Research

Supervised genetic search for parameter selection in painterly rendering


Reference:

Collomosse, J. P., 2006. Supervised genetic search for parameter selection in painterly rendering. In: Applications of Evolutionary Computing, Proceedings. Vol. 3907. , pp. 599-610. (Lecture Notes in Computer Science)

Related documents:

This repository does not currently have the full-text of this item.
You may be able to access a copy if URLs are provided below.

Abstract

This paper investigates the feasibility of evolutionary search techniques as a mechanism for interactively exploring the design space of 2D painterly renderings. Although a growing body of painterly rendering literature exists, the large number of low-level configurable parameters that feature in contemporary algorithms can be counter-intuitive for nonexpert users to set. In this paper we first describe a multi-resolution painting algorithm capable of transforming photographs into paintings at interactive speeds. We then present a supervised evolutionary search process in which the user scores paintings on their aesthetics to guide the specification of their desired painterly rendering. Using our system, nonexpert users are able to produce their desired aesthetic in approximately 20 mouse clicks-around half an order of magnitude faster than manual specification of individual rendering parameters by trial and error.

Details

Item Type Book Sections
CreatorsCollomosse, J. P.
DepartmentsFaculty of Science > Computer Science
StatusPublished
ID Code5359
Additional InformationID number: ISI:000237228900057

Export

Actions (login required)

View Item