@Article{BarreraDougTomi:1997:AuPrBi,
               author = "Barrera, Junior and Dougherty, Edward R. and Tomita, Nina Sumiko",
                title = "Automatic programming of binary morphological machines by design 
                         of statistically optimal operators in the context of computational 
                         learning theory",
              journal = "Journal of Eletronic Imaging",
                 year = "1997",
               volume = "6",
               number = "1",
                pages = "54--67",
                month = "January",
                 note = "{}",
             keywords = "computational learning theory, mathematical morphology, bynary 
                         morphological machines, W-operators, optimal operator design.",
             abstract = "Representation of set operators by artificial neural networks and 
                         design of such operators by inference of network parameters is a 
                         popular technique in binary image analysis. We propose an 
                         alternative to this technique: automatic programming of 
                         morphological machines (MMachs) by the design of statiscally 
                         optimal operators. We propose a formulation of the procedure for 
                         designing set operators that extends the one stated by Dougherty 
                         for binary image restoration, show the relation of this new 
                         formulation with the one stated by Haussler for learning Boolean 
                         concepts in the context of machine learning theory (which usually 
                         is applied to neural networks), present a new learning algorithm 
                         for Boolean concepts represented as MMachs programs, and give some 
                         application examples in binary image analysis.",
           copyholder = "faria - tese",
        urlaccessdate = "03 maio 2024"
}