The development of local solar irradiance for outdoor computer graphics rendering

Atmospheric effects are approximated by solving the light transfer equation, LTE, of a given viewing path. The resulting accumulated spectral energy (its visible band) arriving at the observer’s eyes, defines the colour of the object currently on the line of sight. Due to the convenience of using a single rendering equation to solve the LTE for daylight sky and distant objects (aerial perspective), recent methods had opt for a similar kind of approach. Alas, the burden that the real-time calculation brings to the foil had forced these methods to make simplifications that were not in line with the actual world observation. Consequently, the results of these methods are laden with visual-errors. The two most common simplifications made were: i) assuming the atmosphere as a full-scattering medium only and ii) assuming a single density atmosphere profile. This research explored the possibility of replacing the real-time calculation involved in solving the LTE with an analytical-based approach. Hence, the two simplifications made by the previous real-time methods can be avoided. The model was implemented on top of a flight simulator prototype system since the requirements of such system match the objectives of this study. Results were verified against the actual images of the daylight skies. Comparison was also made with the previous methods’ results to showcase the proposed model strengths and advantages over its peers

Image preprocessing for artistic robotic painting

Artistic robotic painting implies creating a picture on canvas according to a brushstroke map preliminarily computed from a source image. To make the painting look closer to the human artwork, the source image should be preprocessed to render the effects usually created by artists. In this paper, we consider three preprocessing effects: aerial perspective, gamut compression and brushstroke coherence. We propose an algorithm for aerial perspective amplification based on principles of light scattering using a depth map, an algorithm for gamut compression using nonlinear hue transformation and an algorithm for image gradient filtering for obtaining a well-coherent brushstroke map with a reduced number of brushstrokes, required for practical robotic painting. The described algorithms allow interactive image correction and make the final rendering look closer to a manually painted artwork. To illustrate our proposals, we render several test images on a computer and paint a monochromatic image on canvas with a painting robot

Roadmap on 3D integral imaging: Sensing, processing, and display

This Roadmap article on three-dimensional integral imaging provides an overview of some of the research activities in the field of integral imaging. The article discusses various aspects of the field including sensing of 3D scenes, processing of captured information, and 3D display and visualization of information. The paper consists of a series of 15 sections from the experts presenting various aspects of the field on sensing, processing, displays, augmented reality, microscopy, object recognition, and other applications. Each section represents the vision of its author to describe the progress, potential, vision, and challenging issues in this field

Investigation of effects of land use changes in Tahtalı River Basin on water quality

Thesis (Master)--Izmir Institute of Technology, Civil Engineering, Izmir, 2009Includes bibliographical references (leaves: 85-91)Text in English; Abstract: Turkish and Englishx, 97 leavesThe rapid increase of population, industrial growth and disorganized urbanization have put considerable stress on the available water sources, which are already scarce, not only by the increased usage but also by deterioration of the quality of available resources. Both statistical and GIS analyses were adopted in this study to examine the changes in water quality parameters associated with the changes in landuse within a major watershed in the city of Izmir, Turkey. In this study, the satellite images containing the periods prior and after filling of the main pool of the Tahtali reservoir, were analyzed and the effects of the land use changes on the water quality were investigated. For this purpose, the aerial photos of the basin taken in 1995 (October) composed of 130 sections having a scale of 1/5000 were obtained and these images were compared with images of the Ikonos satellite taken in 2005 (November) with a resolution of 1 meter. New residential buildings, greenhouses and industrial buildings were presented in separate layers to document changes in basin activities since 1995.Later on, changes in all 130 sections were merged and the thematic maps of the basin were obtained. This analysis utilized several GIS techniques including manual digitizing, remote sensing and use of existing digital base maps for the preparation of input data. The data analysis included transformation between map projections and data formats, editing of attributes and use of query functions, use of spatial overlaying and also both retrieval and classification.In order to investigate the effects of changes on the water quality, the water analysis values obtained from samples taken at 6 different reaches within the basin and at the main lake for the years of 1995-2005 were obtained. Seasonal Kendall and Mann Kendall tests were selected and applied to the water quality data to investigate which parameters increased/decreased and how these changes were related to the effects of urbanization and industrial development. This study also investigated and quantified soil erosion in the basin by the universal soil loss equation (USLE) for two different land use compositions and soil maps from two years: 1995 and 2005

Visibility in underwater robotics: Benchmarking and single image dehazing

Dealing with underwater visibility is one of the most important challenges in autonomous underwater robotics. The light transmission in the water medium degrades images making the interpretation of the scene difficult and consequently compromising the whole intervention. This thesis contributes by analysing the impact of the underwater image degradation in commonly used vision algorithms through benchmarking. An online framework for underwater research that makes possible to analyse results under different conditions is presented. Finally, motivated by the results of experimentation with the developed framework, a deep learning solution is proposed capable of dehazing a degraded image in real time restoring the original colors of the image.Una de las dificultades más grandes de la robótica autónoma submarina es lidiar con la falta de visibilidad en imágenes submarinas. La transmisión de la luz en el agua degrada las imágenes dificultando el reconocimiento de objetos y en consecuencia la intervención. Ésta tesis se centra en el análisis del impacto de la degradación de las imágenes submarinas en algoritmos de visión a través de benchmarking, desarrollando un entorno de trabajo en la nube que permite analizar los resultados bajo diferentes condiciones. Teniendo en cuenta los resultados obtenidos con este entorno, se proponen métodos basados en técnicas de aprendizaje profundo para mitigar el impacto de la degradación de las imágenes en tiempo real introduciendo un paso previo que permita recuperar los colores originales

CLOSE-RANGE AND SATELLITE REMOTE SENSING OF ALGAL BIOMASS IN THE IOWA GREAT LAKES

The utility of both close-range and satellite remote sensing for assessing inland water quality was examined in the Iowa Great Lakes. The water quality of this system is of considerable interest because of its status as an environmental, recreational, and therefore, economic resource. The broad range of optical conditions present in the lakes and the wealth of literature on the system make it an ideal environment for water quality remote sensing research. The goal of this research was to survey the water quality of the Iowa Great Lakes via remote sensing, evaluate different predictive algorithms, and map the distribution of algal biomass. In situ sampling was carried out on Spirit, West Okoboji, and East Okoboji lakes, concurrent with a SPOT-2 satellite overpass, August 13, 1997. A total of 26 sample sites were visited. Measurements included chlorophyll a, turbidity, vertical attenuation, hyperspectral radiance/reflectance, and GPS. Aerial photographs were taken later in order to illustrate the spatial characteristics of various aquatic features. Empirical relationships between chlorophyll and both close-range (hyperspectral) and satellite (broad-band) data, were evaluated using correlation and regression techniques. Results suggest that low chlorophyll concentrations are difficult to estimate, while moderate to high biomass levels can be accurately modeled using either closerange or satellite data. The near-infrared portion of the optical spectrum proved the single most useful spectral region for estimating chlorophyll concentration. Due to their ability to correct for nonalgal scattering, the NIR:Red ratio (r2 = 0.997) and the Baseline Sum (r2 = 0.998) algorithms, proved the most effective for estimating chlorophyll In hypereutrophic East Okoboji Lake. Chlorophyll a maps were developed by applying an algorithm based on nearinfrared band radiance magnitude (r2 = 0.833) to the SPOT imagery. The distribution of algal biomass is predominantly homogeneous in West Okoboji and Spirit lakes, but extremely patchy in Lake East Okoboji. This spatial heterogeneity of water quality constituents may be a significant source of error for monitoring programs based solely on point sampling. Advisor: Donald C. Rundquis

Assessing the relative accuracy of coral heights reconstructed from drones and structure from motion photogrammetry on coral reefs

Low-altitude high-resolution aerial photographs allow for the reconstruction of structural properties of shallow coral reefs and the quantification of their topographic complexity. This study shows the scope and limitations of two-media (air/water) Structure from Motion—Multi-View Stereo reconstruction method using drone aerial photographs to reconstruct coral height. We apply this method in nine different sites covering a total area of about 7000 m2, and we examine the suitability of the method to obtain topographic complexity estimates (i.e., seafloor rugosity). A simple refraction correction and survey design allowed reaching a root mean square error of 0.1 m for the generated digital models of the seafloor (without the refraction correction the root mean square error was 0.2 m). We find that the complexity of the seafloor extracted from the drone digital models is slightly underestimated compared to the one measured with a traditional in situ survey method

Cost-Effective Sensor Systems for Measuring Extracted Chlorophyll-a Concentration

Chlorophyll-a concentration is one of the most measured metrics in both water quality and plant health monitoring. It is an indicator of algal biomass and provides insight into stressors such as eutrophication and bloom risk. It is also a widely used metric in terrestrial ecosystems as an indicator of photosynthetic activity and nutrient limitation. Most currently used laboratory-based methods for measuring chlorophyll-a exploit spectroscopic techniques and require expensive instrumentation, like spectrophotometer or fluorometer. In addition, the readings are taken inside a black box to avoid optical noise. The purpose of this thesis is to propose a smart, low-cost, and portable sensor system to measure the concentration of chlorophyll-a in an extracted solution. The goals were achieved using two distinct spectral method. The first approach involves two consumer-grade spectral sensors that read the optical reflectance at 12 discrete wavelengths in visible and near-infrared spectra. The system was tuned for an optimal distance from the sensors to the solution and an enclosure was printed to maintain the distance, as well as to avoid natural light interference. Extracted chlorophyll-a solutions of 52 different concentrations were prepared, and at least 5 readings per sample were taken using the proposed smart sensor system. The ground truth values of the samples were measured in the laboratory using Thermo Nano 2000C. After cleaning the anomalous data, different machine learning models were trained to determine the significant wavelengths that contribute most towards chlorophyll-a measurement. Finally, a decision tree model with 5 important features was chosen based on the lowest Root Mean Square and Mean Absolute Error when it was tested on the validation set. The final model resulted in a mean error of ±0.9 μg/L when applied on the test set. The total cost for the device was around CAD 135. For the next approach, a rapid system has been proposed using electric impedance spectroscopy (EIS) to measure the concentration of chlorophyll-a, extracted into 95%(v/v) ethanol. Two electrodes accompanied with a high precision impedance converter from Analog Device was used for the development of the sensor. The system was tuned for a fixed electrode orientation, effective area, electrode to electrode distance and excitation voltage by studying different relevant experiments. The proposed sensor was calibrated using the impedance of 95%(v/v) ethanol. Extracted chlorophyll solutions of 60 different concentrations were prepared. At least 5 readings per sample were taken using the proposed system from 1.5 kHz to 7.5 kHz. Samples were then analyzed using standard methods by a spectrophotometer (Genesys20) from Thermo Scientific. Study of Pearson coefficient, principal component analysis, variance inflation factor and backward elimination were used to identify the significant features for chlorophyll-a measurement using EIS. Finally, a simple linear regression model with 11 important features in the range 2.3kHz to 4.7kHz was chosen based on the lowest Root Mean Square (RMS) and Mean Absolute (MA) Error. The coefficient of determination, R2 of the fitted model was 0.93. MAE for the final proposed model is ±0.904 μgL-1 when applied on the test set