Realization Law of Pragnanz and Closure of gestalt theory using Active Learning Method

This paper presents a new algorithm for predicting missing parts of images. We investigate two laws of one of the well-known theories about visual system called Gestalt theories. These theories tried to define how a biological neural network can perform. Active Learning Method (ALM) is used as an artificial intelligent approach to realize law of Pragnanz and Closure principle of Gestalt theory. ALM is a pattern-based algorithm for soft computing which uses the Ink Drop Spread (IDS) algorithm. The present framework learns the patterns in an unsupervised manner or alongside any supervised task according to symmetry and pattern of rest of shape without considering unnecessary details. This method inspired by Morphology and used ALM as an iterative process. At each iteration the proposed algorithm finds Center of Gravity (COG) and the accuracy requirement can be achieved based on distances between centers to predict removed segment of image.

Realization Law of Pragnanz and Closure of gestalt theory using Active Learning Method Keywords:

Active Learning Method (ALM) , Pragnanz , Closure , Gestalt , Morphology , Ink Drop Spread (IDS)