Qualitative landmark recognition using visual cues

Pattern Recognition Letters

Volumn 18, Issue 11-13, 1997, Pages 1405-1414

  Srinivasan, Sridhar ^a   Kanal, Laveen ^a  

^a LNK Corporation Inc   (United States)

Author keywords

Automatic nagivation; Feature extraction; Feature grouping; Landmark recognition; Object modeling; Raster to vector; Region of interest; Texture classification

Indexed keywords

COMPUTER SIMULATION; EDGE DETECTION; FEATURE EXTRACTION; HEURISTIC METHODS; IMAGE UNDERSTANDING; NAVIGATION; NEURAL NETWORKS;

AUTOMATIC NAVIGATION; LANDMARK RECOGNITION; TEXTURE CLASSIFICATION;

PATTERN RECOGNITION;

EID: 0031277146     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-8655(97)00142-6     Document Type: Article

References (15)

1. Blake, A., Zisserman, A., 1987.Visual Reconstruction. MIT Press, Cambridge, MA.
2. Canny, J., 1986. A computational approach to edge detection. IEEE Trans. Pattern Recognition 8 (6).
3. Daugman, J., 1989. Networks for image analysis: Motion and texture. In: Proc. IJCNN, pp. I-189-193.
4. Dickmanns, E.D., 1995. Performance improvements for autonomous road vehicles. In: Proc. Internat. Conf. on Intelligent Autonomous Systems, Karlsruhe, March 1995.
5. Dickmanns, E.D., Müller, N., 1995. Scene recognition and landmark navigation for road vehicles. In: Proc. 2nd IFAC Conf. on Intelligent Autonomous Vehicles, Otaniemi.
6. Kanal, L., Raghavan, S., 1992. Hybrid systems - A key to intelligent pattern recognition. In: Proc. IJCNN.
7. Kisner, J., Johnson, J., 1996. Stabilized input with a feedback pulse-coupled neural network. Optical Engineering 35 (8), pp. 2158-2161.
8. Marr, D., Poggio T., 1976. A Cooperative Stereo Algorithm. Science, 1976.
9. Manjunath, B.S., Chellappa, R., 1991. A unified approach to boundary perception: Edges, textures and illusory contours. USC-SIPI Report No. 167, 1991.
10. Raghavan S., et al., 1991. A layered-object recognition system using a hierarchical hybrid neural network architecture. In: Proc. SPIE Conf. on Advances in Robotics Vision, Cambridge, MA.
11. Raghavan, S., Gupta, N., Kanal, L., 1992. Computing discontinuity-preserved optical flow. In: Proc. 11th IAPR Conf., The Netherlands.
12. Ramachandran, K., Vetterli, M., 1993. Best wavelet packet bases in a rate-distortion sense.IEEE Trans. Image Processing 2 (2), 160-174.
13. Ranganath, H., Kuntimad, G., Johnson, J., 1995. Pulse coupled neural networks for image processing. IEEE Southeast Conference, Raleigh, NC.
14. Schiller, P., 1986. The central visual system.Vision Research 26 (9), 1351-1386.
15. Vetterli, M., Herley, C., 1992. Wavelets and filter banks: Theory and design. IEEE Trans. Signal Processing 40 (9), pp. 2207-2232.