Data integration in multi-sensor based robotic workstations

A relaxation labeling algorithm is developed for image processing and object identification. The major advantage of this algorithm over the existing one is that the mathematic operation is simplified. The simplification eases the analysis of the convergence properties. Both the theoretical and application aspects of the proposed algorithm are investigated. The local convergence properties of a labeling process with n labels and m labels are established. The investigation of the interaction among the nodes in a multinode labeling process reveals some insight into the mathematical issues involved in the relaxation operations.<<ETX>>

[1]  Shmuel Peleg,et al.  A New Probabilistic Relaxation Scheme , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Olivier D. Faugeras,et al.  Improving Consistency and Reducing Ambiguity in Stochastic Labeling: An Optimization Approach , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Azriel Rosenfeld,et al.  Shape Segmentation Using Relaxation , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Azriel Rosenfeld,et al.  Relaxation: Evaluation and Applications , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Steven W. Zucker,et al.  On the Foundations of Relaxation Labeling Processes , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Azriel Rosenfeld,et al.  A Relaxation Method for Multispectral Pixel Classification , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Mandayam A. L. Thathachar,et al.  Relaxation Labeling with Learning Automata , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Dianne P. O'Leary,et al.  Analysis of relaxation processes: The two-node two-label case , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[9]  J. A. Richards,et al.  Pixel Labeling by Supervised Probabilistic Relaxation , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  David A. Landgrebe,et al.  Adaptive Relaxation Labeling , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Steven W. Zucker,et al.  A Gradient Projection Algorithm for Relaxation Methods , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Azriel Rosenfeld,et al.  Scene Labeling by Relaxation Operations , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[13]  Azriel Rosenfeld,et al.  Blob Detection by Relaxation , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Azriel Rosenfeld,et al.  Thresholding Using Relaxation , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Shmuel Peleg,et al.  Determining Compatibility Coefficients for Curve Enhancement Relaxation Processes , 1978 .

[16]  J. Y. S. Luh,et al.  Multi-sensor integration in intelligent robotic workstation , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[17]  Steven W. Zucker,et al.  Continuous Relaxation and Local Maxima Selection: Conditions for Equivalence , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Carl V. Page,et al.  Augmented Relaxation Labeling and Dynamic Relaxation Labeling , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  P. Swain,et al.  On the accuracy of pixel relaxation labeling , 1981 .

[20]  Shmuel Peleg,et al.  A Note on the Evaluation of Probabilistic Labelings , 1981 .

[22]  Steven W. Zucker,et al.  Radial Projection: An Efficient Update Rule for Relaxation Labeling , 1989, IEEE Trans. Pattern Anal. Mach. Intell..