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"abstract": "Abstract—Structures of dynamic scenes can only be recovered using a real-time range sensor. Depth from defocus offers an effective solution to fast and dense range estimation. However, accurate depth estimation requires theoretical and practical solutions to a variety of problems including recovery of textureless surfaces, precise blur estimation, and magnification variations caused by defocusing. Both textured and textureless surfaces are recovered using an illumination pattern that is projected via the same optical path used to acquire images. The illumination pattern is optimized to maximize accuracy and spatial resolution in computed depth. The relative blurring in two images is computed using a narrow-band linear operator that is designed by considering all the optical, sensing, and computational elements of the depth from defocus system. Defocus invariant magnification is achieved by the use of an additional aperture in the imaging optics. A prototype focus range sensor has been developed that has a workspace of 1 cubic foot and produces up to 512 × 480 depth estimates at 30 Hz with an average RMS error of 0.2%. Several experimental results are included to demonstrate the performance of the sensor.",
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"content": "Abstract—Structures of dynamic scenes can only be recovered using a real-time range sensor. Depth from defocus offers an effective solution to fast and dense range estimation. However, accurate depth estimation requires theoretical and practical solutions to a variety of problems including recovery of textureless surfaces, precise blur estimation, and magnification variations caused by defocusing. Both textured and textureless surfaces are recovered using an illumination pattern that is projected via the same optical path used to acquire images. The illumination pattern is optimized to maximize accuracy and spatial resolution in computed depth. The relative blurring in two images is computed using a narrow-band linear operator that is designed by considering all the optical, sensing, and computational elements of the depth from defocus system. Defocus invariant magnification is achieved by the use of an additional aperture in the imaging optics. A prototype focus range sensor has been developed that has a workspace of 1 cubic foot and produces up to 512 × 480 depth estimates at 30 Hz with an average RMS error of 0.2%. Several experimental results are included to demonstrate the performance of the sensor.",
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"abstract": "The concept of depth from focus involves calculating distances to points in an observed scene by modeling the effect that the camera's focal parameters have on images acquired with a small depth of field. This technique is passive and requires only a single camera. The most difficult segment of calculating depth from focus is deconvolving the defocus operator from the scene and modeling it. Most current methods for determining the defocus operator employ inverse filtering. The authors reveal some fundamental problems with inverse filtering: inaccuracies in finding the frequency domain representation, windowing effects, and border effects. A general, matrix-based method using regularization is presented, which eliminates these problems. The new method is confirmed experimentally, with the results showing an RMS error of 1.3%.",
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"content": "The concept of depth from focus involves calculating distances to points in an observed scene by modeling the effect that the camera's focal parameters have on images acquired with a small depth of field. This technique is passive and requires only a single camera. The most difficult segment of calculating depth from focus is deconvolving the defocus operator from the scene and modeling it. Most current methods for determining the defocus operator employ inverse filtering. The authors reveal some fundamental problems with inverse filtering: inaccuracies in finding the frequency domain representation, windowing effects, and border effects. A general, matrix-based method using regularization is presented, which eliminates these problems. The new method is confirmed experimentally, with the results showing an RMS error of 1.3%.",
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"abstract": "When visualizing tubular 3D structures, external representations are often used for guidance and display, and such views in 2D can often contain occlusions. Virtual dissection methods have been proposed where the entire 3D structure can be mapped to the 2D plane, though these will lose context by straightening curved sections. We present a new method of creating maps of 3D tubular structures that yield a succinct view while preserving the overall geometric structure. Given a dominant view plane for the structure, its curve skeleton is first projected to a 2D skeleton. This 2D skeleton is adjusted to account for distortions in length, modified to remove intersections, and optimized to preserve the shape of the original 3D skeleton. Based on this shaped 2D skeleton, a boundary for the map of the object is obtained based on a slicing path through the structure and the radius around the skeleton. The sliced structure is conformally mapped to a rectangle and then deformed via harmonic mapping to match the boundary placement. This flattened map preserves the general geometric context of a 3D object in a 2D display, and rendering of this flattened map can be accomplished using volumetric ray casting. We have evaluated our method on real datasets of human colon models.",
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"content": "When visualizing tubular 3D structures, external representations are often used for guidance and display, and such views in 2D can often contain occlusions. Virtual dissection methods have been proposed where the entire 3D structure can be mapped to the 2D plane, though these will lose context by straightening curved sections. We present a new method of creating maps of 3D tubular structures that yield a succinct view while preserving the overall geometric structure. Given a dominant view plane for the structure, its curve skeleton is first projected to a 2D skeleton. This 2D skeleton is adjusted to account for distortions in length, modified to remove intersections, and optimized to preserve the shape of the original 3D skeleton. Based on this shaped 2D skeleton, a boundary for the map of the object is obtained based on a slicing path through the structure and the radius around the skeleton. The sliced structure is conformally mapped to a rectangle and then deformed via harmonic mapping to match the boundary placement. This flattened map preserves the general geometric context of a 3D object in a 2D display, and rendering of this flattened map can be accomplished using volumetric ray casting. We have evaluated our method on real datasets of human colon models.",
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"abstract": "The Voreen volume-rendering engine provides an environment for realizing volume visualizations, which exploit GPU-based ray casting techniques. To alleviate the development process, visual prototyping can be peformed by editing a data flow network graph. While this environment gives full flexibility when designing novel visualizations, the component-based architecture ensures that the realized visualization techniques can be exploited in interactive applications without any performance penalty.",
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"normalizedAbstract": "Abstract—A complete, fast and practical isolated object recognition system has been developed which is very robust with respect to scale, position and orientation changes of the objects as well as noise and local deformations of shape (due to perspective projection, segmentation errors and non-rigid material used in some objects). The system has been tested on a wide variety of three-dimensional objects with different shapes and material and surface properties. A light-box setup is used to obtain silhouette images which are segmented to obtain the physical boundaries of the objects which are classified as either convex or concave. Convex curves are recognized using their four high-scale curvature extrema points. Curvature Scale Space (CSS) Representations are computed for concave curves. The CSS representation is a multi-scale organization of the natural, invariant features of a curve (curvature zero-crossings or extrema) and useful for very reliable recognition of the correct model since it places no constraints on the shape of objects. A three-stage, coarse-to-fine matching algorithm prunes the search space in stage one by applying the CSS aspect ratio test. The maxima of contours in CSS representations of the surviving models are used for fast CSS matching in stage two. Finally, stage three verifies the best match and resolves any ambiguities by determining the distance between the image and model curves. Transformation parameter optimization is then used to find the best fit of the input object to the correct model.",
"title": "Silhouette-Based Isolated Object Recognition through Curvature Scale Space",
"normalizedTitle": "Silhouette-Based Isolated Object Recognition through Curvature Scale Space",
"fno": "i0539",
"hasPdf": true,
"idPrefix": "tp",
"keywords": [
"Object Recognition System",
"Light Box Setup",
"Boundary Contours",
"Curvature Scale Space Representation",
"Maxima Of Curvature Zero Crossing Contours",
"Coarse To Fine Matching Strategy",
"Transformation Parameter Optimization"
],
"authors": [
{
"givenName": "Farzin",
"surname": "Mokhtarian",
"fullName": "Farzin Mokhtarian",
"affiliation": null,
"__typename": "ArticleAuthorType"
}
],
"replicability": null,
"showBuyMe": true,
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"isOpenAccess": false,
"issueNum": "05",
"pubDate": "1995-05-01 00:00:00",
"pubType": "trans",
"pages": "539-544",
"year": "1995",
"issn": "0162-8828",
"isbn": null,
"notes": null,
"notesType": null,
"__typename": "ArticleType"
},
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|
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