Partition-distance methods for assessing spatial segmentations of images and videos

• Tipo: Artigo em Revista Científica Internacional
• Data: 2009

Autores

Jaime S Cardoso (Autor) FEUP; Pedro Carvalho (Autor) FEUP; Luis F Teixeira (Autor) FEUP; Luis Corte Real (Autor) FEUP

Revista

Título: Computer Vision and Image Understanding

Vol. 113 Nº 7

Páginas: 811-823

ISSN: 1077-3142

Editora: Elsevier

Indexação

ISI Web of Knowledge - 6 Citações

ISI Web of Science

Scopus - 6 Citações

COMPENDEX

INSPEC

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

CORDIS: Ciências Tecnológicas > Tecnologia > Tecnologia de computadores > Processamento de imagem

Outras Informações

ID Authenticus: P-003-HPD

DOI: 10.1016/j.cviu.2009.02.001

Abstract (EN): The primary goal of the research on image segmentation is to produce better segmentation algorithms. In spite of almost 50 years of research and development in this Held, the general problem of splitting in image into meaningful regions remains unsolved. New and emerging techniques are constantly being applied with reduced Success. The design of each of these new segmentation algorithms requires spending careful attention judging the effectiveness of the technique. This paper demonstrates how the proposed methodology is well suited to perform a quantitative comparison between image segmentation algorithms using I ground-truth segmentation. It consists of a general framework already partially proposed in the literature, but dispersed over several works. The framework is based on the principle of eliminating the minimum number of elements Such that a specified condition is met. This rule translates directly into a global optimization procedure and the intersection-graph between two partitions emerges as the natural tool to solve it. The objective of this paper is to summarize, aggregate and extend the dispersed work. The principle is clarified, presented striped of unnecessary supports and extended to sequences of images. Our Study shows that the proposed framework for segmentation performance evaluation is simple, general and mathematically sound.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 13