Defining a target distinctness measure through a single-channel computational model of vision

Authors:
M. C. Aranda;J. Fdez-Valdivia;J. A. García
Affiliations:
Departamento de Lenguajes y Ciencias de la Computación, E.T.S. de Informática, Universidad de Málaga, Plaza El Ejido, s/n 29071 Málaga, Spain;Departamento de Ciencias de la Computación e I.A., E.T.S. de Ingeniería Informática, Universidad de Granada, 18071 Granada, Spain;Departamento de Ciencias de la Computación e I.A., E.T.S. de Ingeniería Informática, Universidad de Granada, 18071 Granada, Spain
Venue:
Pattern Recognition Letters
Year:
2003

Citing 4
Cited 0

A Computational Approach to Edge Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
The visible differences predictor: an algorithm for the assessment of image fidelity

Digital images and human vision
The RGFF Representational Model: A System for the Automatically Learned Partitioning of 'Visual Patterns' in Digital Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Models for Predictiong Visual Target Distinctness

Computational Models for Predictiong Visual Target Distinctness

Quantified Score

Hi-index	0.10

Visualization

Abstract

The main goal of this paper is to define a computational measure for quantifying the visual distinctness of targets in complex natural scenes, using a single-channel vision model. The basic features incorporated in the defined target distinctness measure are: (i) an adaptive contrast sensitivity function; (ii) an estimation of image contrast masking and (iii) an estimation of a set of interest points in the image. Several experiments are performed to determine the relative contribution of each of these factors to the measure, using the correlation with human performance data as the evaluation function.