The Experts below are selected from a list of 9654 Experts worldwide ranked by ideXlab platform

Ryosuke Shibasaki - One of the best experts on this subject based on the ideXlab platform.

  • reconstructing a textured cad model of an urban environment using vehicle borne laser range scanners and line cameras
    Machine Vision Applications, 2003
    Co-Authors: Huijing Zhao, Ryosuke Shibasaki
    Abstract:

    Abstract. In this paper, a novel method is presented for generating a textured CAD model of an outdoor urban environment using a vehicle-borne sensor system. In data measurement, three single-row laser range scanners and six line cameras are mounted on a measurement vehicle, which has been equipped with a GPS/INS/Odometer-based navigation system. Laser range and line images are measured as the vehicle moves forward. They are synchronized with the navigation system so they can be geo-referenced to a world coordinate system. Generation of the CAD model is conducted in two steps. A geometric model is first generated using the geo-referenced laser range data, where urban features, such as buildings, ground surFaces, and trees are extracted in a hierarchical way. Different urban features are represented using different geometric primitives, such as a Planar Face, a triangulated irregular network (TIN), and a triangle. The texture of the urban features is generated by projecting and resampling line images onto the geometric model. An outdoor experiment is conducted, and a textured CAD model of a real urban environment is reconstructed in a full automatic mode.

  • reconstructing textured cad model of urban environment using vehicle borne laser range scanners and line cameras
    International Conference on Computer Vision Systems, 2001
    Co-Authors: Huijing Zhao, Ryosuke Shibasaki
    Abstract:

    In this paper, a novel method is presented to generate textured CAD model of out-door urban environment using a vehicle-borne sensor system. In data measurement, three single-row laser range scanners and six line cameras are mounted on a measurement vehicle, which has been equipped with a GPS/INS/Odometer based navigation system. Laser range and line images are measured as the vehicle moves ahead. They are synchronized with the navigation system, so that can be geo-referenced to a world coordinate system. Generation of CAD model is conducted in two steps. A geometric model is first generated using the geo-referenced laser range data, where urban features like buildings, ground surFace and trees are extracted in a hierarchical way. Different urban features are represented using different geometric primitives like Planar Face, TIN and triangle. Texture of the urban features is generated by projecting and re-sampling line images on the geometric model. An out-door experiment is conducted, and a textured CAD model of a real urban environment is reconstructed in a full automatic mode.

Huijing Zhao - One of the best experts on this subject based on the ideXlab platform.

  • reconstructing a textured cad model of an urban environment using vehicle borne laser range scanners and line cameras
    Machine Vision Applications, 2003
    Co-Authors: Huijing Zhao, Ryosuke Shibasaki
    Abstract:

    Abstract. In this paper, a novel method is presented for generating a textured CAD model of an outdoor urban environment using a vehicle-borne sensor system. In data measurement, three single-row laser range scanners and six line cameras are mounted on a measurement vehicle, which has been equipped with a GPS/INS/Odometer-based navigation system. Laser range and line images are measured as the vehicle moves forward. They are synchronized with the navigation system so they can be geo-referenced to a world coordinate system. Generation of the CAD model is conducted in two steps. A geometric model is first generated using the geo-referenced laser range data, where urban features, such as buildings, ground surFaces, and trees are extracted in a hierarchical way. Different urban features are represented using different geometric primitives, such as a Planar Face, a triangulated irregular network (TIN), and a triangle. The texture of the urban features is generated by projecting and resampling line images onto the geometric model. An outdoor experiment is conducted, and a textured CAD model of a real urban environment is reconstructed in a full automatic mode.

  • reconstructing textured cad model of urban environment using vehicle borne laser range scanners and line cameras
    International Conference on Computer Vision Systems, 2001
    Co-Authors: Huijing Zhao, Ryosuke Shibasaki
    Abstract:

    In this paper, a novel method is presented to generate textured CAD model of out-door urban environment using a vehicle-borne sensor system. In data measurement, three single-row laser range scanners and six line cameras are mounted on a measurement vehicle, which has been equipped with a GPS/INS/Odometer based navigation system. Laser range and line images are measured as the vehicle moves ahead. They are synchronized with the navigation system, so that can be geo-referenced to a world coordinate system. Generation of CAD model is conducted in two steps. A geometric model is first generated using the geo-referenced laser range data, where urban features like buildings, ground surFace and trees are extracted in a hierarchical way. Different urban features are represented using different geometric primitives like Planar Face, TIN and triangle. Texture of the urban features is generated by projecting and re-sampling line images on the geometric model. An out-door experiment is conducted, and a textured CAD model of a real urban environment is reconstructed in a full automatic mode.

Shi-you Ding - One of the best experts on this subject based on the ideXlab platform.

  • Labeling the Planar Face of crystalline cellulose using quantum dots directed by type-I carbohydrate-binding modules
    Cellulose, 2008
    Co-Authors: Melvin P. Tucker, Phil Arenkiel, Garry Rumbles, Junji Sugiyama, Michael E. Himmel, Shi-you Ding
    Abstract:

    We report a new method for the direct labeling and visualization of crystalline cellulose using quantum dots (QDs) directed by carbohydrate-binding modules (CBMs). Two type-I (surFace binding) CBMs belonging to families 2 and 3a were cloned and expressed with dual histidine tags at the N- and C- termini. Semiconductor (CdSe)ZnS QDs were used to label these CBMs following their binding to Valonia cellulose crystals. Using this approach, we demonstrated that QDs are linearly arrayed on cellulose, which implies that these CBMs specifically bind to a Planar Face of cellulose. Direct imaging has further shown that different sizes (colors) of QDs can be used to label CBMs bound to cellulose. Furthermore, the binding density of QDs arrayed on cellulose was modified predictably by selecting from various combinations of CBMs and QDs of known dimensions. This approach should be useful for labeling and imaging cellulose-containing materials precisely at the molecular scale, thereby supporting studies of the molecular mechanisms of lignocellulose conversion for biofuels production.

Steven P. Smith - One of the best experts on this subject based on the ideXlab platform.

  • The Solution Structure of the C-terminal Modular Pair from Clostridium perfringens μ-Toxin Reveals a Noncellulosomal Dockerin Module
    Journal of molecular biology, 2008
    Co-Authors: Seth Chitayat, Jarrett J. Adams, Heather S.t. Furness, Edward A. Bayer, Steven P. Smith
    Abstract:

    Abstract The genome of the opportunistic pathogen Clostridium perfringens encodes a large number of secreted glycoside hydrolases. Their predicted activities indicate that they are involved in the breakdown of complex carbohydrates and other glycans found in the mucosal layer of the human gastrointestinal tract, within the extracellular matrix, and on the surFace of host cells. One such group of these enzymes is the family 84 glycoside hydrolases, which has predicted hyaluronidase activity and comprises five members [ C. perfringens glycoside hydrolase family 84 ( Cp GH84) A–E]. The first identified member, Cp GH84A, corresponds to the μ-toxin whose modular architecture includes an N-terminal catalytic domain, four family 32 carbohydrate-binding modules, three FIVAR modules of unknown function, and a C-terminal putative calcium-binding module. Here, we report the solution NMR structure of the C-terminal modular pair from the μ-toxin. The three-helix bundle FIVAR module displays structural homology to a heparin-binding module within the N-terminal of the a C protein from group B Streptoccocus . The C-terminal module has a typical calcium-binding dockerin fold comprising two anti-parallel helices that form a Planar Face with EF-hand calcium-binding loops at opposite ends of the module. The size of the helical Face of the μ-toxin dockerin module is approximately equal to the Planar region recently identified on the surFace of a cohesin-like X82 module of Cp GH84C. Size-exclusion chromatography and heteronuclear NMR-based chemical shift mapping studies indicate that the helical Face of the dockerin module recognizes the Cp GH84C X82 module. These studies represent the structural characterization of a noncellulolytic dockerin module and its interaction with a cohesin-like X82 module. Dockerin/X82-mediated enzyme complexes may have important implications in the pathogenic properties of C. perfringens .

Jarek Rossignac - One of the best experts on this subject based on the ideXlab platform.

  • Hierarchical representation for rasterized Planar Face complexes
    Computers and Graphics, 2018
    Co-Authors: Guillaume Damiand, Jarek Rossignac, Aldo Gonzalez-lorenzo, Florent Dupont
    Abstract:

    A useful example of a Planar Face Complex (PFC) is a connected network of streets, each modeled by a zero-thickness curve. The union of these decomposes the plane into Faces that may be topologically complex. The previously proposed rasterized representation of the PFC (abbreviated rPFC) (1) uses a fixed resolution pixel grid, (2) quantizes the geometry of the vertices and edges to pixel-resolution, (3) assumes that no street is contained in a single pixel, and (4) encodes the graph connectivity using a small and fixed number of bits per pixel by decomposing the exact topology into per-pixel descriptors. The hierarchical (irregular) version of the rPFC (abbreviated hPFC) proposed here improved on rPFC in several ways: (1) It uses an adaptively constructed tree to eliminate the " no street in a pixel " constraint of the rPFC, hence making it possible to represent exactly any PFC topology and (2) for PFCs of the models tested, and more generally for models with relatively large empty regions, it reduces the storage cost significantly.

  • Rasterized Planar Face Complex
    Computer-Aided Design, 2017
    Co-Authors: Guillaume Damiand, Jarek Rossignac
    Abstract:

    We consider a Planar Face Complex (PFC). It is defined by the immersion of a Planar and connected graph G, which comprises a set of vertices joined by curved edges. G decomposes the plane into Faces that need not be manifold or open-regularized and may be bounded by a single loop edge. The PFC may, for example, be used to represent the complex street network of a city, the decomposition of a continent into countries, or the inhomogeneous structure made of a large set of regions of different materials possibly with internal cracks. The rasterized Planar Face Complex (rPFC) proposed here provides a compact representation of an approximation of a PFC, where the precise location of each vertex is quantized to the pixel that contains it and where the precise geometry of each curved edge is approximated by the ordered list (with possible repetitions) of the pixels traversed by (a chosen polygonal approximation of) the edge. We claim three key contributions: (1) The geometric error between a PFC and its rPFC is bounded by the pixel half-diagonal. (2) In spite of such a drastic discretization of the geometry, the rPFC captures the exact topology of the original PFC (provided that no street lies entirely inside a single pixel) and supports standard graph traversal operators that permit to walk the loop of sidewalks along the streets that bound a Face, to cross a street to the opposite sidewalk, or to cross streets in order while walking around their common junction. (3) The local connectivity and order information needed to provide the above functionality is stored at each pixel using only about 4 bits per crossing. We discuss the details of this representation, our implementation of its exact construction, four possible embodiments that offer different space/time efficiency compromises, experimental results, relations between rPFC and prior solutions.