Nonclassical receptive field inhibition applied to image segmentation

Title:

Nonclassical receptive field inhibition applied to image segmentation

Creator:

Fernandes, Bruno J. T., Cavalcanti, George D. C., and Ren , Tsang I.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:70dbd4f4-525d-4779-b0ea-d7c53afc8e4e
uuid:70dbd4f4-525d-4779-b0ea-d7c53afc8e4e

Subject:

Pattern recognition, image segmentation, and neural network

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

This paper presents a new model to perform a supervised image segmentation task. The proposed model is called segmentation and classification with receptive fields (SCRF) which is based on the concept of receptive fields that analyzes pieces of an image considering not only a pixel or a group of pixels, but also the relationship between them and their neighbors. In order to work with the SCRF model, we propose a new artificial neural network, called I-PyraNet, which is a hybrid implementation of the recently described PyraNet and the nonclassical receptive fields inhibition. Furthermore, the model and the neural network are combined to accomplish a satellite image segmentation task.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2009 Volume:19 | Number:1

Harvested from:

CDK

Metadata only:

false