An integrated system for modeling, animating and rendering hair

There are basically four problems to solve in order to produce realistic animated synthetic actors with hair: hair modeling and creation, hair motion, collision detection and hair rendering. The authors describe a complete methodology to solve these basic four problems. They present how hair styles may be designed with a Hair Styler module. They survey the animation model and emphasize a method of collision processing. Finally, they explain how hair may be rendered using an extension of a standard ray-tracing program. Applications of synthetic actors with various hair styles and different styles of mustaches and beards are show

Data from: Geographic Object-Based Image Analysis Framework for Mapping Vegetation Physiognomic Types at Fine Scales in Neotropical Savannas

<p>Spatially-explicit information featuring a wide range of detailed vegetation structural types are necessary to support conservation and ecological analyses. A systematic approach that accurately maps such detailed categories at regional scales is currently lacking for neotropical savannas. We developed a systematic Geographic Object-Based Image Analysis (GEOBIA) framework that accounts for spectral and spatial properties to map Cerrado vegetation structural types at 5-m resolution. This framework counts with a two-step mapping approach: (1) image segmentation and a Random Forest land cover classification based on spectral information (Level 1 classification), followed by (2) a GEOBIA knowledge-based classification that follows contextual and topological spatial rules developed in a systematic manner for mapping Cerrado ecological classes (Level 2 classification).</p><p>The framework was tested for two large study sites covering most major Cerrado physiognomic types: a control site in central Brazil (Taquara watershed site, located in Brasilia, Federal District, and embraces the IBGE Ecological Reserve) and another larger site that is located in an agricultural landscape in the western portion of Bahia State (encompasses parts of the municipalities of: Sao Desiderio, Luis Eduardo Magalhaes, Barreiras, and Riachao das Neves), which brings additional mapping challenges related to intra-class spectral similarity. </p><p>Results demonstrate that our GEOBIA approach is effective for mapping 13 land cover classes with 87.6% overall accuracy, of which all 11 major vegetation classes were identified. For additional details, please check the associated publication with these datasets (Ribeiro et al. 2020).</p><p>The datasets developed in this study are available in shapefile format with attributed metadata following ISO 19115 standards: </p><ul><li><strong>wba_lcmap_geobiaL2_2011lucmask:</strong> final land cover map (Level 2 -- physiognomic types) for a section of the western bahia region in the Cerrado. This product uses a land use mask of 2011 derived from ancillary data</li><li><strong>wba_lcmap_geobiaL2_2013lucmask:</strong> final land cover map (Level 2 -- physiognomic types) for a section of the western bahia region in the Cerrado. This product accounts for the land use mask of 2011 as well as an updated land use mask of 2013 derived from ancillary data</li><li><strong>ibge_taquara_luc_L1_L2_2013_original:</strong> land cover maps (Levels 1 and 2) developed for the Taquara/IBGE site</li><li><strong>ibge_taquara_detailed_luc_2013:</strong> final land cover map (Level 2 -- physiognomic types) developed for the Taquara/IBGE site with additional detailed land use categories</li></ul><p> </p><p><strong>Data</strong></p><p>Mapped land cover classes: 1) cerrado woodland, 2) savanna, 3) open savanna, 4) shrubby grassland, 5) grassland (only present in the Taquara site), 6) cerrado scrub (only present in the western bahia site), 7) marsh, 8) shrub swamp, 9) palm swamp, 10) riparian forest, 11) semi-deciduous forest, 12) seasonally dry tropical forest (only present in the western bahia site), 13) invasive forbes (only present in the Taquara site), 14) non-natural/barren, 15) water, 16) shade, 17) clouds</p><p>The land-use mask incorporated into our maps feature the following classes: main roads, farming/crops, silviculture, and urban areas. </p><p> </p><p><strong>Coordinate Reference System</strong></p><p>Datasets were projected to <strong>South America Albers Equal Area</strong> <strong>Conic</strong>, with all areal calculations based on this projection.</p><p> </p><p><strong>Data Usage</strong> </p><p>The datasets are publicly available and should be cited appropriately (main publication: Ribeiro et al. 2020).</p><p> </p><p><strong>Additional information</strong></p><p>For any other data requests or questions, please contact Fernanda Ribeiro ([email protected]).</p><p>Regional maps of vegetation structure are necessary for delineating species habitats and for supporting conservation and ecological analyses. A systematic approach that can discriminate a wide range of meaningful and detailed vegetation classes is still lacking for neotropical savannas. Detailed vegetation mapping of savannas is challenged by seasonal vegetation dynamics and substantial heterogeneity in vegetation structure and composition, but fine spatial resolution imagery (<10 m) can improve map accuracy in these heterogeneous landscapes. Traditional pixel-based classification methods have proven problematic for fine spatial resolution data due to increased within-class spectral variability. Geographic Object-Based Image Analysis (GEOBIA) is a robust alternative method to overcome these issues. We developed a systematic GEOBIA framework accounting for both spectral and spatial features to map Cerrado structural types at 5-m resolution. This two-step framework begins with image segmentation and a Random Forest land cover classification based on spectral information, followed by spatial contextual and topological rules developed in a systematic manner in a GEOBIA knowledge-based approach. Spatial rules were defined <i>a priori</i> based on descriptions of environmental characteristics of 11 different physiognomic types and their relationships to edaphic conditions represented by stream networks (hydrography), topography, and substrate. The Random Forest land cover classification resulted in 10 land cover classes with 84.4% overall map accuracy and was able to map 7 of the 11 vegetation classes. The second step resulted in mapping 13 classes with 87.6% overall accuracy, of which all 11 vegetation classes were identified. Our results demonstrate that 5-m spatial resolution imagery is adequate for mapping land cover types of savanna structural elements. The GEOBIA framework, however, is essential for refining land cover categories to ecological classes (physiognomic types), leading to a higher number of vegetation classes while improving overall accuracy.</p&gt

Geographic Object-Based Image Analysis Framework for Mapping Vegetation Physiognomic Types at Fine Scales in Neotropical Savannas

Regional maps of vegetation structure are necessary for delineating species habitats and for supporting conservation and ecological analyses. A systematic approach that can discriminate a wide range of meaningful and detailed vegetation classes is still lacking for neotropical savannas. Detailed vegetation mapping of savannas is challenged by seasonal vegetation dynamics and substantial heterogeneity in vegetation structure and composition, but fine spatial resolution imagery (<10 m) can improve map accuracy in these heterogeneous landscapes. Traditional pixel-based classification methods have proven problematic for fine spatial resolution data due to increased within-class spectral variability. Geographic Object-Based Image Analysis (GEOBIA) is a robust alternative method to overcome these issues. We developed a systematic GEOBIA framework accounting for both spectral and spatial features to map Cerrado structural types at 5-m resolution. This two-step framework begins with image segmentation and a Random Forest land cover classification based on spectral information, followed by spatial contextual and topological rules developed in a systematic manner in a GEOBIA knowledge-based approach. Spatial rules were defined a priori based on descriptions of environmental characteristics of 11 different physiognomic types and their relationships to edaphic conditions represented by stream networks (hydrography), topography, and substrate. The Random Forest land cover classification resulted in 10 land cover classes with 84.4% overall map accuracy and was able to map 7 of the 11 vegetation classes. The second step resulted in mapping 13 classes with 87.6% overall accuracy, of which all 11 vegetation classes were identified. Our results demonstrate that 5-m spatial resolution imagery is adequate for mapping land cover types of savanna structural elements. The GEOBIA framework, however, is essential for refining land cover categories to ecological classes (physiognomic types), leading to a higher number of vegetation classes while improving overall accuracy