Automatic Interpretation of Remotely Sensed Images for Urban Form Assessment

Mashford, John; Lipkin, Felix; Olie, Charlelie; Cuchennec, Mailys; Song, Yong

doi:10.1007/978-3-319-11758-4_48

Cited by 2 publications

(1 citation statement)

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“…Additionally, future work may involve incorporation of further concepts from the concurrent vision approach into an integrated vision system such as using information such as the NDVI (normalized difference vegetation index) and other features to inform region merging at different levels in a system for the interpretation of remotely sensed images for the detection of trees e.g. see [14].…”

Section: Some Results Of Testing the MCV Algorithmmentioning

confidence: 99%

A Neural Markovian Multiresolution Image Labeling Algorithm

Mashford,

Lane,

Ciesielski

et al. 2018

Preprint

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This paper describes the MCV image labeling algorithm which is a (semi-) hierarchical algorithm commencing with a partition made up of single pixel regions and merging regions or subsets of regions using a Markov random field (MRF) image model. It is an example of a general approach to computer vision called concurrent vision in which the operations of image segmentation and image classification are carried out concurrently. The output of the MCV algorithm can be a simple segmentation partition or a sequence of partitions which can provide useful information to higher level vision systems. In the case of an autoregressive Gaussian MRF the evaluation of sub-images for homogeneity is computationally inexpensive and may be effected by a hardwired feed-forward neural network. The merge operation of the algorithm is massively parallelizable. While being applicable to images (i.e. 2D signals), the algorithm is equally applicable to 1D signals (e.g. speech) or 3D signals (e.g. video sequences

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Section: Some Results Of Testing the MCV Algorithmmentioning

confidence: 99%