Image labelling in real conditions

Abstract

PurposeAccording to the problems of visual perception, we propose a model for the processing of vision in adverse situations of illumination, scale, etc. In this paper, a model for image segmentation and labelling obtained in real conditions with different scales is proposed.Design/methodology/approachThe model is based on the texture identification of the scene's objects by means of comparison with a database that stores series of each texture perceived with successive optic parameter values. As a basis for the model, self‐organising maps have been used in several phases of the labelling process.FindingsThe model has been conceived to systematically deal with the different causes that make vision difficult and allows it to be applied in a wide range of real situations. The results show high success rates in the labelling of scenes captured in different scale conditions, using very simple describers, such as different histograms of textures.Research limitations/implicationsOur interest is directed towards systematising the proposal and experimenting on the influence of the other variables of the vision. We will also tackle the implantation of the classifier module so that the different causes can be dealt with by the reconfiguration of the same hardware (using reconfigurable hardware).Originality/valueThis research approaches a very advanced angle of the vision problems: visual perception under adverse conditions. In order to deal with this problem, a model formulated with a general purpose is proposed. Our objective is to present an approach to conceive universal architectures (in the sense of being valid with independence of the implied magnitudes).