Casamento de padrões e operadores morfológicos adaptativos

Abstract

Mathematical morphology is an approach applied in processing and image analysis problems that performs transformations in an object (image) by patterns of predefined forms. Such transformations are called morphological operators, with erosion and dilation being the elementary morphological operators. This work presents a machine learning mechanism applied for the automatic generation of templates, to be used by inexact template matching morphological operators. This model of operator is called Template Matching Adaptive Morphological Operator (OMACP), and combines the formalism of mathematical morphology through ELUTs (Elementary Look-Up Tables) with machine learning techniques. The ELUTs based template matching morphological operators already described in the literature allow inexact pattern recognition in digital images by defining a range around a reference pattern. This range has a constant value that is applied to all pixels of reference patterns, and its choice depends on parameters whose adjustments is usually performed based on empirical results, besides being highly sensitive to user idiosyncrasies. This work also proposes a mechanism, based on statistical parameters, which automates the choice of these range. Besides not consider it a constant value for all pixels of reference pattern. Such mechanism reduces the interference of a user to define the parameters of the morphological operator. To prove the effectiveness achieved with the inclusion of learning techniques and the choices mechanism of the range around the pattern reference, were performed comparative experiments between the proposed OMACP (with the inclusion of new features) with operators already studied without these features.