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51	Discriminative image representations using spatial and color information for category-level classification Khan, Rahat 08 October 2013 (has links) (PDF) Image representation is in the heart of many computer vision algorithms. Different computer vision tasks (e.g. classification, detection) require discriminative image representations to recognize visual categories. In a nutshell, the bag-of-visual-words image representation is the most successful approach for object and scene recognition. In this thesis, we mainly revolve around this model and search for discriminative image representations. In the first part, we present a novel approach to incorporate spatial information in the BoVW method. In this framework, we present a simple and efficient way to infuse spatial information by taking advantage of the orientation and length of the segments formed by pairs of similar descriptors. We introduce the notion of soft-similarity to compute intra and inter visual word spatial relationships. We show experimentally that, our method adds important discriminative information to the BoVW method and complementary to the state-of-the-art method. Next, we focus on color description in general. Differing from traditional approaches of invariant description to account for photometric changes, we propose discriminative color descriptor. We demonstrate that such a color description automatically learns a certain degree of photometric invariance. Experiments show that the proposed descriptor outperforms existing photometric invariants. Furthermore, we show that combined with shape descriptor, the proposed color descriptor obtain excellent results on four challenging data sets.Finally, we focus on the most accurate color representation i.e. multispectral reflectance which is an intrinsic property of a surface. Even with the modern era technological advancement, it is difficult to extract reflectance information without sophisticated instruments. To this end, we propose to use the display of the device as an illuminant while the camera captures images illuminated by the red, green and blue primaries of the display. Three illuminants and three response functions of the camera lead to nine response values which are used for reflectance estimation. Results show that the accuracy of the spectral reconstruction improves significantly over the spectral reconstruction based on a single illuminant. We conclude that, multispectral data acquisition is potentially possible with consumer hand-held devices such as tablets, mobiles, and laptops [SPI:OTHER] Engineering Sciences/Other Computer vision Image classification Image representation Bag of visual words Color descriptor Multispectral imaging Spatial information
52	Early Assessment of Burn Severity in Human Tissue with Multi-Wavelength Spatial Frequency Domain Imaging Poon, Chien Sing January 2016 (has links) No description available. Medical Imaging Physics Optics Biomedical Engineering Biomedical Research Biophysics Cellular Biology Engineering Optical Imaging Medical Imaging burn wound assessment diagnosis fluorescence multispectral imaging optical properties quantitative assessment skin burn spatial frequency domain imaging system tissue scattering wounds
53	Multispectral imaging of Sphagnum canopies: measuring the spectral response of three indicator species to a fluctuating water table at Burns Bog Elves, Andrew 02 May 2022 (has links) Northern Canadian peatlands contain vast deposits of carbon. It is with growing urgency that we seek a better understanding of their assimilative capacity. Assimilative capacity and peat accumulation in raised bogs are linked to primary productivity of resident Sphagnum species. Understanding moisture-mediated photosynthesis of Sphagnum spp. is central to understanding peat production rates. The relationship between depth to water table fluctuation and spectral reflectance of Sphagnum moss was investigated using multispectral imaging at a recovering raised bog on the southwest coast of British Columbia, Canada. Burns Bog is a temperate oceanic ombrotrophic bog. Three ecohydrological indicator species of moss were chosen for monitoring: S. capillifolium, S. papillosum, and S. cuspidatum. Three spectral vegetation indices (SVIs) were used to characterize Sphagnum productivity: the normalized difference vegetation index 660, the chlorophyll index, and the photochemical reflectance index. In terms of spectral sensitivity and the appropriateness of SVIs to species and field setting, we found better performance for the normalized difference vegetation index 660 in the discrimination of moisture mediated species-specific reflectance signals. The role that spatiotemporal scale and spectral mixing can have on reflectance signal fidelity was tested. We were specifically interested in the relationship between changes in the local water table and Sphagnum reflectance response, and whether shifting between close spatial scales can affect the statistical strength of this relationship. We found a loss of statistical significance when shifting from the species-specific cm2 scale to the spectrally mixed dm2 scale. This spatiospectral uncoupling of the moisture mediated reflectance signal has implications for the accuracy and reliability of upscaling from plot based measurements. In terms of species-specific moisture mediated reflectance signals, we were able to effectively discriminate between the three indicator species of Sphagnum along the hummock-to-hollow gradient. We were also able to confirm Sphagnum productivity and growth outside of the vascular growing season, establishing clear patterns of reflectance correlated with changes in the local moisture regime. The strongest relationships for moisture mediated Sphagnum productivity were found in the hummock forming species S. capillifolium. Each indicator Sphagnum spp. of peat has distinct functional traits adapted to its preferred position along the ecohydrological gradient. We also discovered moisture mediated and species-specific reflectance phenologies. These phenospectral characteristics of Sphagnum can inform future monitoring work, including the creation of a regionally specific phenospectral library. It’s recommended that further close scale multispectral monitoring be carried out incorporating more species of moss, as well as invasive and upland species of concern. Pervasive vascular reflectance bias in remote sensing products has implications for the reliability of peatland modelling. Avoiding vascular bias, targeted spectral monitoring of Sphagnum indicator species provides a more reliable measure for the modelling of peatland productivity and carbon assimilation estimates. / Graduate peatland peatland restoration peatland monitoring peatland modelling peat peat moss multispectral multispectral imaging multicamera array multitemporal multiple linear regression linear mixed effects Sphagnum sphagna Sphagnum capillifolium Sphagnum papillosum Sphagnum cuspidatum spatiospectral uncoupling near-sensing remote sensing phenospectral phenology phenologies phenospectral bias phenospectral libraries carbon assimilation carbon dioxide carbon dynamics carbon emissions carbon geographies carbon mineralization carbon sequestration carbon source carbon sink carbon storage irrecoverable carbon climate forcing methane Ecological Restoration restoration ecology ecohydrology ecohydrological restoration hydrology ecohydrological gradients ecological integrity reflectance nature based solutions ombrotrophic moss raised peatland mires mires photochemical reflectance index normalized difference vegetation index chlorophyll index climate change climate forcing climate envelope climatope nonvascular vascular bias plant functional type plant functional types structure equation models spectral vegetation indices hummock hollow acrotelm catotelm diplotelmic biocrusts organic soil periodicity moisture deficit senescence rejuvenescence productivity photosynthesis light use efficiency photosynthetic function photosynthetically active radiation water table hydrology rewetting depth to water table landscape change landscape degradation peat subsidence Burns Bog

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