Scaling up document-image classifiers to handle an unlimited variety of document and image types poses serious challenges to conventional trainable classifier technologies. Highly...
This paper proposes two new methods for optimizing objectives and constraints. The GP approach is very general and hardware resources in finite wordlength implementation of it allo...
In this case study, various ways to partition a code between the microprocessor and FPGA are examined. Discrete image convolution operation with separable kernel is used as the ca...
This paper considers a method that combines ideas from Bayesian learning, Bayesian network inference, and classical hypothesis testing to produce a more reliable and robust test o...
—Over the past few decades, a large family of algorithms—supervised or unsupervised; stemming from statistics or geometry theory—has been designed to provide different soluti...