G. Barequet and M. Sharir. Partial surface and volume matching in three dimensions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(9):929--948, September 1997.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....[7] 8] 9] Researchers have also continued to expand the standard pointbased least squares method ( 10] 11] 12] 13] 14] The novel solution introduced in [15] uses Fourier transforms to bypass the need for explicit correspondences. Gradient descent techniques have been explored by [16] and [17] Unlike the vast majority of previous AO research (including most of the aforementioned references) this paper explicitly addresses two various noise phenomenon outliers and mismatches in both algorithm development and analysis. In Section III A a formal definition of the AO ....

G. Barequet and M. Sharir, "Partial surface and volume matching in three dimensions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 9, pp. 929--948, September 1997.

....geometry compression algorithm developed earlier and thus our work is complementary to the earlier work in this field. 3.3 Repeating Component level Features In a given 3D model, one does not know which repeating feature patterns to expect. Hence, the traditional approach of matching 3D objects [23, 2, 4, 14, 13] by maintaining a dictionary of features objects and then retrieving those features in the given model is not applicable for our work. Our goal is to automatically discover repeating feature patterns in a polygon mesh models, without using a knowledge base of known features. In most engineering ....

....(2) carry out discovery of repeating feature patterns at the connected component level (described in 3.3.1) and build the master geometry instance transform hierarchy. 3.3. 1 Detecting Repeating Components Partial matching of polygonal shapes has been an area of interest in the recent years [23, 2, 4, 14, 13]. Many sophisticated algorithms have been developed for robust matching and alignment of similar shapes. In our implementation we have used a simple and efficient technique based on principal component analysis (PCA) with suitable extensions to overcome its limitations. While this is a very simple ....

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Gill Barequet and Micha Sharir. Partial Surface and Volume Matching in Three Dimensions. IEEE PAMI, 19(9):929--948, 1997.

....with the surface of the model in the specified pose. The quantization of range images into voxels, and the related octree representation, is a technique that has been used to advantage in mesh generation [12, 13] image registration [14] gaze planning [15] and recently object recognition [16]. While many recognition methods quantize some mapping of the image signal [17, 18] to the author s knowledge, the only known method in which voxel template matching has been used directly is [19] Each voxel in the range image voxel map takes on one of four values : ffl surface (S) the voxel ....

....with the number of templates. ffl parallelizable: The process can be executed independantly accross seeds (seed parallelism) or piped accross phases (phase parallelism) ffl other sensing modes: The method can be applied directly to 2D binary images (as in OCR) or volumetric data (as in [16]) The continuing research will focus on revisiting some of the heuristics and algorithms used with the goal of improving upon the overall reliablility and efficiency of the method. It may be beneficial to add a hashing phase following the hypothesis generation phase, as in [18] Additional tree ....

Gill Barequet and Micha Sharir. Partial surface and volume matching in three dimensions. IEEE Trans. PAMI, 19(9):929--948, 1997.

....area over the last few years; a recent paper [7] gives an overview of different methods in this category. These methods can break the hardware limit and enable us to register different views scanned from arbitrary view points. For an overview of previous work, the paper by Barequat and Sharir [1] contains an extensive bibliography along with the description of their method based on geometric hashing. The Iterative Closest Points (ICP) algorithm[2] 5] is a well known method that is used to register images with significant overlap. The ICP algorithm starts from an initial configuration of ....

G. Barequet and M. Sharir. Partial surface and volume matching in three dimensions. IEEE PAMI, 19(9):929--948, September 1997.

....use the mean inclination of the fracture surface relative to the object s surface as an additional component of the signal. Taking this idea to its natural limit, one should consider fractures as surfaces rather than curves, and use surface matching techniques (as proposed by Berequet and Sharir [1, 2] and Levoy [7] to nd the adjacent fragments. This approach will surely supersede contourbased methods, once ways are found to reduce its formidable computational cost. In any case, it seems likely that the Fourier based techniques of this paper can be extended to two dimensional signals, and ....

Gill Barequet and Micha Sharir. Partial surface and volume matching in three dimensions. In IEEE Trans. on Pattern Analysis and Machine Intelligence (T-PAMI), volume 19, pages 929-948, September 1997. Available also at http: //www.cs.jhu.edu /~barequet/papers.html.

....or 3 dimensional volumes. We provide here only a sketchy review of the extensive existing literature, mostly in computer vision and pattern recognition, on full or partial surface and volume matching. More details can be found in the two comprehensive surveys [7, 11] and in a companion paper [4]. Some works (e.g. 9] depend on the ability to match significant features of the objects, like knobs and holes, whose existence is not usually guaranteed. Other methods, which do not rely on the existence of a certain type of features, are pose clustering [30] alignment [19] and, of course, ....

....(partial) volume matching problem, either with volume overlap or with volume complementarity. Our algorithm is more suited however for surface matching, where it is usually easier to generate directed footprints. The work reported here builds upon an earlier algorithm that we have developed in [4]. The new algorithm is based on the use of directed footprints, whereas the preceding algorithm used undirected footprints. We will later compare the two solutions and discuss the advantages (and only some disadvantages) of the new technique. Here is a brief overview of our algorithm. First, we ....

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G. Barequet and M. Sharir, Partial surface and volume matching in three dimensions, IEEE Trans. on Pattern Analysis and Machine Intelligence, 19 (1997), 929--948.

....The problem of finding a full or a partial match between three dimensional objects attracted considerable attention in the literature during the past decade. We omit here a review of the existing literature and refer the reader to the two comprehensive surveys [BJ, CD] and to a companion paper [BS]. We are given two sets of points representing two respective objects in 3 space, and expected to be spread more or less uniformly on the boundary of the corresponding objects or in the volumes that they occupy. In the former case we seek a partial (or full) surface match between the boundaries of ....

....we seek a volume match, involving either volume overlap or volume complementarity. Our work was motivated by earlier works on the partial curve matching technique, first proposed by Kalvin et al. KSSS] and by Schwartz and Sharir [SS] It builds upon an earlier algorithm that we have developed in [BS]. The new algorithm is based on the use of directed footprints, whereas the preceding algorithm used undirected footprints. Our algorithm does not depend on any correspondence between the two sets of input data points or on the existence of any predetermined features of the objects. It is very ....

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G. Barequet and M. Sharir, Partial surface and volume matching in three dimensions, Proc. 12th Ann. IAPR and IEEE Int. Conf. on Pattern Recognition, Jerusalem, Israel, vol. II, 1994, 610--614.

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G. Barequet and M. Sharir. Partial surface and volume matching in three dimensions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(9):929--948, September 1997.

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G. Barequet and M. Sharir. Partial Surface and Volume Matching in Three Dimensions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19, 1997.

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G. Barequet and M. Sharir, "Partial surface and volume matching in three dimensions", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 9, 1997, pp. 1-21.

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G. Barequet and M. Sharir, "Partial Surface and Volume Matching in Three Dimensions," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 9, pp. 929-948, Sept. 1997.

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G. Barequet and M. Sharir, "Partial surface and volume matching in three dimensions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 9, pp. 929-- 948, September 1997.

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G. Barequet and M. Sharir. Partial Surface and Volume Matching in Three Dimensions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19, 1997.

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