Range image segmentation by dynamic neural network architecture

Pattern Recognition

Volumn 29, Issue 2, 1996, Pages 315-329

  Chandrasekaran, Venkatachalam ^a   Palaniswami, Marimuthu ^a   Caelli, Terry M ^b  

^a UNIVERSITY OF MELBOURNE   (Australia)

^b CURTIN UNIVERSITY   (Australia)

Author keywords

Crease edge detection; Dynamic neural network architecture; Edge contours; Edge maps; Edge operators; Fractional differencing; Range image segmentation

Indexed keywords

CREASE EDGE DETECTION; DYNAMIC NEURAL NETWORK ARCHITECTURE; FRACTIONAL DIFFERENCING; RANGE IMAGE SEGMENTATION;

COMPUTATIONAL METHODS; DIFFERENTIATION (CALCULUS); EDGE DETECTION; NEURAL NETWORKS; OBJECT RECOGNITION;

IMAGE SEGMENTATION;

EID: 0030085297     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/0031-3203(95)00038-0     Document Type: Article

References (18)

1. R. Hoffman and A. K. Jain, Segmentation and classification of range images, IEEE Trans. Pattern Anal. Mach. Intell. PAMI-9(5), 608-620 (September 1987).
2. P. J. Besl and R. C. Jain, Intrinsic and extrinsic surface characteristics, in Proc. IEEE Comput. Vis. Pattern Recognition pp. 226-233. San Francisco, California, USA, June 9-13 (1985).
3. P. J. Besl and R. C. Jain, Segmentation through symbolic surface descriptions, in Proc. IEEE Comput. Vis. Pattern Recognition, pp. 77-85. Miami Beach, Florida, USA, June 22-26 (1986).
4. P. J. Besl and R. C. Jain, Invariant surface characteristics for 3D object recognition in range images, Comput. Vis. Graphics Image Process. 33, 33-80 (1986).
5. P. J. Besl and R. C. Jain, Segmentation through variable-order surface fitting, IEEE Trans. Pattern Anal. Mach. Intell. 10(2), 167-192 (March 1988).
6. O. D. Faugerass and E. Pauchon, Measuring the shape of 3D objects, in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognition, pp. 1-7. Washington, D.C., June 19-23 (1983).
7. O. D. Faugerass, M. Hebert and E. Pauchon, Segmentation of range data into planar and quadratic patches, in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognition, pp. 8-13. Washington, D.C. June 19-23 (1983).
8. T. J. Fan, Describing and Recognizing 3D Objects Using Surface Properties. Springer-Verlag, Berlin (1990).
9. J. Berkmann and T. M. Caelli, Computation of surface geometry and segmentation using covariance techniques, IEEE Trans. PAMI, 16, 1114-1116 (1994).
10. S. K. Hungenahally, A. Postula and L. C. Jain, Neuromorphology of biological vision: fractional discriminant functions in the emulation of visual receptive fields for remote sensed images, in Proc. Fourth Australian Conf. Neural Networks (ACNN93), 146-149 (1993).
11. M. N. Oguztoreli, G. M. Steil and T. M. Caelli, Underlying neural computations for some visual phenomena, Biolog. Cybern. 60, 89-106 (1988).
12. D. J. Defatta, J. G. Lucas and W. S. Hodgkiss, Digital Signal Processing: A System Design Approach. John Wiley & Sons, USA (1988).
13. S. Venkatesh and L. J. Kitchen, Edge evaluation using necessary components, CVGIP: Graphical Models Image Process. 54(1), 23-30 (1992).
14. T. M. Caelli and A. Pennigton, An improved rule generation method for evidence based classification systems. Pattern Recognition, 26(5), 733-740 (1993).
15. T. M. Caelli and A. Drier, Variations on the evidence based object recognition theme, Pattern Recognition, 27(2), 185-204 (1994).
16. A. K. Jain and R. Hoffman, Evidence based recognition of objects, IEEE Trans. Pattern Anal. Mach. Intell. 10(6), 783-802, (November 1988).
17. J. Koh, M. Suk and S. M. Bhandarkar, A multi-layer kohonen's self-organizing feature map for range image segmentation, in Proc. IEEE Int. Conf. Neural Networks, pp. 1270-1276. San Francisco, California, March 28-April 1 (1993).
18. V. Chandrasekaran, M. Palaniswami and T. M. Caelli, Temporal feature extraction by sparse decomposition of range images using gated neurons, Proc. IEEE World Congress Comput. Intell. (Neural Networks), Orlando, Florida, USA, June 26-July 2 (1994).