145 research outputs found

    CHAIN-WISE GENERALIZATION OF ROAD NETWORKS USING MODEL SELECTION

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    On the Geometries of Conic Section Representation of Noisy Object Boundaries

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    This paper studies some geometrical properties of conic sections and the utilization of these properties for the generation of conic section representations of object boundaries in digital images. Several geometrical features of the conic sections, such as the chord, the characteristic point, the guiding triangles, and their appearances under the tessellation and noise corruption of the digital images are discussed. The study leads to a noniterative algorithm that takes advantage of these features in the process of formulating the conic section parameters and generating the approximations of object boundaries from the given sequences of edge pixels in the images. The results can be optimized with respect to certain different criteria of the fittings

    A conceptual design tool: a sketch and fuzzy logic based system

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    Abstract: A real-time sketch and fuzzy logic based prototype system for conceptual design has been developed. This system comprises four phases. In the ® rst one, the system accepts the input of online free-hand sketches, and segments them into meaningful parts by using fuzzy knowledge to detect corners and in¯ection points on the sketched curves. The fuzzy knowledge is applied to capture user' s drawing intention in terms of sketching position, direction, speed and acceleration. During the second phase, each segmented subpart (curve) can be classi® ed and identi® ed as one of the following two-dimensional primitives: straight lines, circles, circular arcs, ellipses, elliptical arcs or B-spline curves. Then, two-dimensional topology information (connectivity, unitary constraints and pairwise constraints) is extracted dynamically from the identi® ed two-dimensional primitives. From the extracted information, more accurate two-dimensional geometry can be built up by a two-dimensional geometric constraint solver. The two-dimensional topology and geometry information is then employed to further interpretation of a three-dimensional geometry. The system can not only accept sketched input but also users' interactive input of two-and three-dimensional primitives. This makes it friendly and easier to use, in comparison with`sketched input only' or interactive input only' systems. Finally, examples are given to illustrate the system

    Computer-Aided Geometry Modeling

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    Techniques in computer-aided geometry modeling and their application are addressed. Mathematical modeling, solid geometry models, management of geometric data, development of geometry standards, and interactive and graphic procedures are discussed. The applications include aeronautical and aerospace structures design, fluid flow modeling, and gas turbine design

    ARCHITECTURE ESTIMATION FROM SPARSE IMAGES USING GRAMMATICAL SHAPE PRIORS FOR CULTURAL HERITAGE

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    The estimation and reconstruction of 3D architectural structures is of great in- terest in computer vision, as well as cultural heritage. This dissertation proposes a novel approach to solve the di??cult problem of estimating architectural structures from sparse images and e??ciently generating 3D models from estimation results for cultural heritage. This approach takes as input one plan drawing image and a few fac¸ade images, and provides as output the volumetric 3D models which represent the structures in the sparse images. Support of this research goal has motivated new investigations in underlying structure estimation problems including detecting structural feature points in 2D images, decomposing plan drawings into semantically meaningful shapes for medieval castles, estimating rectangular and Gothic fac¸ades using shape priors, and estimating complete 3D models for architectural structures using a novel volumetric shape grammar. Major outstanding challenges in each of these topic areas are addressed resulting in contributions to current state-of-the-art as it applied to these di??cult problems

    Hyperaccurate Ellipse Fitting without Iterations

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    This paper presents a new method for fitting an ellipse to a point sequence extracted from images. It is widely known that the best fit is obtained by maximum likelihood. However, it requires iterations, which may not converge in the presence of large noise. Our approach is algebraic distance minimization; no iterations are required. Exploiting the fact that the solution depends on the way the scale is normalized, we analyze the accuracy to high order error terms with the scale normalization weight unspecified and determine it so that the bias is zero up to the second order. We demonstrate by experiments that our method is superior to the Taubin method, also algebraic and known to be highly accurate
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