This paper presents an efficient pipeline architecture to perform gray-scale morphologic operations. The features of the architecture are 1) lower hardware cost, 2) faster operation time in processing an image. 3) lower data access times from the image memory, 4) shorter latency, 5) suitability for VLSI implementation, and 6 ) adaptability for N*N morphologic operations.

In this paper, A dynamic handwritten Chinese signature verflcation system based upon a Bayesian neural network is presented. Due to a great deal of variability of handwritten Chinese signatures, the proposed Bayesian neural network is trained by an incrementaZ Zearning vector quantization (ILVQ) algorithm, which endows this system with incremental learning ability, and outputs a posteriori probability to give a more reliable distance estimation. The performance analysis was based upon a set of signature data consisting of 800 true specimens, 200 "simple " forgeries and 200 "skilled" forgeries. The experimental results show the type I error is about 2% and the type II error rates are about 0.1% and 2.5% for %nple" and "skilled" forgeries respectively.

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