Object Segmentation Using Background Modelling and Cascaded Change Detection

dc.contributor.author Luís F. Teixeira en
dc.contributor.author Luís Corte Real en
dc.contributor.author Jaime Cardoso en
dc.date.accessioned 2017-11-16T12:27:09Z
dc.date.available 2017-11-16T12:27:09Z
dc.date.issued 2007 en
dc.description.abstract The automatic extraction and analysis of visual information is becoming generalised. The first step in this processing chain is usually separating or segmenting the captured visual scene in individual objects. Obtaining a perceptually correct segmentation is however a cumbersome task. Moreover, typical applications relying on object segmentation, such as visual surveillance, introduce two additional requirements: (1) it should represent only a small fraction of the total amount of processing time and (2) realtime overall processing. We propose a technique that tackles these problems using a cascade of change detection tests, including noise-induced, illumination variation and structural changes. An objective comparison of common pixelwise modelling methods is first done. A cost-based partition distance between segmentation masks is introduced and used to evaluate the methods. Both the mixture of Gaussians and the kernel density estimation are used as a base to detect structural changes in the proposed algorithm. Experimental results show that the cascade technique consistently outperforms the base methods, without additional post-processing and without additional processing overheads. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/1505
dc.language eng en
dc.relation 243 en
dc.relation 3889 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Object Segmentation Using Background Modelling and Cascaded Change Detection en
dc.type article en
dc.type Publication en