Red , Green And Blue Research Articles

Fluorescence analyzer based on smartphone camera and wireless for detection of Ochratoxin A

This manuscript describes an innovative approach to quantify the concentrations of ochratoxin A (OTA) using the smartphone as fluorescence device. The detection through the smartphone is based on the fluorescence phenomenon. It occurs when the sample is excited with ultraviolet (UV) light, the fluorescence from excited sample passes through a lens to smartphone camera. Finally, it is send by wireless to personal computer. The fluorescence image data from the smartphone camera is analyzed by a personal computer; then, the images are represented in its Red, Green and Blue (RGB) components. It is reported that OTA is naturally fluorescent, hence when the light is emitted through solution with OTA; it provides a blue fluorescence, so the image is captured with the smartphone camera whereas for blank solution, no fluorescence was observed. To process the image, a graphical user interface (GUI) in MATLAB R2011a was made. The best linear range with the developed system was in the range 2–20μg/L and the limit of detection (LOD) was 2μg/L. This is comparable with commercial equipment in a portable system and employing smartphone as a power source also.

Evaluation of an Airborne Remote Sensing Platform Consisting of Two Consumer-Grade Cameras for Crop Identification

Remote sensing systems based on consumer-grade cameras have been increasingly used in scientific research and remote sensing applications because of their low cost and ease of use. However, the performance of consumer-grade cameras for practical applications has not been well documented in related studies. The objective of this research was to apply three commonly-used classification methods (unsupervised, supervised, and object-based) to three-band imagery with RGB (red, green, and blue bands) and four-band imagery with RGB and near-infrared (NIR) bands to evaluate the performance of a dual-camera imaging system for crop identification. Airborne images were acquired from a cropping area in Texas and mosaicked and georeferenced. The mosaicked imagery was classified using the three classification methods to assess the usefulness of NIR imagery for crop identification and to evaluate performance differences between the object-based and pixel-based methods. Image classification and accuracy assessment showed that the additional NIR band imagery improved crop classification accuracy over the RGB imagery and that the object-based method achieved better results with additional non-spectral image features. The results from this study indicate that the airborne imaging system based on two consumer-grade cameras used in this study can be useful for crop identification and other agricultural applications.

Sensor RGB para detectar cambios de color en piel de frutas

In this work an optical sensing setup is designed and implemented to estimate the ripening in fruits. Here by white light reflected signal the color fruit can be calculated to provide information about the fruit condition. The main components in the proposed system are red, green and blue (RGB) module, photodetector and an optical plastic fiber. Moreover the sensing system is compact and versatile, due to the light signal used in the system can me adequate according to some fruit requirements by using a Labview interface, besides the detection and result displaying is proposed by a microcontroller device and Matlab interface. In order to demonstrated the system proposed a guava fruit was used to show the evolution color changes during its ripeness.

Interpretation of Forest Resources at the Individual Tree Level in Japanese Conifer Plantations Using Airborne LiDAR Data

More than 50% of the national lands in Japan have been surveyed by airborne laser scanning (ALS) data with different point densities; and developing an effective approach to take full advantage of these ALS data for forest management has thus become an urgent topic of study. This study attempted to assess the utility of ALS data for individual tree detection and species classification in a mixed forest with a high canopy density. For comparison, two types of tree tops and tree crowns in the study area were delineated by the individual tree crown (ITC) approach using the green band of the orthophoto imagery and the digital canopy height model (DCHM) derived from the ALS data, respectively. Then, the two types of tree crowns were classified into four classes—Pinus densiflora (Pd), Chamaecyparis obtusa (Co), Larix kaempferi (Lk), and broadleaved trees (Bl)—by a crown-based classification approach using different combinations of the three orthophoto bands with intensity and slope maps as follows: RGB (red, green and blue); RGB and intensity (RGBI); RGB and slope (RGBS); and RGB, intensity and slope (RGBIS). Finally, the tree tops were annotated with species attributes from the two best-classified tree crown maps, and the number of different tree species in each compartment was counted for comparison with the field data. The results of our study suggest that the combination of RGBIS yielded greater classification accuracy than the other combinations. In the tree crown classifications delineated by the green band and DCHM data, the improvements in the overall accuracy compared to the RGB ranged from 5.7% for the RGBS to 9.0% for the RGBIS and from 8.3% for the RGBS to 11.8% for the RGBIS. The laser intensity and slope derived from the ALS data may be valuable sources of information for tree species classification, and in terms of distinguishing species for the detection of individual trees, the findings of this study demonstrate the advantages of using DCHM instead of optical data to delineate tree crowns. In conclusion, the synthesis of individual tree delineation using DCHM data and species classification using the RGBIS combination is recommended for interpreting forest resources in the study area. However, the usefulness of this approach must be verified in future studies through its application to other forests.

Building extraction in satellite images using active contours and colour features

ABSTRACTObtaining the segmentation of building footprints from satellite images is a complex process since building areas and their surroundings are presented with various colour intensity values and complex features. Active contour region-based segmentation methods can be used to establish the corresponding boundary of building structures. Typically, these methods divide the image into regions that exhibit a certain similarity and homogeneity. However, using the traditional active contour algorithms for building structures detection, in several cases where spectral heterogeneity exists, over-detection or under-detection are usually noticed. In this work, the Red, Green and Blue (RGB) representation and the properties of the Hue, Saturation and Value (HSV) colour space have been analysed and used to optimize the extraction of buildings from satellite images in an active contour segmentation framework. Initially, the satellite image was processed by applying a clustering technique using colour features to eliminate vegetation areas and shadows that may adversely affect the performance of the algorithm. Subsequently, the HSV representation of the image was used and a new active contour model was developed and applied for building extraction, utilizing descriptors derived from the value and saturation images. A new energy term is encoded for biasing the contours to achieve better segmentation results. An effective procedure has been designed and incorporated in the proposed model for the active contour initialization. This process enhances the performance of the model, leading to lower computational cost and higher building detection accuracy. Additionally, statistical measures are used for designing optimum morphological filters to eliminate any misleading information that may still exist. Qualitative and quantitative measures are used for evaluating the performance of the proposed method.

RGB and white-emitting organic lasers on flexible glass.

Two formats of multiwavelength red, green and blue (RGB) laser on mechanically-flexible glass are demonstrated. In both cases, three all-organic, vertically-emitting distributed feedback (DFB) lasers are assembled onto a common ultra-thin glass membrane substrate and fully encapsulated by a thin polymer overlayer and an additional 50 µm-thick glass membrane in order to improve the performance. The first device format has the three DFB lasers sitting next to each other on the glass substrate. The DFB lasers are simultaneously excited by a single overlapping optical pump, emitting spatially separated red, green and blue laser output with individual thresholds of, respectively, 28 µJ/cm(2), 11 µJ/cm(2) and 32 µJ/cm(2) (for 5 ns pump pulses). The second device format has the three DFB lasers, respectively the red, green and blue laser, vertically stacked onto the flexible glass. This device format emits a white laser output for an optical pump fluence above 42 µJ/cm(2).

PhotoMetrix: An Application for Univariate Calibration and Principal Components Analysis Using Colorimetry on Mobile Devices

This article describes the development of a mobile colorimetric analysis tool. The application, called PhotoMetrix, employs the techniques of simple linear correlation for univariate analysis and principal components analysis (PCA) for multivariate exploratory analysis. The image data are captured by the main camera of the device and converted into red, green and blue (RGB) histograms. As regards the application, the iron determinations were performed in vitamin supplements (univariate module) and differentiation of banknotes was performed by PCA (multivariate module). For the iron determinations, three samples of vitamins at concentrations of 14, 40 and 50 mg of iron per tablet were tested and the results were not statistically significant (p > 0.05) compared to the reference method. The differentiation of banknotes was performed on Brazilian and Argentinean banknotes. The results showed clustering of the same types of banknotes, and through the loadings graph it was possible to observe the variables through the formation of clusters.

Quantum Deep Learning Triuniverse

An original quantum foundations concept of a deep learning computational Universe is introduced. The fundamental information of the Universe (or Triuniverse) is postulated to evolve about itself in a Red, Green and Blue (RGB) tricoloured stable self-mutuality in three information processing loops. The colour is a non-optical information label. The information processing loops form a feedback-reinforced deep learning macrocycle with trefoil knot topology. Fundamental information processing is driven by ψ-Epistemic Drive, the Natural appetite for information selected for advantageous knowledge. From its substrate of Mathematics, the knotted information processing loops determine emergent Physics and thence the evolution of super-emergent Life (biological and artificial intelligence). RGB-tricoloured information is processed in sequence in an Elemental feedback loop (R), then an Operational feedback loop (G), then a Structural feedback loop (B) and back to an Elemental feedback loop (R), and so on around the trefoil in deep learning macrocycles. It is postulated that hierarchical information correspondence from Mathematics through Physics to Life is mapped and conserved within each colour. The substrate of Mathematics has RGB-tricoloured feedback loops which are respectively Algebra (R), Algorithms (G) and Geometry (B). In Mathematics, the trefoil macrocycle is Algebraic Algorithmic Geometry and its correlation system is a Tensor Neural Knot Network enabling Qutrit Entanglement. Emergent Physics has corresponding RGB-tricoloured feedback loops of Quantum Mechanics (R), Quantum Deep Learning (G) and Quantum Geometrodynamics (B). In Physics, the trefoil macrocycle is Quantum Intelligent Geometrodynamics and its correlation system is Quantum Darwinism. Super-emergent Life has corresponding RGB-tricoloured loops of Variation (R), Selection (G) and Heredity (B). In the evolution of Life, the trefoil macrocycle is Variational Selective Heredity and its correlation ecosystem is Darwin’s ecologically “Entangled Bank”.

An Analysis of Leaf Chlorophyll Measurement Method Using Chlorophyll Meter and Image Processing Technique

A regular and periodic monitoring of crop health is essential in any cultivation. An important parameter which act as indices of crop health is the leaf chlorophyll measurement. In the Asian part of the world, Betel vine (Piper betle L., family Piperaceae) ranks second to coffee and tea in terms of daily consumption. Therefore, these important and highly productive crash crop is selected for the purpose of study. The experiment was conducted in an established pan betel vine crop field (Pan boroj). A small review of the popular method of leaf chlorophyll measurement is done and some of the drawbacks of the existing methods are reported. The review point out a need for fast and precise leaf chlorophyll measurement technique. Thus an image processing technique based on trichromatic colors i.e., red green and blue (RGB) model is proposed. For the purpose of analysis of the proposed model, the model outcome was compared with atLEAF+ chlorophyll meter reading. And a regression analysis was performed the result of regression analysis proof that there is a strong correlation between proposed image processing technique and chlorophyll meter reading. Thus, it appears that the proposed image processing technique of leaf chlorophyll measurement will be a good alternative for measuring leaf chlorophyll rapidly and with ease.

INTENSITY ENHANCEMENT ON OUTDOOR IMAGES

Outdoor images that are captured in bad weather conditions have low contrast and infidelity colours. Under the turbid medium conditions such as haze, mist, fog and drizzle, the light which reaches to the sensor is attenuated by atmospheric particles. These atmospheric phenomena degrade the contrast intensity of outdoor images based on haze density. In this research, we present new method to improve both the intensity and fine details of outdoor scene images. The RGB (Red, Green and Blue) input image is converted to the HSI (Hue Saturation Intensity) colour space and the density of the haze is estimated. Then, we use Contrast Limited Adaptive Histogram Equalization (CLAHE) technique to enhance the degraded intensity based on the estimation of the density of the haze. Our method is effective in a wide range of weather conditions and under different levels of visibility.

Ultrasound color histogram assessment allows better view of echotexture damage

INTRODUCTION The colored ultrasound images can better highlight the joint echotexture damage. OBJECTIVE To detect echotexture changes in joints of patients with rheumatic diseases, through a color histogram, and to correlate the area measurements with the gray scale (GS). METHODS Four patients with chondrocalcinosis, 5 patients with osteoarthritis and 1 patient with psoriatic arthritis were evalutated. A total of 104 synovitis (SYN) and calcifications (calcif) measurements in cm2 were performed in the dorsal radiocarpal joint (DRR); triangular fibrocartilage complex (TFC); dorsal metacarpophalangeal joints (MCP) and lateral and medial knee suprapatellar recesses. For each GS measure, one was performed for each standard color image. Spearman was used for correlation analysis. RESULTS Mean±SD of image patterns: RGB (Red, Green and Blue) SYN 16.96±0.25cm2; indigo SYN 6.43±0.07cm2, RGB Calcif 0.03±0.00cm2; indigo Calcif 0.06±0.03cm2; GS SYN 16.13±0.35cm2 and GS Calcif 0.56±0.01cm2. Correlations of color images patterns RGB DRR SYN and indigo DRR SYN: r=1, p<0.001; indigo DRR SYN and RGB suprapattelar SYN: r=1, p<0.001; RGB MCP SYN and indigo MCP SYN: r=1, p<0.001; indigo DRR calcif and RGB TFC calcif: r=1 p<0.001; the RGB and indigo measurement standards when correlated with GS for SYN and calcifs were not statistically significant (p=0.333 to 0.667). CONCLUSION The RGB and indigo patterns seem to better define these echotexture damage.

White light emission and optical gains from a Si nanocrystal thin film

We report a Si nanocrystal thin film consisting of free-standing Si nanocrystals, which can emit white light and show positive optical gains for its red, green and blue (RGB) components under ultraviolet excitation. Si nanocrystals with ϕ = 2.31 ± 0.35 nm were prepared by chemical etching of Si powder, followed by filtering. After being mixed with SiO2 sol-gel and thermally annealed, a broadband photoluminescence (PL) from the thin film was observed. The RGB ratio of the PL can be tuned by changing the annealing temperature or atmosphere, which is 1.00/3.26/4.59 for the pure white light emission. The origins of the PL components could be due to differences in oxygen-passivation degree for Si nanocrystals. The results may find applications in white-light Si lasing and Si lighting.

Uncertainties involved in leaf fall phenology detected by digital camera

We evaluated the uncertainty in the estimation of year-to-year variability in the timing of leaf fall detected by the analysis of red, green and blue (RGB) values extracted from daily phenological images in a deciduous broad-leaved forest in Japan. We examined (1) the spatial distribution of individual tree species within a 1-ha permanent plot and the spatio-temporal variability of leaf litter of various species for 8years; and (2) the relationship between the year-to-year variability of leaf fall detected by leaf litter and that detected by phenological images of various species. Uncertainties were caused by (1) the heterogeneous distribution of each species within the whole forest community; (2) the year-to-year variability of the timing of leaf fall among species; and (3) differences in leaf colouring and leaf fall patterns among species. Our results indicate the importance of integrating RGB analysis of each species and of the whole canopy on the basis of spatial locations of individuals and proportions of tree species within a forest to reduce uncertainty.

Discriminating features learning in hand gesture classification

The advent and popularity of Kinect provides a new choice and opportunity for hand gesture recognition (HGR) research. In this study, the authors propose a discriminating features extraction for HGR, in which features from red, green and blue (RGB) images and depth images are both explored. More specifically, histogram of oriented gradient feature, local binary pattern feature, structure feature and three‐dimensional voxel feature are first extracted from RGB images and depth images, then these features are further reduced with a novel deflation orthogonal discriminant analysis, which enhances the discriminative ability of the features with supervised subspace projection. The extensive experimental results show that the proposed method improves the HGR performance significantly.

Anatomical digital image analysis of the angle and optic nerve – a novel method for glaucoma imaging

PurposeTo devise a simple, fast and low‐cost method for glaucoma imaging using anatomical digital image analysis of the angle and optic nerve in human subjects.MethodsA pilot study included 10 open‐angle glaucoma patients attending the glaucoma services at the Department of Ophthalmology, University of Szeged, Hungary; colour photographs of the fundus, standard optic nerve images with OCT and additional digital slit lamp images of the angle where collected. Digital image conversion and analysis of the angle using Image J (NIH, USA) and adaptive histogram equalization Contrast Limited AHE (CLAHE) to prevent noise amplification. Angle and optic nerve images, were analyzed separately in the Red, Green and Blue (RGB) channels, then 3D volumetric analysis of the degrees of angle, cup volume of the optic nerve took place. Horizontal tomogram reconstitution and nerve fiber detection methods were developed and compared to a standard Topcon 3D OCT.ResultsAngle measurements from digital angle images by gonioscopy all fitted within one standard deviation of Anterior OCT measurements. Comparative analysis of RGB channels produced volumetric cup and Horizontal tomogram results closely resembling those of OCT 3D appearance and B‐scan of the cup, respectively. Similar RGB channel splitting and image subtraction produced a map closely resembling that of the thickness RNFL map on OCT.ConclusionsWhile spectral domain OCT is rapidly progressing in the area of optic disc and chamber angle assessment, rising health care costs and lack of availability of the technology, opens demand for alternative forms of image analysis of glaucoma. Volumetric, geometric and segmentational data obtained through digital image analysis correspond well to those obtained by high definition OCT imaging. A larger number of subjects is needed to further validate the method.

Image Retrieval Using RGB,HSV

This paper proposes a novel feature descriptor called local oppugnant color space extrema patterns (LOCSEP) for image indexing retrieval. The existing directional local extrema pattern (DLEP) extracts the directional edge information based on local extrema in 0o, 45o, 90o, and 135o directions in an image. The proposed method integrates the concepts of color and texture features. First, the color image is converted into RGB (red, green and blue) and HSV (hue, saturation and value) color spaces. Then oppugnant color spaces, RH, GH, BH are used for the extract of oppugnant DLEP features (LOCSEP). The performance of the proposed method is tested by conducting two experiments on benchmark databases viz. Corel-1K and MIT VisTex databases. The performance of the proposed method is compared with the state-of-the-art methods for image retrieval and face recognition applications in terms of average retrieval precision (ARP) and average retrieval rate (ARR). The results after being investigated show a significant improvement in terms of their evaluation measures as compared to state-of-the-art methods on respective databases.

The challenge of objective scar colour assessment in a clinical setting: using digital photography.

Scar assessment in the clinical setting is typically impeded by a lack of quantitative data and most systems rely on subjective rating scales which are user dependant and show considerable variability between raters. The growing use of digital photography in medicine suggests a more objective approach to scar evaluation. Our objective was to determine if cameras could be of practical use for measuring colour in a clinical setting. The measurement of colour and reflectance spectra in photographs faces two difficulties: firstly the effects of variable illumination spectra, and secondly to recover accurate colour and spectral information from the sparse red, green and blue (RGB) camera signals. As a result the colour rendition is often inaccurate, and spectral information is lost. To deal with variable illumination and other factors that systematically affect all reflectance spectra ColourWorker (a method for image-based colour measurement implemented in software) calibrates the spectral responses of the camera's RGB sensors using a colour standard in the image. To make best use of the calibrated signals, it takes advantage of the fact that although a given RGB signal can be caused by an infinite number of spectra, most natural reflectance spectra vary smoothly and have predictable forms. This means given a set of examples of spectra produced by the materials of interest, it is possible to estimate the specific spectrum that produced a given RGB signal once corrected for the illumination. We describe a method for recovering spectral and chromatic information relating to surface reflectance from ordinary digital images and apply this to analyse photographs of surgical scars, taken as part of a clinical trial, in an attempt to better quantify clinical scar assessment. It should be noted the pre-existing trial protocol did not allow for a comprehensive evaluation of the accuracy of the method which would require the spectrophotometric measurement of skin regions corresponding to those in the photographs. Scar colour was estimated reliably, and with simple image analysis we were able to record the change in colour across the skin. Furthermore, we describe a simple automated assessment procedure that enables scar severity to be quantified and defined using a single scalar value easily. Such image-based colour measurement and assessment offers considerable advantages over other current methods, including spectrometers, which measure only a single point, or printed charts.

Spatial application of Random Forest models for fine-scale coastal vegetation classification using object based analysis of aerial orthophoto and DEM data

High spatial resolution mapping of natural resources is much needed for monitoring and management of species, habitats and landscapes. Generally, detailed surveillance has been conducted as fieldwork, numerical analysis of satellite images or manual interpretation of aerial images, but methods of object-based image analysis (OBIA) and machine learning have recently produced promising examples of automated classifications of aerial imagery. The spatial application potential of such models is however still questionable since the transferability has rarely been evaluated.We investigated the potential of mosaic aerial orthophoto red, green and blue (RGB)/near infrared (NIR) imagery and digital elevation model (DEM) data for mapping very fine-scale vegetation structure in semi-natural terrestrial coastal areas in Denmark. The Random Forest (RF) algorithm, with a wide range of object-derived image and DEM variables, was applied for classification of vegetation structure types using two hierarchical levels of complexity. Models were constructed and validated by cross-validation using three scenarios: (1) training and validation data without spatial separation, (2) training and validation data spatially separated within sites, and (3) training and validation data spatially separated between different sites.Without spatial separation of training and validation data, high classification accuracies of coastal structures of 92.1% and 91.8% were achieved on coarse and fine thematic levels, respectively. When models were applied to spatially separated observations within sites classification accuracies dropped to 85.8% accuracy at the coarse thematic level, and 81.9% at the fine thematic level. When the models were applied to observations from other sites than those trained upon the ability to discriminate vegetation structures was low, with 69.0% and 54.2% accuracy at the coarse and fine thematic levels, respectively.Evaluating classification models with different degrees of spatial correlation between training and validation data was shown to give highly different prediction accuracies, thereby highlighting model transferability and application potential. Aerial image and DEM-based RF models had low transferability to new areas due to lack of representation of aerial image, landscape and vegetation variation in training data. They do, however, show promise at local scale for supporting conservation and management with vegetation mappings of high spatial and thematic detail based on low-cost image data.

Low-cost assessment of wheat resistance to yellow rust through conventional RGB images

Establishing low-cost methods for stripe (yellow) rust (Puccinia striiformis f. sp. tritici) phenotyping is paramount to maintain the breeding pipeline in wheat. Twelve winter wheat genotypes were grown to test rust resistance and yield performance. Physiological traits, including leaf chlorophyll content (Chl), net photosynthesis rate (Pn), stomatal conductance (gs), transpiration rate (E) and canopy temperature depression (CTD), together with diverse color components derived from Red, Green and Blue (RGB) images, were measured at different crop stages. Grain yield (GY) and grain yield loss index (GYLI) were assessed through comparison with the previous normal planting year. Genotypes exhibited a wide range of resistance to yellow rust, with GYLI values ranging from about −3% for the more resistant (Zhoumai 22) to 89% for the most susceptible (Lankao 298) genotypes. Moreover yellow rust reduced Chl and to a lesser extent, Pn, while traits related to water status were lower (gs) or not affected (E and CTD). The color parameters Green Fraction, Greener Fraction, Hue, a and u measured during grain filling were much better correlated with GY and GYLI (r2 ranging between 74% and 81%) than the set of photosynthetic and transpirative traits (Chl, Pn, gs, E, CTD) measurements in the same stage. Conventional digital imaging appears to be a potentially affordable approach for high-throughput phenotyping of yellow rust resistance.

Ink-Jet Printed Colorimetric Sensor for the Determination of Fe(II)

Ink-jet printing has been used to fabricate paper strips for the direct measurement of Fe(II) ions by means of a solid-state colorimetric assay. Toward this end, 2, $2^{\prime }$ -bipyridyl dye entrapped in a Nafion matrix was used and subsequently immobilized onto the paper strips. An apparatus for the simple readout of the paper strips, comprising a CMOS camera that allows to acquire Red, Green and Blue (RGB) colors and an image analysis software for data elaboration, has been also developed. The fabricated sensor has been tested for the determination of Fe(II) between 0 and 6 mM, allowing a lower detection limit of 50 $\mu $ M. The paper sensor was rapid—<5 min for the color development and color scanning. The printed paper strips were found stable, without any change in their sensitivity after storage for long periods. The developed sensor was successfully applied to the determination of iron(II) in pharmaceutical formulations.