOLOv7 object detection model and compares various color correction techniques, including histogram equalization (HE), adaptive histogram equalization (AHE), and color correction using the ColorChecker. Additionally, the research examines the role of data augmentation methods, such as image and bounding box rotation, in conjunction with these preprocessing techniques. The findings reveal that while all preprocessing methods improve detection performance compared to non-processed images, AHE is particularly effective in dealing with natural lighting variability. The study also demonstrates that image rotation augmentation consistently enhances model accuracy across different preprocessing methods. These results contribute significantly to agricultural technology, highlighting the importance of tailored image preprocessing in object detection models. The conclusions drawn from this research offer valuable insights for optimizing deep learning applications in agriculture, particularly in scenarios with inconsistent environmental conditions. Full article

Silk fiber, recognized as a versatile bioresource, holds wide-ranging significance in agriculture and the textile industry. During the breeding of silkworms to yield new varieties, optical sensing techniques have been employed to distinguish the colors of silk cocoons, aiming to assess their improved suitability across diverse industries. Despite visual comparison retaining its primary role in differentiating colors among a range of silk fibers, the presence of uneven surface texture leads to color distortion and inconsistent color perception at varying viewing angles. As a result, these distorted and inconsistent visual assessments contribute to unnecessary fiber wastage within the textile industry. To solve these issues, we have devised an optical system employing an integrating sphere to deliver consistent and uniform illumination from all orientations. Utilizing a ColorChecker, we calibrated the RGB values of silk cocoon images taken within the integrating sphere setup. This process accurately extracts the authentic RGB values of the silk cocoons. Our study not only helps in unraveling the intricate color of silk cocoons but also presents a unique approach applicable to various specimens with uneven surface textures. Full article

Pumpkins are a nutritious and globally enjoyed fruit for their rich and earthy flavor. The biophysical properties of pumpkins play an important role in determining their yield. However, manual in-field techniques for monitoring these properties can be time-consuming and labor-intensive. To address this, this research introduces a novel approach that feeds high-resolution pumpkin images to train a mathematical model to automate the measurement of each pumpkin’s biophysical properties. Color correction was performed on the dataset using a color-checker panel to minimize the impact of varying light conditions on the RGB images. A segmentation model was then trained to effectively recognize two fundamental components of each pumpkin: the fruit and vine. Real-life measurements of various biophysical properties, including fruit length, fruit width, stem length, stem width and fruit peel color, were computed and compared with manual measurements. The experimental results on 10 different pumpkin samples revealed that the framework obtained a small average mean absolute percentage error (MAPE) of 2.5% compared to the manual method, highlighting the potential of this approach as a faster and more efficient alternative to conventional techniques for monitoring the biophysical properties of pumpkins. Full article

Colorimetric characterization is the basis of color information management in color imaging systems. In this paper, we propose a colorimetric characterization method based on kernel partial least squares (KPLS) for color imaging systems. This method takes the kernel function expansion of the three-channel response values (RGB) in the device-dependent space of the imaging system as input feature vectors, and CIE-1931 XYZ as output vectors. We first establish a KPLS color-characterization model for color imaging systems. Then we determine the hyperparameters based on nested cross validation and grid search; a color space transformation model is realized. The proposed model is validated with experiments. The CIELAB, CIELUV and CIEDE2000 color differences are used as evaluation metrics. The results of the nested cross validation test for the ColorChecker SG chart show that the proposed model is superior to the weighted nonlinear regression model and the neural network model. The method proposed in this paper has good prediction accuracy. Full article

Hyperspectral imaging and distance data have previously been used in aerial, forestry, agricultural, and medical imaging applications. Extracting meaningful information from a combination of different imaging modalities is difficult, as the image sensor fusion requires knowing the optical properties of the sensors, selecting the right optics and finding the sensors’ mutual reference frame through calibration. In this research we demonstrate a method for fusing data from Fabry–Perot interferometer hyperspectral camera and a Kinect V2 time-of-flight depth sensing camera. We created an experimental application to demonstrate utilizing the depth augmented hyperspectral data to measure emission angle dependent reflectance from a multi-view inferred point cloud. We determined the intrinsic and extrinsic camera parameters through calibration, used global and local registration algorithms to combine point clouds from different viewpoints, created a dense point cloud and determined the angle dependent reflectances from it. The method could successfully combine the 3D point cloud data and hyperspectral data from different viewpoints of a reference colorchecker board. The point cloud registrations gained $0.29$–$0.36$ fitness for inlier point correspondences and RMSE was approx. 2, which refers a quite reliable registration result. The RMSE of the measured reflectances between the front view and side views of the targets varied between $0.01$ and $0.05$ on average and the spectral angle between $1.5$ and $3.2$ degrees. The results suggest that changing emission angle has very small effect on the surface reflectance intensity and spectrum shapes, which was expected with the used colorchecker. Full article

An improved spectral reflectance estimation method was developed to transform captured RGB images to spectral reflectance. The novelty of our method is an iteratively reweighted regulated model that combines polynomial expansion signals, which was developed for spectral reflectance estimation, and a cross-polarized imaging system, which is used to eliminate glare and specular highlights. Two RGB images are captured under two illumination conditions. The method was tested using ColorChecker charts. The results demonstrate that the proposed method could make a significant improvement of the accuracy in both spectral and colorimetric: it can achieve 23.8% improved accuracy in mean CIEDE2000 color difference, while it achieves 24.6% improved accuracy in RMS error compared with classic regularized least squares (RLS) method. The proposed method is sufficiently accurate in predicting the spectral properties and their performance within an acceptable range, i.e., typical customer tolerance of less than 3 DE units in the graphic arts industry. Full article

Quality smartphone cameras and affordable dermatoscopes have enabled teledermoscopy to become a popular medical and veterinary tool for analyzing skin lesions such as melanoma and erythema. However, smartphones acquire images in an unknown RGB color space, which prevents a standardized colorimetric skin analysis. In this work, we supplemented a typical veterinary teledermoscopy system with a conventional color calibration procedure, and we studied two mid-priced smartphones in evaluating native and erythematous canine skin color. In a laboratory setting with the ColorChecker, the teledermoscopy system reached CIELAB-based color differences ΔE of 1.8–6.6 (CIE76) and 1.1–4.5 (CIE94). Intra- and inter-smartphone variability resulted in the color differences (CIE76) of 0.1, and 2.0–3.9, depending on the selected color range. Preliminary clinical measurements showed that canine skin is less red and yellow (lower a* and b* for ΔE of 10.7) than standard Caucasian human skin. Estimating the severity of skin erythema with an erythema index led to errors between 0.5–3%. After constructing a color calibration model for each smartphone, we expedited clinical measurements without losing colorimetric accuracy by introducing a simple image normalization on a white standard. To conclude, the calibrated teledermoscopy system is fast and accurate enough for various colorimetric applications in veterinary dermatology. Full article

In this work, we address the problem of spectral reflectance recovery from both CIEXYZ and RGB values by means of a machine learning approach within the fuzzy logic framework, which constitutes the first application of fuzzy logic in these tasks. We train a fuzzy logic inference system using the Macbeth ColorChecker DC and we test its performance with a 130 sample target set made out of Artist’s paints. As a result, we obtain a fuzzy logic inference system (FIS) that performs quite accurately. We have studied different parameter settings within the training to achieve a meaningful overfitting-free system. We compare the system performance against previous successful methods and we observe that both spectrally and colorimetrically our approach substantially outperforms these classical methods. In addition, from the FIS trained we extract the fuzzy rules that the system has learned, which provide insightful information about how the RGB/XYZ inputs are related to the outputs. That is to say that, once the system is trained, we extract the codified knowledge used to relate inputs and outputs. Thus, we are able to assign a physical and/or conceptual meaning to its performance that allows not only to understand the procedure applied by the system but also to acquire insight that in turn might lead to further improvements. In particular, we find that both trained systems use four reference spectral curves, with some similarities, that are combined in a non-linear way to predict spectral curves for other inputs. Notice that the possibility of being able to understand the method applied in the trained system is an interesting difference with respect to other ’black box’ machine learning approaches such as the currently fashionable convolutional neural networks in which the downside is the impossibility to understand their ways of procedure. Another contribution of this work is to serve as an example of how, through the construction of a FIS, some knowledge relating inputs and outputs in ground truth datasets can be extracted so that an analogous strategy could be followed for other problems in color and spectral science. Full article

Glare is an unwanted optical phenomenon which affects imaging systems with optics. This paper presents for the first time a set of hyperspectral image (HSI) acquisitions and measurements to verify how glare affects acquired HSI data in standard conditions. We acquired two ColorCheckers (CCs) in three different lighting conditions, with different backgrounds, different exposure times, and different orientations. The reflectance spectra obtained from the imaging system have been compared to pointwise reference measures obtained with contact spectrophotometers. To assess and identify the influence of glare, we present the Glare Effect (GE) index, which compares the contrast of the grayscale patches of the CC in the hyperspectral images with the contrast of the reference spectra of the same patches. We evaluate, in both spatial and spectral domains, the amount of glare affecting every hyperspectral image in each acquisition scenario, clearly evidencing an unwanted light contribution to the reflectance spectra of each point, which increases especially for darker pixels and pixels close to light sources or bright patches. Full article