Registro completo de metadatos
| Campo DC | Valor | Lengua/Idioma |
|---|---|---|
| dc.creator | Baumgartner, Josef Sylvester | - |
| dc.creator | Flesia, Ana Georgina | - |
| dc.creator | Gimenez Romero, Javier Alejandro | - |
| dc.creator | Pucheta, Julián Antonio | - |
| dc.date | 2018-09-05T15:33:41Z | - |
| dc.date | 2018-09-05T15:33:41Z | - |
| dc.date | 2015-12 | - |
| dc.date | 2018-09-04T21:38:03Z | - |
| dc.date.accessioned | 2019-04-29T15:52:31Z | - |
| dc.date.available | 2019-04-29T15:52:31Z | - |
| dc.date.issued | 2015-12 | - |
| dc.identifier | Baumgartner, Josef Sylvester; Flesia, Ana Georgina; Gimenez Romero, Javier Alejandro; Pucheta, Julián Antonio; A new image segmentation framework based on two-dimensional hidden Markov models; IOS Press; Integrated Computer-aided Engineering; 23; 1; 12-2015; 1-13 | - |
| dc.identifier | 1069-2509 | - |
| dc.identifier | http://hdl.handle.net/11336/58360 | - |
| dc.identifier | CONICET Digital | - |
| dc.identifier | CONICET | - |
| dc.identifier.uri | http://rodna.bn.gov.ar:8080/jspui/handle/bnmm/304344 | - |
| dc.description | Image segmentation is one of the fundamental problems in computer vision. In this work, we present a new segmentation algorithm that is based on the theory of two-dimensional hidden Markov models (2D-HMM). Unlike most 2D-HMM approaches we do not apply the Viterbi algorithm; instead we present a computationally efficient algorithm that propagates the state probabilities through the image. Our algorithm, called Complete Enumeration Iteration (CEP), is flexible in the sense that it allows the use of different probability distributions as emibion probabilities. Not only do we compare the performance of different probability functions plugged into our framework but also propose three methods to update the distributions of each state "online" during the segmentation proceb. We compare our algorithm with a 2D-HMM standard algorithm and Iterated Conditional Modes (ICM) using real world images like a radiography or a satellite image as well as synthetic images. The experimental results are evaluated by the kappa coefficient (κ). In those cases where the average κ coefficient is higher than 0.7 we observe an average relative improvement of 8% of CEP with respect to the benchmark algorithms. For all other segmentation tasks CEP shows no significant improvement. Besides that, we demonstrate how the choice of the emibion probability can have great influence on the segmentation results. Surprisingly, we observe that the normal distribution is an appropriate density function for many segmentation tasks. | - |
| dc.description | Fil: Baumgartner, Josef Sylvester. Universidad Nacional de Córdoba. Facultad de Cs.exactas Físicas y Naturales. Departamento de Electronica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina | - |
| dc.description | Fil: Flesia, Ana Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; Argentina | - |
| dc.description | Fil: Gimenez Romero, Javier Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina | - |
| dc.description | Fil: Pucheta, Julián Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Cs.exactas Físicas y Naturales. Departamento de Electronica; Argentina | - |
| dc.format | application/pdf | - |
| dc.format | application/pdf | - |
| dc.language | eng | - |
| dc.publisher | IOS Press | - |
| dc.relation | info:eu-repo/semantics/altIdentifier/url/https://content.iospress.com/articles/integrated-computer-aided-engineering/ica497 | - |
| dc.relation | info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.3233/ICA-150497 | - |
| dc.rights | info:eu-repo/semantics/restrictedAccess | - |
| dc.rights | https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ | - |
| dc.source | reponame:CONICET Digital (CONICET) | - |
| dc.source | instname:Consejo Nacional de Investigaciones Científicas y Técnicas | - |
| dc.source | instacron:CONICET | - |
| dc.subject | HIDDEN MARKOV MODELS | - |
| dc.subject | IMAGE SEGMENTATION | - |
| dc.subject | KAPPA COEFFICIENT | - |
| dc.subject | PROBABILITY DENSITY FUNCTION | - |
| dc.subject | VITERBI TRAINING | - |
| dc.subject | Matemática Pura | - |
| dc.subject | Matemáticas | - |
| dc.subject | CIENCIAS NATURALES Y EXACTAS | - |
| dc.title | A new image segmentation framework based on two-dimensional hidden Markov models | - |
| dc.type | info:eu-repo/semantics/article | - |
| dc.type | info:eu-repo/semantics/publishedVersion | - |
| dc.type | info:ar-repo/semantics/articulo | - |
| Aparece en las colecciones: | CONICET | |
Ficheros en este ítem:
No hay ficheros asociados a este ítem.