Farshid Arman
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Abstract
The goal in computer vision systems is to analyze data collected from the environment and derive an interpretation to complete a specified task. Vision system tasks may be divided into data acquisition, low-level processing, representation, model construction, and matching subtasks. This paper presents a comprehensive survey of model-based vision systems using dense-range images. A comprehensive survey of the recent publications in each subtask pertaining to dense-range image object recognition is presented.
- ARMAN, F., AND AGGARWAL, J. K. 1993. CAD- based vision: Object recognition in cluttered range images using recognition strategies. In Computer Vision, Graphics, and Image Processing: Image Understanding. To be published. Google Scholar
- ARMAN, F., AND AGGARWAL, J.K. 1991. Automatic generation of recognition strategies using CAD models. In the IEEE Workshop on Dtrections tn Automated CAD-Based Vtswn (Maul, Hawaii, June 2-3). IEEE, New York, 124 133.Google Scholar
- ARMAN, F, AND ACGARWAL, J. K. 1990. Object recognition in dense range images using a CAD system as a model base. In Procee&ngs of the IEEE Conference on Robotics and Automation (Cincinnati, Oh., May 13-18). IEEE, New York, 1858 1863Google Scholar
- ASADA, H., AND BRADY. M. 1986. Curvature primal sketch. IEEE Trans. Part. Anal. Machine Intell. 8, I (Jan.), 2 14. Google Scholar
- BALLARD, D. $. 1981. Generahzmg the Hough transform to detect arbitrary shapes. Part. Recog. 13, p. 111.Google Scholar
- BALLARD, D. H., AND BROWN, C.M. 1982. Cornputer Vision. Prentice-Hall, Englewood Cliffs, N.J. Google Scholar
- BARR, A. H. 1981. Superquadrics and anglepreserving transformations. IEEE Cornput. Graph. Appl. 1, i (Jan.), 11-23.Google Scholar
- BAUMGART, B.G. 1972. Winged edge polyhedron representation. Tech. Rep. STAN-CS-320, AIM 179, Stanford Univ., Computer Science Dept., AI Lab. Stanford, Calif. Google Scholar
- BESL, P.J. 1989. Active optical range imaging. In Advances tn Machine V~ston, J. L. C. Sanz, Ed. Springer-Verlag, New York. Google Scholar
- BESL, P. J. 1988a. Surfaces in Range Image Understanding. Springer-Verlag, New York. Google Scholar
- BESL, P. J 1988b. Geometric modeling and computer vision. Proc. IEEE 76, 8 (Aug.), 936-958.Google Scholar
- BESL, P. J., AND JAIN, R.C. 1988. Segmentation through variable-order surface fitting. IEEE Trans. Patt. Anal. Machine Intell. 10, 2 (Mar), 167 192. Google Scholar
- BESL, P. J., AND JAIN, R C. 1986. Invanant surface characteristics for 3D object recognition in range images. Cornput. V~s~on Graph. Image Process. 33, 1, 33-80. Google Scholar
- BESL, P. J., ANU JAIN, R. C. 1985. Threedimensional object recognition. ACM Cornput. Surv 17, 1 (Mar.), 75-145. Google Scholar
- BHANU, B. 1984. Representation and shape matching of 3-D objects. IEEE Trans. Patt. Anal. Machine Intell. 6, 340 351.Google Scholar
- BaANu, B., AND HO, C.C. 1987a. CAD-based 3D object representation for robot wsion. Computer (Aug.), 19-35. Google Scholar
- BHANU, B, AND HO, C C 1987b. 3-D modehng for computer vision. Patt. Recog. Lett. 5, 349-356. Google Scholar
- BHANU, B. AND HO, C.C. 1986. Geometric design based 3-D models for machine vision. In 8th Internattonal Conference on Pattern Recognttion (Paris, Oct. 27 31), 107 110.Google Scholar
- BINFORD, T O. 1971. Visual perception by computer. In IEEE Conference on Systems and Control (Miami, Dec.). IEEE, New York.Google Scholar
- BOLLE, R. M., AND VEMURI, B. C. 1991 On three-dimensional surface reconstruction methods IEEE Trans. Patt. Anal. Machine Intell 13, i (Jan.), 1 13. Google Scholar
- BOULANGER, P., AND RIOUX, M. 1987. Segmentation of planar and quadric surfaces. In Intelhgent Robots and Computer Vtston: Sixth in Sertes. Proceedzngs of SPIE 848 (Cambridge, Mass., Nov. 2 6), 395-403.Google Scholar
- BOUL% T. E., AND GROSS, A. D. 1988. On the recovery of superellipsoids. In Proceedtngs of the DARPA Image Understandtng Workshop. DARPA, Washington, D.C., 1052-1063.Google Scholar
- BOWYER, K. 1991. Why aspect graphs are not (yet) practmal for computer vision--A discussion. In IEEE Workshop on Directtons in Automated CAD-Based Vision (Mare, Hawaii, June 2-3). IEEE, New York, 98-104Google Scholar
- BOWYER, K., AND DYER, C. 1990. Aspect graphs An introduction and survey of recent results. In Close Range Photograrnrnetr~ Meets Machtne Vtszon. Proceedtngs of SPIE, vol. 1395, 200-208.Google Scholar
- BOYER, K. L, AND KAK, A.C. 1987. Color-encoded structured light for rapid active ranging. IEEE Trans. Putt. Anal. Machine Intell. 9, i (Jan.), 14-28. Google Scholar
- BRADY, M, PONCE, J., YULLIE, A., AND ASADA, H. 1985 Describing surfaces. Comput. Vision Graph. Image Process. 32, pp. 1-28.Google Scholar
- BROWN, C. M. 1982. PADL-2: A technical summary. IEEE Cornput. Graph. Appl. 2, 2 (Mar.), 69-84.Google Scholar
- BURT, P.J. 1983. Fast algorithms for estimating local image properties. Cornput. Graph. Image Process. 21, pp. 368 386.Google Scholar
- BURT, P. J., HONG, T. H., AND ROSENFELD, A. 1981. Segmentation and estimation of image region properties through cooperative hmrarchical computation. IEEE Trans. Syst. Man Cybernettcs SMC-11, 12 (Dec.), 802-809.Google Scholar
- CALLAHAN, J., AND WEISS, R. 1985. A model for describing surface shape. In Proceedtngs of Computer Vzsion and Pattern Recognttton (San Francisco, June 19 13), 240 245.Google Scholar
- CANNY, J. 1986. A computational approach to edge detection. IEEE Trans. Part. Anal. Machtne Intell. 8, 6 (Nov.), 679 698. Google Scholar
- CHEN, S., AND STOC~IAN, G. 1989. Object wings --2 1/2-D Primitives for 3-D Recogmtion. In Proceedings of Computer Vision and Pattern Recogmtton (San Diego, June 4-8), 535-540.Google Scholar
- CHIN, R. T., AND DYER, C.R. 1986. Model-based recognition in robot vision. ACM Cornput. Surv. 18, 1 (Mar.), 67-108. Google Scholar
- CmEN, C. H., AND AGGARWAL, J.K. 1989. Model construction and shape recognition from occluding contours. IEEE Trans. Patt. Anal. Machine Intell. 11, 4 (Apr.), 372-389. Google Scholar
- CSmN, C. H., Sin, Y. B., AND AGGARWAL, J. K. 1988. Generation of volume/surface octree from range data. In Proceedings of Computer Vision and Pottern Recognition (Ann Arbor, Mich., June 5-8), pp. 254 260.Google Scholar
- DEO, N. 1974. Graph Theory with Applications to Engineering and Computer Science. Prentice-Hall, Englewood Cliffs, N.J. Google Scholar
- Do CARMO, M.P. 1976. D~fferenhal Geometry of Curves and Surfaces. Prentice-Hall, Englewood, NJ.Google Scholar
- EGGERT, D., AND BOWYE~, K. 1989. Computing the orthographic projection aspect graph of solids of revolution. In IEEE Workshop on Interpretation of 3-D Scenes (Austin, Tex., Nov. 27-29). IEEE, New York, 102-108.Google Scholar
- EVANS, F. 1986. A survey and comparison of the Hough transform. In Computer Architecture for Pattern Analyszs and Image Database Management, 378-380.Google Scholar
- FAN, T.J. 1990. Describing and Recognizing 3-D Objects Using Surface Properties. Springer- Verlag, New York. Google Scholar
- FAN, T. J., MEmom, G., AND NEVAT~A, R. 1989. Recognizing 3-D objects using surface descriptions. IEEE Trans. Putt. Anal. Machine Intell. 11, 11 (Nov.), 1140-1157. Google Scholar
- FAN, T. J., MEmONI, G., ANn NEVATTA, R. 1987. Segmented descriptions of 3-D surfaces. IEEE Int. J. Robot. Automat. 3, 6 (Dec.), 527 538.Google Scholar
- FAUGERAS, O.D. 1984. New steps toward a flexible 3-D vision system for robotics. In The 2nd Internahonal Symposium on Robottcs Research (Kyoto, Japan, Aug. 20 23, 1985), 25-33.Google Scholar
- FAUGE~AS, O. D., AND HEBERT, M. 1987. The representation, recognition, and positioning of 3-D shapes from range data. In Techniques for 3-D Machine Percephon, A. Rosenfeld, Ed. North-Holland, Netherlands, 13-52. Also, In Three-Dimensional Machine Vision. Kluwer Academic Publishers, Boston, 301 353.Google Scholar
- FAux, I. D., AND PRATT, M. J. 1985. Computational Geometry for Design and Manufacture. John Wiley and Sons, New York. Google Scholar
- F~SnER, R. B. 1990. Determining back-facing curved model surfaces by analysis at the boundary. In The 3rd hlternational Conference on Computer Vision (Osaka, Japan, Dec. 4-7), 296-303.Google Scholar
- FLYNN, P. J., AND JAIN, A. K. 1992. 3-D object recognition using invariant feature indexing of interpretation tables. Comput. Vision Graph. Image Process.: Image Understand. 55, 2 (Mar.) 119-129. Google Scholar
- FL~N, P. J., AND JAIN, A. K. 1991a. BONSAI: 3-D object recognition using constrained search. IEEE Trans. Patt. Anal. Machine Intell. 13, 10 (Oct.), 1066-1075. Google Scholar
- FLYNN, P. J., AND JAIN, A.K. 1991b. CAD-based computer vision: From CAD models to relational graphs. IEEE Trans. Patt. Anal. Machine Intell. 13, 2 (Feb.), 114-132. Google Scholar
- FLYNN, P. J., AND JAIN, A. K. 1989. On reliable curvature estimation. In The Proceedings of Computer Vision and Pattern Recogn~twn (San Diego, Calif., June 4-8), 110 116.Google Scholar
- FLYNN, P. J., AND JAIN, A.K. 1988. Surface classification: Hypothesis testing and planar estimation. In Proceedings of Computer V~slon and Pattern Recognition (Ann Arbor, Mich., June 5 9), 261 267.Google Scholar
- FREEMAN, H. 1988. Machine Vtsion Algorithms, Archztectures, and Systems. Academic Press, San Diego, Calif. Google Scholar
- Fu, K. S., GONZALEZ, R. C., AND LEE, C. S.G. 1987. Robotics: Control, Sensing, Vision, and Intelligence. McGraw-Hill, New York. Google Scholar
- GARmNER, M. 1965. The superellipse: A curve that lies between the ellipse and the rectangle. Scl. Amer. 231, 222 234.Google Scholar
- GIGUS, Z., AND MALIK, J. 1988. Computing the aspect graph for line drawing of polyhedral objects. In Proceedings of Computer Vision and Pattern Recognition (Ann Arbor, Mich., June 5-8), 654 661.Google Scholar
- GILBARG, D., AND TRUNDINGER, N. 1983. Elliptic Partial Differential Equations of Second Order. Springer-Verlag, New York.Google Scholar
- GOAD, C. 1983. Special purpose automatic programming for 3D model-based vision. In Proceedmgs of DARPA Image Understandtng Workshop (Cambridge, Mass., Apr. 6-8), 94-104.Google Scholar
- GODIN, G. D., AND LEVINE, M. D. 1989. Structured edge map of curved objects in a range image. In Proceedzngs of Computer Vision and Pattern Recognttton (San Diego, Calif., June 4-8), 276 281.Google Scholar
- GRIMSON, W. E. L. 1990. The effect of indexing on the complexity of object recognition. In Proceedtngs of the 3rd International Conference on Computer Vision (Osaka, Japan, Dec. 4-7), 644 651.Google Scholar
- GR~MSON, W. E. L. 1989. On the recognition of curved objects. IEEE Trans. Patt. Anal. Machtne Intell. 11, 6 (June), 632-642. Google Scholar
- GRIMSON, W. E. L. 1987. On the recognition of parameterized objects. In The 4th International Symposium on Robotics Research (Santa Cruz, Calif. Aug.). Google Scholar
- GRIMSON, W. E. L., AND HUTTENLOCHER, D. P. 1990a. On the sensitivity of geometric hashing. In Proceedings of the 3rd International Con{erence on Computer Viswn (Osaka, Japan, Dec. 4 7), 334 335.Google Scholar
- GRIMSON, W. E. L., AND HUTTENLOCHER, D. P. 1990b. On the sensitivity of the Hough transform for object recognition. IEEE Trans. Part. Anal. Machine Intell. 12 3 (Mar.), 255-274. Google Scholar
- GRIMSON, W. E. L., AND LOZANO-PEREZ, T. 1987. Localizing overlapping parts by searching the interpretation tree. IEEE Trans. Patt. Anal. Machine Intell. 9, 4 (Apr.), 469 482. Google Scholar
- GRUSS, A., KANADE, T., AND CARLEY, L. R. 1990. A fast hghtstripe range finding system with smart VLSI sensor. In Machine Vision for Three-Dtmensional Scenes, H. Freeman, Ed. Academic Press, San Diego, Calif., 381-397.Google Scholar
- GUGGENHEIMER, H.W. 1977. Dtfferentlal Geometry. Dover, New York.Google Scholar
- HAMILTON, W.R. 1969. Elements of Quaternions. Chelsea, New YorkGoogle Scholar
- HAN, J., VOLTZ, R. A., AND MUDGE, T. N. 1987. Range image segmentation and surface parameter extraction for 3-D object recognition of industrial parts In Proceedzng's of the 1987 IEEE Conference on Robotics and Automation (Raleigh, N.C., Mar. 31-Apr. 3). IEEE, New York, 380-386.Google Scholar
- HANSEN, C., AND HENDERSON, T. C. 1989. CAGD-based computer vision. IEEE Trans. Part. Anal. Machine Intell. 11, 11 (Nov.), 1181-1193. Google Scholar
- HANSEN, C., AND HENDERSON, T. C. 1988 Towards automatic generation of recognition strategies. In Proceedings of the Internattonal Conference on Computer Viston (Tampa, Fla., Dec. 5-8), 275-279. Google Scholar
- HERSMAN, M., GOODWIN, F., KElX~AN, S., AND SLOTWINSKI, A. 1987. Coherent laser radar application to 3D vision and metrology. In Vision '87: Conference Proceeding (Detroit, Mmh, June 8-11), section 3, 1-12.Google Scholar
- HILBERT, D., AND COHN-VOSSEN, S 1952. Geome-try and the Imagination. Chelsea. New York.Google Scholar
- HOFFMAN, D. A. 1971. Impossible objects as non-sense sentences. Machzne Intell. 6.Google Scholar
- HOFFM^N, R., AND JAIN, R. C. 1987. Segmentation and classification of range images. IEEE Trans Patt Anal. Machine Intell 9, 5 (Sept.), 608 620. Google Scholar
- HOFFMAN, R., KESHAVAN, H. R., AND TOWFIQ, F. 1989. CAD-driven machine vision IEEE Trans. S. yst. Man Cybernet~c,s 19, 6 (Nov./Dec.), 1477-1488.Google Scholar
- HEN6, K. S, IKEUCHI, K., AND GREMBAN, K. D. 1990. Minimum cost aspect classification: A module of a vision algorithm compiler. In Proceedtngs of the lOth Internatmnal Conference on Pattern Recogniaon (Atlantic City, N.J., June 16 21), 65 69.Google Scholar
- HC~RN, B. K P 1986 Robot Vzszon. MIT Press, Cambridge, MassGoogle Scholar
- HORN, B. K.P. 1984. Extended Gaussianimages. Prec. IEEE 72, 12 (Dec.), 1671 1686Google Scholar
- HSmNG, C.C. 1981. A First Course tn Dif/brential Geometry. Wiley-Interscience, New York.Google Scholar
- HURT, S. L., AND ROSENFELD, A. 1984. Noise reduction m three-dimensional digital images. Patt. Recog. 17, 4, 407 421.Google Scholar
- IKEUEHI, K. 1987a. Generating an interpretation tree from a CAD model for 3D object recognition in bin-picking tasks. Int. J. Comput. Vtsion 1, 145-165.Google Scholar
- IKEUCm, K. 1987b. Pre-compiling a geometrical model into an interpretation tree for object recognition in bin-picking tasks. In DARPA Image Understanding Workshop (Los Angeles, Feb. 23-25), 321-339.Google Scholar
- IKEUCHI, K., AND HEN6, K.S. 1989. Determining linear shape change: Toward automatic generation of object recognition programs. In Proceedlngs of Computer Vision and Pattern Recognition (San Diego, Calif., June 4-8), 450 457.Google Scholar
- IKEUCHI, K., AND KANADE, T. 1988. Applying sensor models to automatic generation of object recognition programs. In Proceedings of Internatwnal Conference on Computer V~sion (Tampa, Fla., Dec. 5-8), 228-237.Google Scholar
- IKEUCHI, K., AND ROBERT, J. C. 1989. Modeling sensor detectability with VANTAGE geometric modeler. In Image Understanding Workshop (Pale Alto, Calif., May 23 26), 721 746. Google Scholar
- INOKUCH1, S., SATe, K., AND M2kTSUDA, F. 1984. Range-imaging system for 3-D object recognition. In Proceedings of the 7th International Conference on Pattern Recognztion (Montreal, Quebec, July 30-Aug. 2), 806-808.Google Scholar
- JAIN, A. K., AND DUBES, R. C 1988. Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs, N.J. Google Scholar
- JAIN, A. K., AND HOFFMAN, R. 1988. Evidencebased recognition of 3-D objects. IEEE Trans Patt. Anal. Machine Intell. 10, 6 (June), 783-802. Google Scholar
- JACKINS, C. L., AND TANIMOTO, S. L. 1980. Octtrees and their use m representing three- &menmonal objects. Comput. Graph. Image Process. 14, 3 (Nov.), 249-270.Google Scholar
- JARWS, R.A. 1983a. A perspective on range finding techniques for computer vision. IEEE Trans. Patt. Anal. Machine Intell. 5, 2 (Mar.l, 122-139.Google Scholar
- JARVIS, R.A. 1983b. A laser time-of-flight range scanner for robot vision. IEEE Trans. Part. Anal. Machine Intell 5, 5 (Sept.), 505-512.Google Scholar
- JObLIFFE, I. T. 1986. Prznczpal Component ,A~c~ly~ie, Springer-Verlag, New York.Google Scholar
- KAK, A. C., BOYER, K. L., CHEN, C. H., SAFRANEK, R. J., AND YANG, H. S. 1986. A knowledgebased robotic assembly cell. IEEE Expert (Spring).Google Scholar
- KAK, A. C., VAYADA, A. J. CORMWELL, R. L., KIM, W. Y., AND CHEN, C. H. 1987. Knowledgebased robotics. In Proceedzngs of the IEEE Internatwnal Conference on Robotics and Automation. IEEE, New York, 637 644.Google Scholar
- KANADE, T. 1987. Three-Dlmenswnal Machuze Vlszon. Kluwer Academic Publishers, Boston, Mass. Google Scholar
- KANAOE, T., BALAKAMAR, P., ROBERT, J. C., HOFFMAN, R., AND IKEUCHI, K. 1988. VANTAGE: A frame-based geometric modeler with explicit symbolic representation of 3-D and 2-D information. In Proceedings of the International Symposium and Exposition on Robots (Sydney, Australia, Nov. 6-8), 1405 1420.Google Scholar
- KANADE, T., GRUSS, A., AND CARLEY, L.R. 1989. A VLSI sensor based range finding system. In The 5th International Symposium on Robotics Research (Tokyo, Aug.). Google Scholar
- KXN~, S. B, ANn I~mucm, K. 1991. Determining 3-D object pose using the complex extended Gaussian image. In Proceedings of Computer Viston and Pattern Recognition (Maul, Hawaii, June 3-6), 580-585.Google Scholar
- KIM, W. Y., AND KAK, A. C. 1991. 3-D object recognition using bipartite matching embedded in discrete relaxation. IEEE Trans. Patt. Anal. Machine Intell. 13, 3 (Mar.), 224-251. Google Scholar
- I4AMUP~, F., AND HOSAKA, M. 1978. Program package GEOMAP. In Proceedings of Geometric Model Project Meeang, CAM-I.Google Scholar
- KOENDERIK, J.J. 1990. Solid Shape. MIT Press, Cambridge, Mass. Google Scholar
- KOENDERm, J. J., AND VAN DOORN, A. J. 1979. The internal representation of solid shape with respect to vision. Bio. Cybernetics 32, 211 216.Google Scholar
- KOENDERIK, J. J., AND VAN DOORN, A. J. 1976. The singularities of the visual mapping. Bw. Cybernettcs 24, 51 59.Google Scholar
- KORN, M. R., AND DYER, C.R. 1987. 3-D multiview object representations for model-based object recognition. Part. Recog. 20, 1, 91 104. Google Scholar
- KOSHIKAWA, K., AND SHIRAI, Y. 1985. A 3-D modeler for vision research. In Proceedings of Internattonal Conference on Advanced Robotws, 185 190.Google Scholar
- KRIEGMAN, D. J., AND PONCE, J. 1989. Computing exact aspect graphs of curved objects: Solids of revolution. In Proceedings of the IEEE Workshop on the Interpretatwn of 3-D Scenes (Austin, Tex., Nov. 27-29). IEEE, New York, 116-122.Google Scholar
- KRISHNAPARAM, R., AND CASANET, D. 1989. Determination of three-dimensional object location and orientation from range images. {EEE Trans. Part. Anal. Machine Intell. 11, 11 (Nov.), 1158 1167. Google Scholar
- KUN~, T. L., SATOn, T., AND YAMA6UCm, K. 1985. Generation of topological boundary representation from octree encoding. IEEE Comput. Graph. Appl. 5, 3 (Mar.), 29 38.Google Scholar
- LAMDAN, Y., AND WOLFSON, H.J. 1988. Geometric hashing: A general and efficient model-based recognition scheme. In Proceedings of the 3rd International Conference on Computer Vision (Osaka, Japan, Doe. 4-7), 238-249.Google Scholar
- LEWIS, R. A., AND JOHNSTON, A.R. 1977. A scanning laser range finder for a robotic vehicle. In Proceedings of the 5th International Joint Conference on Artificial Intelligence (Cambridge, Mass., Aug. 22 25), 762-768.Google Scholar
- Lo, C. H., AND DON, H.S. 1989. Representation and recognition of 3-D curves. In Proceedings of Computer Vision and Pattern Recognition (San Diego, Calif., June 4-8), 523 528.Google Scholar
- MIcRoCAD NEWS 1990. CAD/CAM and NC: Separate and equal. MicroCAD News 5, 10 (Nov.), 40 41.Google Scholar
- MACKWORTH, A. K., AND MOKHTARIAN, F. 1988. The renormalized curvature scale space and the evolution properties of planar curves. In Proceedings of Computer Vision and Pattern Recognition (Ann Arbor, Mich., June 5-9), 318-326.Google Scholar
- MACKWORTH, A. K., AND MOKHTARIAN, F. 1986. Scale-based description and recognition of planar curves and two dimensional shapes. IEEE Trans. Patt. Anal. Machine Intell. 8, i (Jan.), 34-43. Google Scholar
- MARR, D. AND HILDRETH, E. 1980. Theory of edge detection. In Proceedings of the Royal Society of London, series B, vol. 207, 195-240.Google Scholar
- MARR, D., AND POGGIO, T. 1977. A theory of human stereo vision. MIT AI Lab Memo 451, (Nov). Google Scholar
- MASTIN, G.A. 1985. Adaptive filters for digital image noise smoothing: An evaluation. Comput. Vision Graph. Image Process. 31, 103 121.Google Scholar
- MAZUMDER, P. 1988. A new strategy for octree representation of three-dimensional objects. In Proceedings of Computer Vision and Pattern Recognition (Ann Arbor, Mich., June 5-8), 270 275.Google Scholar
- MEYER, A. 1991. 3D modeling techniques. MicroCAD News 6, 6 (June), 23-29, 39.Google Scholar
- MO~TARIAN, F. 1988. Multi-scale description of space curves and three-dimensional objects. In Proceedings of Computer V~swn and Pattern Recogmtion (Ann Arbor, Mich., June 5 9), 298-303.Google Scholar
- MUNDY, J. L., AND PORTER, G. B., III 1987. A three-dimensional sensor based on structured light. In Three-Dimensional Machine Vtsion, T. Kanade, Ed. Kluwer Academic Publishers, Boston, Mass., 3 61.Google Scholar
- NALWA, V.S. 1988. Representing oriented piecewise C2 surfaces. In Proceedings of the International Conference on Computer Vision (Tampa, Fla., Dec. 5 8), 40-51.Google Scholar
- NITZAN, D. 1988. Three-dimensional vision structure for robot applications. IEEE Trans. Patt. Anal. Machine Intell. 10, 3 (May), 291 309. Google Scholar
- PARK, H. D., AND MITCHELL, O. R. 1988. CAD based planning and execution of inspection. In Proceedings of Computer Vision and Pattern Recognitmn (Ann Arbor, Mich., June 5 9), 858-863.Google Scholar
- PENTLAND, A. P. 1987. Recognition by parts. In Proceedings of the 1st International Conference in Computer Vtswn (London, England), 612- 620.Google Scholar
- PENTLAND, A. P. 1984. Perceptual organization and the representation of natural form. In Readings in Computer Vision, 680-699. Google Scholar
- PERONA, P., AND MALIK, J. 1987 Scale space and edge detection using anisotropic diffusion. In Proceedings of the IEEE Workshop on Computer Vtslon (Miami, Fla., Nov.), 16 22.Google Scholar
- P~CTON, P.D. 1987. Hough transform references. Int. J. Patt. Recog. Artif. Intell. 1,413 425.Google Scholar
- PLANTINC~, W. H., AND DYER, C.R. 1986. An algorithm for constructing an aspect graph. In Proceedmgs of the IEEE Symposium on the Foundatton of Computer Sctences. IEEE, New York, 123-131.Google Scholar
- PONCE, J., AND BRADY, M. 1987. Toward a surface primal sketch. In Three-Dimensional Machtne Vzslon, T. Kanade, Ed. Kluwer Academic Publishers, Boston, Mass, 195-240.Google Scholar
- PONCE, J, CHELBERG, D., AND MANN, W.B. 1989. Invariant properties of straight homogeneous generalized cylinders and their contours. IEEE Trans Part. Anal. Machine Intell. 11, 9 (Sept.), 951-966. Google Scholar
- PRATT, W. K. 1978. Digital Image Processing. Wlley-Interscience, New York. Google Scholar
- REQUICHA, A, AND CHAN, S.C. 1986. Representation of gemnetric features, tolerances, and attributes in solid modelers based on constructive geometry. IEEE J. Robot. Automat. RA-2, 3 (Sept.), 156-166Google Scholar
- RIoux, M. 1984. Laser range finder based on synchronized scanners. Appl Opt. 23, 3837- 3844.Google Scholar
- RIOUX, M., BLIAS, F., AND BOULANGER, P. 1989. Range imaging sensors development at NRC laboratories. In Proceedings of Workshop on Interpretation of 3-D Scenes (Austin, Tex., Nov ), 154-161.Google Scholar
- ROACH, J. W., AND WRIGHT, J. S 1986 Spherical dual images: A 3-D representation method for solid objects that combines dual space and Gausman spheres. In Proceedtng~ of the Computer Vtslon and Pattern Recognttmn (Miami Beach, Fla., June 22 26). 236-241.Google Scholar
- RoAca, J W, PARmATh P. K., AND WRIGHT, J. S. 1987 A CAD system based on spherical dual representations. Computer (Aug.), 37-44. Google Scholar
- SABATA, B., ARMAN, F., AND AC~OARWAL, J.K. 1993. Segmentation of 3-D range images using pyra midal data structures. Comput Vtsion Graph. Image Process.: Image Understand. To be published. Google Scholar
- SABATA, B. ARMAN, F , AND AGGARWAL, J.K. 1990. Segmentation of 3-D range images using pyramidal data structures. In Proceedings of the Internahonal Conference m Computer Vtsion (Osaka, Japan, Dec. 4-7), 662--665.Google Scholar
- SAINT-MARC, P, AND MEDIONI, G. 1988. Adaptive smoothing for feature extraction. In Proceedings of the Image Understanding Workshop (Cambridge, Mass. Apr. 6-8), 1100 1113.Google Scholar
- SALLAM, M., STEWMAN, J., AND BOWYER, K. 1990. Computing the visual potential of an articulated assembly of parts. In Proceedings of the International Conference in Computer Vlsmn (Osaka, Japan, Dec 4-7), 636-643.Google Scholar
- SEAMS, W. S. 1990. Computer Numerical Control: Concepts and Programming. Delmar, Albany, N.Y.Google Scholar
- SOLINA, F., AND BAJCSY, R. 1990. Recovery of parametric models fi'om range images: The case for superquadrics with global deformations. IEEE Trans. Patt. Anal. Machine Intell. 12, 2 (Feb.), 131-147. Google Scholar
- SRIPRADISVARAKUL, T., AND JAIN, R. 1989. Generating aspect graph for curved objects. In Proceedings of IEEE Workshop on the Interpretation of 3-D Scenes (Austin, Tex., Nov. 27-29). IEEE, New York, 109 115.Google Scholar
- STEWMAN, J., AND BOWYER, K. 1988. Creating the perspective projections aspect graph of polyhedral objects. In Proceedings of the International Conference on Computer Vzston (Tampa, Fla., Dec. 5-8), 494-500.Google Scholar
- STOCKER, J.J. 1969. Differenttal Geometry. John Wiley and Sons, New York.Google Scholar
- STOKES, H 1991. Testing IGES exchange methods. MlcroCAD News 6, 7 (July), 46-51, 80.Google Scholar
- TAJIMA, J., AND IWAKAWA, M. 1990. 3-D data acquisitmn by Rainbow range finder. In Proceedmgs of the lOth International Conference on Pattern Recognttion (Atlantic City, N.J., June 6 12), 309-313.Google Scholar
- TAYLOR, R. W., SAVINI, M., ANn REEVES, A.P. 1989. Fast segmentatmn of range imagery into planar regions. Comput. VLsion Graph. Image Process. 45, 42-60. Google Scholar
- TAUBIN, G., BOLLE, R. M,, AND COOPER, D.B. 1989. Representing and comparing shapes using shape polynomials. In Proceedings o/Computer Vision and Pattern Recognition (San Diego, Calif, June 4-8), 510-516.Google Scholar
- TECHNICAL Ap~I'S CORPORATION. 1987. Techntcal Arts IOOX Users Manual and Apphcatton Programming Guide. Technical Arts Corporation.Google Scholar
- TURNER, G. P., AND ANDERSON, D. C. 1988. An obJect-oriented approach to interactive, feature based design for quick turnaround manufacturing. In Proceedings of the 1988 ASME Internattonal Computers in Engineering Conference and Exhibitton. ASME, New York, 551-555.Google Scholar
- U.S. DEPARTMENT OF COMMERCE, 1988. Initial Graphics Exchange SpectfTcatton, Verston 4.0 National Bureau of Standards, Washington, D.C. Also Published by Society for Automotive Engineering (SAE), Warrendale Pa.Google Scholar
- VEMURI, B. C., AND AGGARWAL, J.K. 1988. Localization of objects from range data. In Proceedings of Conference on Computer Vision and Pattern Recognition (Ann Arbor, Mich., June 5 9), 893-898.Google Scholar
- VEMURI, B. C., AND AGGARWAL, J. K. 1986. 3-D model constructmn from multiple views using range and intensity data. In Proceedmgs of Conference on Computer Vtsmn and Pattern Recognition (Miami Beach, Fla., June 22-26), 435-437.Google Scholar
- VEMURI, B. C., MITICHE, h., AND AGGARWAL, J. K. 1987. 3-D object representation from range data using intrinsic surface properties. In Three-Dimensional Machine Vlsmn, T. Kanade, Ed. Kluwer Academic Publishers, Boston, Mass., 241-266.Google Scholar
- VEMURI, B. C., Mn'IcnE, A., AND AGGARWAI,, J. K. 1986. Curvature-based representation of objects from range data. J. Image Viston Comput. 4, 2 (May), 107-114. Google Scholar
- VOELCKER, H. B., REQUICHA, A. A. G., HARTQUiST, E., FISHER, W., HUNT, W., ARMSTRONG, G., CHECK, T., MOOTE, R., AND MCSWEENY, J. 1978. The PADL-1.0/2 system for defining and displaying solid objects. ACM Trans. Comput. Graph. 12, 3. Google Scholar
- VUYLSTEKE, P., AND OOSTERLINCK, A. 1990. Range image acquisition with a single binary-encoded light pattern. IEEE Trans. Patt. Anal. Machine Intell. 12, 2 (Feb.), 148-164. Google Scholar
- WE{NSTOCK, N. 1990. The Complete Dtrectory of Automated Destgn Software. Brady, New York.Google Scholar
- WILL, P. M., AND PENNINGTON, K. S. 1972. Grid coding: A novel technique for image processing. Proc. IEEE 60, 6 (June), 669-680.Google Scholar
- WILSON, P. 1~. 1987. A short history of CAD data transfer standards. IEEE Comput. Graph. Appl. (June), 64-67. Google Scholar
- YANG, H. S. 1988. Range image analysis wa quadtree and pyramid structure based on surface curvature. In Intelligent Robots and Computer VLsion: Seventh in Ser~es, D. P. Casasent, Ed. Proceedings of SPIE, vol. 1002, Cambridge, Mass., 597 608.Google Scholar
- YANG, H. S., AND KAK, A.C. 1986. Determination of the identity, position, and orientation of the top most object in a pile. Comput. Vision Graph. Image Process. 36, 229-255. Google Scholar
- YOKOYA, N., AND LEVINE, M.D. 1988. A hybrid approach to range image segmentation. In Proceedings of the 9th International Conference on Pattern Recognition (Rome, Italy, Nov. 14 17), 1-5.Google Scholar
Index Terms
- Model-based object recognition in dense-range images—a review
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Reviews
Andrew David Marshall
The current state of the art of an important issue in computer vision is reviewed. Indeed, for many vision systems, object recognition is the ultimate goal. All issues are discussed well, and most major recent results are documented. The paper would be a good starting point for any person wishing to get to know this subject. The references are excellent and many methods are discussed. A brief overview of the differential geometry of surfaces and various computational methods for the calculation of surface properties are also included. The paper discusses five areas of the subject: three-dimensional sensing, low-level processing, data representation, object modeling, and object recognition. The authors give a particularly good account of sensing methods, which have provided much impetus to research in this field with many technological advances. The sensing methods generally provide vast arrays of range data, so these “raw” data are not readily usable. Thus low-level processing is necessary in order to extract a better representation of the data. These subjects are surveyed well. Models of objects are important since they provide a priori information, such as the geometry and topology of the object, to the recognition system. Many representations are possible, and many of them are used in computer-aided design applications. Finally the most important stage, object recognition, is discussed. Here the representation extracted from the data is compared to the object model. Many methods for achieving this are assessed, and brief algorithmic detail is given in most instances. The paper is well written and provides a comprehensive review of the field. Most persons with an interest in the field should find something of use in this paper.
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