Neural novelty filter for time-sequential imagery
Jarosław Szostakowski, Sławomir Skoneczny · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
Image sequences are very difficult to analyze because of their high dimensionality. The large amount of visual data is generated even in a typical situation. That is why the number of data should be limited for information processing. Often, most information in a frame is relatively slowly changing background and only small pieces of a frame are new or novel. Our purpose is to process a time sequence of images and to model objects and/or background from an image sequence in a compact form suitable for recognition and processing. This problem is similar to compression problems and it can be solved optimally by using a truncated Karhunen-Loeve (KL) expansion of the process. This paper describes a new efficient method for novelty filtering of time-sequential images. This method uses a neural approach for calculating a truncate Karhunen-Loeve expansion of the process. The algorithm employs the multilayer neural networks and it exploits the error back-propagation learning algorithm. A neural network implementation seems to be a very promising and effective tool for novelty filtering on image sequence. The validity and performance of the proposed neural network architecture and associated learning algorithm have been tested by extensive computer simulation.