Estimating 3D Egomotion from Perspective Image Sequence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Tracking Human Motion in Structured Environments Using a Distributed-Camera System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Journal of Cognitive Neuroscience
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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In this paper a probabilistic approach is considered to develop a methodology to solve the problem of estimation of the position of the observer. The base of this methodology is the appearance vision with which an environment map is constructed using Kernel PCA. For the experiments an image set is acquired in unknown locations in the same environment. The performance of Kernel PCA technique was tested according to the optimum dimension of the environment model and the quantity of images correctly classified using a Bayesian algorithm. To validate the results obtained with Kernel PCA the same experiments were performed with PCA and APEX techniques, then the results were compared showing that Kernel PCA has better performance than PCA and APEX.