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Kalman Filtering Neural Networks - Haykin S.

Haykin S. Kalman Filtering Neural Networks - Wiley publishing , 2001. - 202 p.
ISBNs: 0-471-36998-5
Download (direct link): kalmanfilteringneuralnetworks2001.pdf
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by the dorsal ‘‘where’’ pathway. Anatomically, however, there are crossconnections between the two pathways at several points [6]. Furthermore, there is ample behavioral evidence that the processes of shape and motion perception are not completely separate. For example, it has long been established that we are able to infer shape from motion (see e.g., [7]). Conversely, under certain conditions, object recognition can be shown to drive motion perception [8]. In addition, Stone [9] has shown that viewers are much better at recognizing objects when they are moving in characteristic, familiar trajectories as compared with unfamiliar trajectories. These data suggest that when shape and motion are tightly correlated, viewers will learn to use them together to recognize objects. This is exactly what happens in our model.
To accomplish temporal processing in our model, we have incorporated within-layer recurrent connections in the architecture used here. Another possibility would be to incorporate top-down recurrent connections. A key anatomical feature of the visual system is top-down feedback between visual areas [3]. Top-down connections could allow global expectations about the three-dimensional shape of a moving object to guide predictions. Thus, an important direction for future work is to extend the model to allow top-down feedback. Rao and Ballard [10] have proposed an alternative neural network implementation of the EKF that employs top-down feedback between layers, and have applied their model to both static images and time-varying image sequences [10, 11]. Other models of cortical feedback for modeling the generation of expectations have also been proposed (see, e.g., [12, 13]).
Natural visual systems can deal with an enormous space of possible images, under widely varying viewing conditions. Another important direction for future work is to extend our model to deal with more realistic images. Many additional complexities arise in natural images that were not present in the artificial image sequences used here. For example, the simultaneous presence of both foreground and background objects may hinder the prediction accuracy. Natural visual systems likely use atten-tional filtering and binding strategies to alleviate this problem; for example, Moran and Desimone [14] have observed cells that show a suppressed neural response to a preferred stimulus if unattended and in the presence of an attended stimulus. Another simplification of our images is that shape remained constant for many time frames, whereas for real threedimensional objects, the shape projected onto a two-dimensional image may change dramatically over time, because of rotations as well as nonrigid motions (e.g. bending). Humans are able to infer three-dimensional shape from non-rigid motion, even from highly impoverished stimuli such
as moving light displays [7]. It is likely that the architecture described here
could handle changes in shape, provided shape changes predictably and
gradually over time.
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