Red Green Blue Camera Research Articles

Automatic 3D Landmark Extraction System Based on an Encoder–Decoder Using Fusion of Vision and LiDAR

To provide a realistic environment for remote sensing applications, point clouds are used to realize a three-dimensional (3D) digital world for the user. Motion recognition of objects, e.g., humans, is required to provide realistic experiences in the 3D digital world. To recognize a user’s motions, 3D landmarks are provided by analyzing a 3D point cloud collected through a light detection and ranging (LiDAR) system or a red green blue (RGB) image collected visually. However, manual supervision is required to extract 3D landmarks as to whether they originate from the RGB image or the 3D point cloud. Thus, there is a need for a method for extracting 3D landmarks without manual supervision. Herein, an RGB image and a 3D point cloud are used to extract 3D landmarks. The 3D point cloud is utilized as the relative distance between a LiDAR and a user. Because it cannot contain all information the user’s entire body due to disparities, it cannot generate a dense depth image that provides the boundary of user’s body. Therefore, up-sampling is performed to increase the density of the depth image generated based on the 3D point cloud; the density depends on the 3D point cloud. This paper proposes a system for extracting 3D landmarks using 3D point clouds and RGB images without manual supervision. A depth image provides the boundary of a user’s motion and is generated by using 3D point cloud and RGB image collected by a LiDAR and an RGB camera, respectively. To extract 3D landmarks automatically, an encoder–decoder model is trained with the generated depth images, and the RGB images and 3D landmarks are extracted from these images with the trained encoder model. The method of extracting 3D landmarks using RGB depth (RGBD) images was verified experimentally, and 3D landmarks were extracted to evaluate the user’s motions with RGBD images. In this manner, landmarks could be extracted according to the user’s motions, rather than by extracting them using the RGB images. The depth images generated by the proposed method were 1.832 times denser than the up-sampling-based depth images generated with bilateral filtering.

Vegetation indices derived from digital images and stable carbon and nitrogen isotope signatures as indicators of date palm performance under salinity

Date palm is frequently irrigated with brackish water. Developing tools to monitor salinity effects at the single-tree level may assist agronomy and phenotyping. Sixteen elite varieties were grown for 15 years under irrigation with three levels of saline water (5, 10 and 15 dS m−1) at the International Center for Biosaline Agriculture (UAE). Trunk length and diameter, number of branches, and fruit yield per tree were recorded. Different vegetation indices were calculated from single tree-top images taken from the ground with an RGB (Red/Green/Blue) camera. These included indices derived from CIE (Commission Internationale de l’Eclairage) color space models; lightness, together with a* and b* dimensions (CIELab) and u* and v* coordinates (CIELuv); and the HSI color space, referring to the components Hue, Saturation and Intensity. Moreover, Green Area (GA) and the Greener Area (GGA) were also formulated. Also canopy temperature (CT) was measured as an indicator of canopy water status with an infrared thermometer. The carbon isotope composition (δ13C), as a time-integrated indicator of water status, and the nitrogen isotope composition (δ15N) and total nitrogen concentration (N), as nitrogen metabolism indicators, were analyzed in leaflet dry matter. Irrigation conditions and genotypes exhibited significant effects for biomass, fruit yield and all the remote sensing and stable isotope traits evaluated. Hue correlated positively, whereas most of the other RGB vegetation indices along with δ13C and CT correlated negatively with biomass and fruit yield across salinities. Leaf N concentration and δ15N did not correlate with biomass and fruit yield across salinities, but were the only traits correlated with genotypic variability in fruit yield within a given salinity level. Traits that describe canopy color characteristics represent affordable tools for monitoring palm growth and productivity under saline irrigation. However, the results do not support the direct use of RGB indices to phenotype genotypic variability.

An Experimental Evaluation of the Feasibility of Inferring Concentrations of a Visible Tracer Dye from Remotely Sensed Data in Turbid Rivers

The movement of contaminants and biota within river channels is influenced by the flow field via various processes of dispersion. Understanding and modeling of these processes thus can facilitate applications ranging from the prediction of travel times for spills of toxic materials to the simulation of larval drift for endangered species of fish. A common means of examining dispersion in rivers involves conducting tracer experiments with a visible tracer dye. Whereas conventional in situ instruments can only measure variations in dye concentration over time at specific, fixed locations, remote sensing could provide more detailed, spatially-distributed information for characterizing dispersion patterns and validating two-dimensional numerical models. Although previous studies have demonstrated the potential to infer dye concentrations from remotely sensed data in clear-flowing streams, whether this approach can be applied to more turbid rivers remains an open question. To evaluate the feasibility of mapping spatial patterns of dispersion in streams with greater turbidity, we conducted an experiment that involved manipulating dye concentration and turbidity within a pair of tanks while acquiring field spectra and hyperspectral and RGB (red, green, blue) images from a small Unoccupied Aircraft System (sUAS). Applying an optimal band ratio analysis (OBRA) algorithm to these data sets indicated strong relationships between spatially averaged reflectance (i.e., water color) and Rhodamine WT dye concentration across four different turbidity levels from 40–60 NTU. Moreover, we obtained high correlations between spectrally based quantities (i.e., band ratios) and dye concentration for the original, essentially continuous field spectra; field spectra resampled to the bands of a five-band imaging system and an RGB camera; and both hyperspectral and RGB images acquired from an sUAS during the experiment. The results of this study thus confirmed the potential to map dispersion patterns of tracer dye via remote sensing and suggested that this empirical approach can be extended to more turbid rivers than those examined previously.

A Human-Following Mobile Robot Providing Natural and Universal Interfaces for Control With Wireless Electronic Devices

This article presents the development of a mobile robot using a human-following algorithm based on human path prediction and provides a natural gesture interaction for an operator to control wireless electronic devices using a low-cost red-green-blue-depth (RGB-D) sensor. The overall experimental setup consists of a skid-steered mobile robot, Kinect sensor, laptop, red-green-blue (RGB) camera, and two lamps. OpenNI middleware is used to process the depth data from the Kinect sensor, and OpenCV is used to process data from the RGB camera. The human-following control system consists of two feedback control loops for linear and rotational motions, respectively. A lead-lag and proportional-integral derivative controllers are developed for the linear and rotational motion control loops, respectively. There are small delays (0.3 s for linear motion and 0.2 s for rotational motion) of the system's response. However, the delays are acceptable since they do not cause the tracking distance or angle out of our desirable range (±0.05 m and ±10° of the reference input). A new human-position prediction algorithm based on human orientation is proposed for human following. Experimental results show that the tracking algorithm reduces the distance and angular errors by 40% and 50%, respectively. There are four gestures designed for the operator to control the robot. Success rates of gesture recognition are more than 90% within the detectable range of the Kinect sensor.

Assessment of CNN-Based Methods for Individual Tree Detection on Images Captured by RGB Cameras Attached to UAVs.

Detection and classification of tree species from remote sensing data were performed using mainly multispectral and hyperspectral images and Light Detection And Ranging (LiDAR) data. Despite the comparatively lower cost and higher spatial resolution, few studies focused on images captured by Red-Green-Blue (RGB) sensors. Besides, the recent years have witnessed an impressive progress of deep learning methods for object detection. Motivated by this scenario, we proposed and evaluated the usage of Convolutional Neural Network (CNN)-based methods combined with Unmanned Aerial Vehicle (UAV) high spatial resolution RGB imagery for the detection of law protected tree species. Three state-of-the-art object detection methods were evaluated: Faster Region-based Convolutional Neural Network (Faster R-CNN), YOLOv3 and RetinaNet. A dataset was built to assess the selected methods, comprising 392 RBG images captured from August 2018 to February 2019, over a forested urban area in midwest Brazil. The target object is an important tree species threatened by extinction known as Dipteryx alata Vogel (Fabaceae). The experimental analysis delivered average precision around 92% with an associated processing times below 30 miliseconds.

Strawberry Yield Prediction Based on a Deep Neural Network Using High-Resolution Aerial Orthoimages

Strawberry growers in Florida suffer from a lack of efficient and accurate yield forecasts for strawberries, which would allow them to allocate optimal labor and equipment, as well as other resources for harvesting, transportation, and marketing. Accurate estimation of the number of strawberry flowers and their distribution in a strawberry field is, therefore, imperative for predicting the coming strawberry yield. Usually, the number of flowers and their distribution are estimated manually, which is time-consuming, labor-intensive, and subjective. In this paper, we develop an automatic strawberry flower detection system for yield prediction with minimal labor and time costs. The system used a small unmanned aerial vehicle (UAV) (DJI Technology Co., Ltd., Shenzhen, China) equipped with an RGB (red, green, blue) camera to capture near-ground images of two varieties (Sensation and Radiance) at two different heights (2 m and 3 m) and built orthoimages of a 402 m2 strawberry field. The orthoimages were automatically processed using the Pix4D software and split into sequential pieces for deep learning detection. A faster region-based convolutional neural network (R-CNN), a state-of-the-art deep neural network model, was chosen for the detection and counting of the number of flowers, mature strawberries, and immature strawberries. The mean average precision (mAP) was 0.83 for all detected objects at 2 m heights and 0.72 for all detected objects at 3 m heights. We adopted this model to count strawberry flowers in November and December from 2 m aerial images and compared the results with a manual count. The average deep learning counting accuracy was 84.1% with average occlusion of 13.5%. Using this system could provide accurate counts of strawberry flowers, which can be used to forecast future yields and build distribution maps to help farmers observe the growth cycle of strawberry fields.

Broadband extended source imaging Mueller-matrix polarimeter.

An imaging Mueller matrix polarimeter, named the red-green-blue (RGB)950, takes images of medium-sized (tens of centimeters) objects by using a very bright source, large polarization state generator, and high-quality camera. Its broadband extended light source switches between red, green, blue, and near-infrared light to allow taking polarimetric images for comparison with RGB camera images. The large diffuse source makes shadow transitions gradual and spreads out the specular reflected spot into a larger less conspicuous area.

Identification of Water Body Extent Based on Remote Sensing Data Collected with Unmanned Aerial Vehicle

The paper presents an efficient methodology of water body extent estimation based on remotely sensed data collected with UAV (Unmanned Aerial Vehicle). The methodology includes the data collection with selected sensors and processing of remotely sensed data to obtain accurate geospatial products that are finally used to estimate water body extent. Three sensors were investigated: RGB (Red Green Blue) camera, thermal infrared camera, and laser scanner. The platform used to carry each of these sensors was an Aibot X6—a multirotor type of UAV. Test data was collected at 6 sites containing different types of water bodies, including 4 river sections, an old river bed, and a part of a lake shore. The processing of collected data resulted in 2.5-D and 2-D geospatial products that were used subsequently for water body extent estimation. Depending on the type of used sensor, the created geospatial product, and the type of the water body and the land cover, three strategies employing image processing tools were developed to estimate water body range. The obtained results were assessed in terms of classification accuracy (distinguishing the water body from the land) and geometrical planar accuracy of the water body extent. The product identified as the most suitable in water body detection was four bands RGB+TIR (Thermal InfraRed) ortho mosaic. It allowed to achieve the average kappa coefficient of the water body identification above 0.9. The planar accuracy of water body extent varied depending on the type of the sensor, the geospatial product, and the test site conditions, but it was comparable with results obtained in similar studies.

Biomass Prediction of Heterogeneous Temperate Grasslands Using an SfM Approach Based on UAV Imaging

An early and precise yield estimation in intensive managed grassland is mandatory for economic management decisions. RGB (red, green, blue) cameras attached on an unmanned aerial vehicle (UAV) represent a promising non-destructive technology for the assessment of crop traits especially in large and remote areas. Photogrammetric structure from motion (SfM) processing of the UAV-based images into point clouds can be used to generate 3D spatial information about the canopy height (CH). The aim of this study was the development of prediction models for dry matter yield (DMY) in temperate grassland based on CH data generated by UAV RGB imaging over a whole growing season including four cuts. The multi-temporal study compared the remote sensing technique with two conventional methods, i.e., destructive biomass sampling and ruler height measurements in two legume-grass mixtures with red clover (Trifolium pratense L.) and lucerne (Medicago sativa L.) in combination with Italian ryegrass (Lolium multiflorum Lam.). To cover the full range of legume contribution occurring in a practical grassland, pure stands of legumes and grasses contained in each mixture were also investigated. The results showed, that yield prediction by SfM-based UAV RGB imaging provided similar accuracies across all treatments (R2 = 0.59–0.81) as the ruler height measurements (R2 = 0.58–0.78). Furthermore, results of yield prediction by UAV RGB imaging demonstrated an improved robustness when an increased CH variability occurred due to extreme weather conditions. It became apparent that morphological characteristics of clover-based canopies (R2 = 0.75) allow a better remotely sensed prediction of total annual yield than for lucerne-grass mixtures (R2 = 0.64), and that these crop-specific models cannot be easily transferred to other grassland types.

A Novel Null-Space-Based UAV Trajectory Tracking Controller With Collision Avoidance

A new trajectory tracking controller with collision avoidance is proposed in this paper for unmanned aerial vehicle (UAV) navigation. A positive potential function is designed to take into account the movement of obstacles. Thus, the controller with potential function guarantees that the UAV moves through areas of potentials close to zero to ensure safe navigation in dynamic and unknown environments. Such a controller was designed with hierarchical objectives using a behavioral-based approach. A null-space-based controller is adopted, whose main objective is to ensure that the collision avoidance is achieved, whereas other objectives are projected onto the null space. Collision avoidance and trajectory tracking controllers generate reference velocities sent to a dynamic compensator to guarantee the tracking of such velocities, thus characterizing a cascade controller. Stability of the entire closed-loop nonlinear system is demonstrated through Lyapunov's theory. A low-cost indoor framework with just one RGB-D sensor, which is a combination of a RGB (red-green-blue) camera with a depth sensor based on infrared light was used to estimate the positions of the UAV and obstacles. Simulation and experiments are run using a Parrot AR.Drone quadrotor and considering a person as a dynamic obstacle for an AR.Drone quadrotor, and some of their results are reported to validate the proposed controller.

Skeleton estimation of excavator by detecting its parts

Abstract Knowing the near real-time pose of the construction equipment is an important necessity for improving safety and productivity of construction processes. A safer construction site can be achieved by accurately capturing the movements of the equipment and their parts using motion tracking techniques. Recently, the application of Computer Vision (CV) techniques is growing, especially considering that other techniques have difficulties with the deployment and high cost. From the productivity point of view, knowing the pose of the equipment helps to estimate the time that the operator spent on each state of his/her operations. For this purpose, each part or joint of the equipment must be recognized and tracked. Extracting this information requires either using depth sensors or stereo vision, which includes the image information of two or more Red Green Blue (RGB) cameras that have overlapping views. Since the reading range of the common and affordable depth sensors are limited to few meters, this research chooses to use RGB cameras. The most challenging step for this purpose is to process the data of each camera individually for extracting the 2D skeleton of the equipment. After having the 2D skeleton of the equipment from each camera, the 3D pose can be further estimated using the relative rotation and translation information between the cameras coordinate systems. This paper mainly focuses on determining the 2D skeleton of excavators based on the videos received from the cameras available on the site. The method takes advantage of synthetic images of each excavator's part to train the parts' detectors. After detecting the parts, the backgrounds of the detected parts are subtracted. The remaining pixels from the previous step are processed to estimate the skeleton of each part. The final skeleton of the excavator is derived by connecting the individual skeletons of each part to their adjacent parts considering the kinematic information of the excavator.

Detection and Segmentation of Vine Canopy in Ultra-High Spatial Resolution RGB Imagery Obtained from Unmanned Aerial Vehicle (UAV): A Case Study in a Commercial Vineyard

The use of Unmanned Aerial Vehicles (UAVs) in viticulture permits the capture of aerial Red-Green-Blue (RGB) images with an ultra-high spatial resolution. Recent studies have demonstrated that RGB images can be used to monitor spatial variability of vine biophysical parameters. However, for estimating these parameters, accurate and automated segmentation methods are required to extract relevant information from RGB images. Manual segmentation of aerial images is a laborious and time-consuming process. Traditional classification methods have shown satisfactory results in the segmentation of RGB images for diverse applications and surfaces, however, in the case of commercial vineyards, it is necessary to consider some particularities inherent to canopy size in the vertical trellis systems (VSP) such as shadow effect and different soil conditions in inter-rows (mixed information of soil and weeds). Therefore, the objective of this study was to compare the performance of four classification methods (K-means, Artificial Neural Networks (ANN), Random Forest (RForest) and Spectral Indices (SI)) to detect canopy in a vineyard trained on VSP. Six flights were carried out from post-flowering to harvest in a commercial vineyard cv. Carménère using a low-cost UAV equipped with a conventional RGB camera. The results show that the ANN and the simple SI method complemented with the Otsu method for thresholding presented the best performance for the detection of the vine canopy with high overall accuracy values for all study days. Spectral indices presented the best performance in the detection of Plant class (Vine canopy) with an overall accuracy of around 0.99. However, considering the performance pixel by pixel, the Spectral indices are not able to discriminate between Soil and Shadow class. The best performance in the classification of three classes (Plant, Soil, and Shadow) of vineyard RGB images, was obtained when the SI values were used as input data in trained methods (ANN and RForest), reaching overall accuracy values around 0.98 with high sensitivity values for the three classes.

Crop water stress maps for an entire growing season from visible and thermal UAV imagery

Abstract. This study investigates whether a water deficit index (WDI) based on imagery from unmanned aerial vehicles (UAVs) can provide accurate crop water stress maps at different growth stages of barley and in differing weather situations. Data from both the early and late growing season are included to investigate whether the WDI has the unique potential to be applicable both when the land surface is partly composed of bare soil and when crops on the land surface are senescing. The WDI differs from the more commonly applied crop water stress index (CWSI) in that it uses both a spectral vegetation index (VI), to determine the degree of surface greenness, and the composite land surface temperature (LST) (not solely canopy temperature).Lightweight thermal and RGB (red–green–blue) cameras were mounted on a UAV on three occasions during the growing season 2014, and provided composite LST and color images, respectively. From the LST, maps of surface-air temperature differences were computed. From the color images, the normalized green–red difference index (NGRDI), constituting the indicator of surface greenness, was computed. Advantages of the WDI as an irrigation map, as compared with simpler maps of the surface-air temperature difference, are discussed, and the suitability of the NGRDI is assessed. Final WDI maps had a spatial resolution of 0.25 m.It was found that the UAV-based WDI is in agreement with measured stress values from an eddy covariance system. Further, the WDI is especially valuable in the late growing season because at this stage the remote sensing data represent crop water availability to a greater extent than they do in the early growing season, and because the WDI accounts for areas of ripe crops that no longer have the same need for irrigation. WDI maps can potentially serve as water stress maps, showing the farmer where irrigation is needed to ensure healthy growing plants, during entire growing season.

Imaging for High-Throughput Phenotyping in Energy Sorghum

The increasing energy demand in recent years has resulted in a continuous growing interest in renewable energy sources, such as efficient and high-yielding energy crops. Energy sorghum is a crop that has shown great potential in this area, but needs further improvement. Plant phenotyping—measuring physiological characteristics of plants—is a laborious and time-consuming task, but it is essential for crop breeders as they attempt to improve a crop. The development of high-throughput phenotyping (HTP)—the use of autonomous sensing systems to rapidly measure plant characteristics—offers great potential for vastly expanding the number of types of a given crop plant surveyed. HTP can thus enable much more rapid progress in crop improvement through the inclusion of more genetic variability. For energy sorghum, stalk thickness is a critically important phenotype, as the stalk contains most of the biomass. Imaging is an excellent candidate for certain phenotypic measurements, as it can simulate visual observations. The aim of this study was to evaluate image analysis techniques involving K-means clustering and minimum-distance classification for use on red-green-blue (RGB) images of sorghum plants as a means to measure stalk thickness. Additionally, a depth camera integrated with the RGB camera was tested for the accuracy of distance measurements between camera and plant. Eight plants were imaged on six dates through the growing season, and image segmentation, classification and stalk thickness measurement were performed. While accuracy levels with both image analysis techniques needed improvement, both showed promise as tools for HTP in sorghum. The average error for K-means with supervised stalk measurement was 10.7% after removal of known outliers.

Field phenotyping of grapevine growth using dense stereo reconstruction.

BackgroundThe demand for high-throughput and objective phenotyping in plant research has been increasing during the last years due to large experimental sites. Sensor-based, non-invasive and automated processes are needed to overcome the phenotypic bottleneck, which limits data volumes on account of manual evaluations. A major challenge for sensor-based phenotyping in vineyards is the distinction between the grapevine in the foreground and the field in the background – this is especially the case for red-green-blue (RGB) images, where similar color distributions occur both in the foreground plant and in the field and background plants. However, RGB cameras are a suitable tool in the field because they provide high-resolution data at fast acquisition rates with robustness to outdoor illumination.ResultsThis study presents a method to segment the phenotypic classes ‘leaf’, ‘stem’, ‘grape’ and ‘background’ in RGB images that were taken with a standard consumer camera in vineyards. Background subtraction is achieved by taking two images of each plant for depth reconstruction. The color information is furthermore used to distinguish the leaves from stem and grapes in the foreground. The presented approach allows for objective computation of phenotypic traits like 3D leaf surface areas and fruit-to-leaf ratios. The method has been successfully applied to objective assessment of growth habits of new breeding lines. To this end, leaf areas of two breeding lines were monitored and compared with traditional cultivars. A statistical analysis of the method shows a significant (p <0.001) determination coefficient R 2= 0.93 and root-mean-square error of 3.0%.ConclusionsThe presented approach allows for non-invasive, fast and objective assessment of plant growth. The main contributions of this study are 1) the robust segmentation of RGB images taken from a standard consumer camera directly in the field, 2) in particular, the robust background subtraction via reconstruction of dense depth maps, and 3) phenotypic applications to monitoring of plant growth and computation of fruit-to-leaf ratios in 3D. This advance provides a promising tool for high-throughput, automated image acquisition, e.g., for field robots.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0560-x) contains supplementary material, which is available to authorized users.

In vivo multispectral imaging of the absorption and scattering properties of exposed brain using a digital red–green–blue camera

To evaluate multi-spectral images of the absorption and scattering properties in the cerebral cortex of rat brain, we investigated spectral reflectance images estimated by the Wiener estimation method using a digital red–green–blue camera. A Monte Carlo simulation-based multiple regression analysis for the corresponding spectral absorbance images at nine wavelengths (500, 520, 540, 560, 570, 580, 600, 730, and 760 nm) was then used to specify the absorption and scattering parameters. The spectral images of absorption and reduced scattering coefficients were reconstructed from the absorption and scattering parameters. We performed in vivo experiments on exposed rat brain to confirm the feasibility of this method. The estimated images of the absorption coefficients were dominated by hemoglobin spectra. The estimated images of the reduced scattering coefficients had a broad scattering spectrum, exhibiting a larger magnitude at shorter wavelengths, corresponding to the typical spectrum of brain tissue published in the literature.

Information-Theoretic Modeling of Trichromacy Coding of Light Spectrum

Trichromacy is the representation of a light spectrum by three scalar coordinates. Such representation is universally implemented by the human visual system and by RGB (Red Green Blue) cameras. We propose here an informational model for trichromacy. Based on a statistical analysis of the dynamics of individual photons, the model demonstrates a possibility for describing trichromacy as an information channel, for which the input–output mutual information can be computed to serve as a measure of performance. The capabilities and significance of the informational model are illustrated and motivated in various situations. The model especially enables an assessment of the influence of the spectral sensitivities of the three types of photodetectors realizing the trichromatic representation. It provides a criterion to optimize possibly adjustable parameters of the spectral sensitivities such as their center wavelength, spectral width or magnitude. The model shows, for instance, the usefulness of some overlap with smooth graded spectral sensitivities, as observed for instance in the human retina. The approach also, starting from hyperspectral images with high spectral resolution measured in the laboratory, can be used to devise low-cost trichromatic imaging systems optimized for observation of specific spectral signatures. This is illustrated with an example from plant science, and demonstrates a potential of application especially to life sciences. The approach particularizes connections between physics, biophysics and information theory.

Multispectral fluorescence and reflectance imaging at the leaf level and its possible applications

Images taken at different spectral bands are increasingly used for characterizing plants and their health status. In contrast to conventional point measurements, imaging detects the distribution and quantity of signals and thus improves the interpretation of fluorescence and reflectance signatures. In multispectral fluorescence and reflectance set-ups, images are separately acquired for the fluorescence in the blue, green, red, and far red, as well as for the reflectance in the green and in the near infrared regions. In addition, 'reference' colour images are taken with an RGB (red, green, blue) camera. Examples of imaging for the detection of photosynthetic activity, UV screening caused by UV-absorbing substances, fruit quality, leaf tissue structure, and disease symptoms are introduced. Subsequently, the different instrumentations used for multispectral fluorescence and reflectance imaging of leaves and fruits are discussed. Various types of irradiation and excitation light sources, detectors, and components for image acquisition and image processing are outlined. The acquired images (or image sequences) can be analysed either directly for each spectral range (wherein they were captured) or after calculating ratios of the different spectral bands. This analysis can be carried out for different regions of interest selected manually or (semi)-automatically. Fluorescence and reflectance imaging in different spectral bands represents a promising tool for non-destructive plant monitoring and a 'road' to a broad range of identification tasks.

Cataract Image Analysis System.

A system for sequential colour video capture and analysis of lens opacities is described. A sensitive RGB (Red-Green-Blue) camera is coupled to a 486 DX2/66 IBM-compatible computer to obtain high-resolution images of cataracts. Vacuoles, retro-dots, suture opacities, wedge opacities, cortical and posterior subcapsular opacities and nuclear sclerosis can be digitised and subjected to various analysis systems and colour prints obtained. The size and number of vacuoles and opacities can be determined by selecting areas of interest, and line tracings of whole lens, isolated opacities or nuclear sclerosis can be separated by analysis into 16.8 million plus colour gradients. The Cataract Image Analysis System represents a precise and new digitised procedure for cataract documentation and may be of value in determining cataract progression and evaluation of medical therapy.