On color image quality assessment using natural image statistics

Color distortion can introduce a significant damage in visual quality perception, however, most of existing reduced-reference quality measures are designed for grayscale images. In this paper, we consider a basic extension of well-known image-statistics based quality assessment measures to color images. In order to evaluate the impact of color information on the measures efficiency, two color spaces are investigated: RGB and CIELAB. Results of an extensive evaluation using TID 2013 benchmark demonstrates that significant improvement can be achieved for a great number of distortion type when the CIELAB color representation is used

Towards a filmic look and feel in real time computer graphics

Film footage has a distinct look and feel that audience can instantly recognize, making its replication desirable for computer generated graphics. This thesis presents methods capable of replicating significant portions of the film look and feel while being able to fit within the constraints imposed by real-time computer generated graphics on consumer hardware

Simulação de acomodação e aberrações de baixa ordem do olho humano usando árvores de coleta de luz

In this work, we present two practical solutions for simulating accommodation and loworder aberrations of optical systems, such as the human eye. Taking into account pupil size (aperture) and accommodation (focal distance), our approaches model the corresponding point spread function and produce realistic depth-dependent simulations of low-order visual aberrations (e.g., myopia, hyperopia, and astigmatism). In the first solution, we use wave optics to extend the notion of Depth Point Spread Function, which originally relies on ray tracing, to perform the generation of point spread functions using Fourier optics. In the other technique, we use geometric optics to build a light-gathering tree data structure, presenting a solution to the problem of artifacts caused by absence of occluded pixels in the input discretized depth images. As such, the resulting images show seamless transitions among elements at different scene depths. We demonstrate the effectiveness of our approaches through a series of quantitative and qualitative experiments on images with depth obtained from real environments. Our results achieved SSIM values above 0.94 and PSNR above 32.0 in all objective evaluations, indicating strong agreement with the ground-truth.Neste trabalho, apresentamos duas técnicas de simulação de acomodação e aberrações de baixa ordem de sistemas ópticos, tais como o olho humano. Nossos algoritmos lançam mão de determinadas informações, tais como o tamanho da pupila e a acomodação (distância focal), com o objetivo de modelar a função de espalhamento pontual (point spread function) do sistema, resultando na produção de simulações realistas de aberrações de baixa ordem (p.e., miopia, hipermetropia e astigmatismo). Nossas simulações levam também em consideração as distâncias dos objetos que compõem a cena a fim de aplicar o borramento apropriado. A primeira técnica estende o conceito de Função de Espalhamento Pontual com Profundidade (Depth Point Spread Function), originalmente construída mediante o traçado de raios (ray tracing), que passa então a ser gerada por meio de métodos da óptica de Fourier. A segunda técnica, por sua vez, utiliza-se da óptica geométrica para construir uma estrutura de dados em forma de árvore. Esta árvore é então utilizada para simular a propagação da luz no ambiente, gerando os efeitos de borramento esperados, e de quebra soluciona o problema de artefatos visuais causados pela ausência de informação na imagem original (provocada pela oclusão parcial entre elementos da cena). Nós demonstramos a efetividade de nossos algoritmos por meio de uma série de experimentos quantitativos e qualitativos em imagens com profundidade obtidas de ambientes reais. Nossos resultados alcançaram valores de SSIM superiores a 0,94 e valores de PSNR superiores a 32,0 em todas as avaliações objetivas, o que indica uma expressiva concordância com as imagens de referência

Applying RGB- and thermal-based vegetation indices from UAVs for high-throughput field phenotyping of drought tolerance in forage grasses

The persistence and productivity of forage grasses, important sources for feed production, are threatened by climate change-induced drought. Breeding programs are in search of new drought tolerant forage grass varieties, but those programs still rely on time-consuming and less consistent visual scoring by breeders. In this study, we evaluate whether Unmanned Aerial Vehicle (UAV) based remote sensing can complement or replace this visual breeder score. A field experiment was set up to test the drought tolerance of genotypes from three common forage types of two different species: Festuca arundinacea, diploid Lolium perenne and tetraploid Lolium perenne. Drought stress was imposed by using mobile rainout shelters. UAV flights with RGB and thermal sensors were conducted at five time points during the experiment. Visual-based indices from different colour spaces were selected that were closely correlated to the breeder score. Furthermore, several indices, in particular H and NDLab, from the HSV (Hue Saturation Value) and CIELab (Commission Internationale de l’éclairage) colour space, respectively, displayed a broad-sense heritability that was as high or higher than the visual breeder score, making these indices highly suited for high-throughput field phenotyping applications that can complement or even replace the breeder score. The thermal-based Crop Water Stress Index CWSI provided complementary information to visual-based indices, enabling the analysis of differences in ecophysiological mechanisms for coping with reduced water availability between species and ploidy levels. All species/types displayed variation in drought stress tolerance, which confirms that there is sufficient variation for selection within these groups of grasses. Our results confirmed the better drought tolerance potential of Festuca arundinacea, but also showed which Lolium perenne genotypes are more tolerant

Phenology analysis in a cork oak woodland through digital photography and spectral vegetation indexes

Mestrado em Engenharia do Ambiente - Instituto Superior de AgronomiaDigital repeat photography is a method to monitor the phenology of vegetation that has gained momentum this past decade. As a result, the need for further case-studies is required. This work aims to prove that it is possible to use digital cameras instead of spectral information to track phenology in a Mediterranean cork oak woodland. The photos will originate the green chromatic coordinates (GCC) index while the normalized difference vegetation index (NDVI) derives from the spectral data collected with a field spectroradiometer. The results were found by employing a regular commercial camera to take monthly pictures along with the spectral measurements. They showed good agreement among methods especially for the herbaceous layer whose GCC had a very good fit with NDVI. The coefficient of determination for the herbaceous layer, the shrub cistus and shrub ulex was 0.89, 0.62 and 0.30, respectively. However, these regressions may be improved upon by grouping the shrub species. The shrubs had a lower correlation between the two indices and all three groups showed a response to water availability. For these reasons, a linear regression between GCC and the normalized water difference index (NDWI) was pursued. This second regression showed better results for shrubs, with coefficients of determination of 0.78 e 0.55, respectively, and a similar value for the herbaceous layer (0.84). The herbaceous layer was found to react quickly to water. Because it only has access to superficial water, its phenology is dependent on precipitation. This group had a good outcome with more long-term observations than shrubs (eight years of data vs. three years). So, it would be the most suitable plant functional type to be tracked using the digital repeat photography method coupled with GCC. Nonetheless, using photos and GCC proves to have the potential to monitor a wide spectrum of vegetation typesN/

Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing

Phenology is a valuable diagnostic of ecosystem health, and has applications to environmental monitoring and management. Here, we conduct an intercomparison analysis using phenological transition dates derived from near-surface PhenoCam imagery and MODIS satellite remote sensing. We used approximately 600 site-years of data, from 128 camera sites covering a wide range of vegetation types and climate zones. During both “greenness rising” and “greenness falling” transition phases, we found generally good agreement between PhenoCam and MODIS transition dates for agricultural, deciduous forest, and grassland sites, provided that the vegetation in the camera field of view was representative of the broader landscape. The correlation between PhenoCam and MODIS transition dates was poor for evergreen forest sites. We discuss potential reasons (including sub-pixel spatial heterogeneity, flexibility of the transition date extraction method, vegetation index sensitivity in evergreen systems, and PhenoCam geolocation uncertainty) for varying agreement between time series of vegetation indices derived from PhenoCam and MODIS imagery. This analysis increases our confidence in the ability of satellite remote sensing to accurately characterize seasonal dynamics in a range of ecosystems, and provides a basis for interpreting those dynamics in the context of tangible phenological changes occurring on the ground

Monitoring vegetation phenology using an infrared-enabled security camera

Sensor-based monitoring of vegetation phenology is being widely used to quantify phenological responses to climate variability and change. Digital repeat photography, in particular, can characterize the seasonality of canopy greenness. However, these data cannot be directly compared to satellite vegetation indices (e.g. NDVI, the normalized difference vegetation index) that require information about vegetation properties at near-infrared (NIR) wavelengths. Here, we develop a new method, using an inexpensive, NIR-enabled camera originally designed for security monitoring, to calculate a “camera NDVI” from sequential visible and visible + NIR photographs. We use a lab experiment for proof-of-concept, and then test the method using a year of data from an ongoing field campaign in a mixed temperate forest. Our analysis shows that the seasonal cycle of camera NDVI is almost identical to that of NDVI measured using narrow-band radiometric instruments, or as observed from space by the MODIS platform. This camera NDVI thus provides different information about the state of the canopy than can be obtained using only visible-wavelength imagery. In addition to phenological monitoring, our method should be useful for a variety of applications, including continuous monitoring of plant stress and quantifying vegetation responses to manipulative treatments in large field experiments.Organismic and Evolutionary Biolog