Morphological Background Detection and Enhancement of Images With Poor Lighting

Abstract

In this paper, some morphological transformations are used to detect the background in images characterized by poor lighting. Lately, contrast image enhancement has been carried out by the application of two operators based on the Weber's law notion. The first operator employs information from block analysis, while the second transformation utilizes the opening by reconstruction, which is employed to define the multibackground notion. The objective of contrast operators consists in normalizing the grey level of the input image with the purpose of avoiding abrupt changes in intensity among the different regions. Finally, the performance of the proposed operators is illustrated through the processing of images with different backgrounds, the majority of them with poor lighting conditions.

Contrast of a fuzzy relation

“…In defining a new contrast measure, we impose several axiomatic requirements based on our analysis of contrast expressions employed in the literature 0020-0255/$ -see front matter Ó 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.ins.2009.12.013 [1,21,22,30,33,37,45,49]. We also suggest that contrast measures satisfy two new properties in addition to those usually demanded, and examine the conditions under which these properties hold.…”

“…In the various fields employing this concept, ''contrast" is not uniquely defined (see [45]) and therefore has been studied from several points of view (see [19,20,33,44,38]). In image processing, for example, Vlachos and Sergiadis [53] state that ''the very concept of image contrast lacks a precise definition."…”

Contrast enhancement by object based Histogram Equalization

“…Here we propose a modified approach through image segmentation [12] by means of opening by reconstruction. After that object based histogram equalization is proposed.…”

Integrating Kinect Depth Data with a Stochastic Object Classification Framework for Forestry Robots

“…Another common technique, which is applicable for single images, uses morphological filters to classify a specific pattern as background (e.g. Poor lighting in parts of image) (Jimenez-Sanchez et al, 2009). This approach is challenging in forest environments where objects and backgrounds are very similar.…”