Coastal image interpretation using background knowledge and semantics

By Germain Forestier, Cédric Wemmert, A. Puissant. 2013

30 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Geography, Planning and Development, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. A.S. d'Avila Garcez
2. Luciano Serafini
3. Ivan Donadello
4. Gesina Schwalbe
5. Bettina Finzel

In Geography, Planning and Development

1. Luciano Serafini
2. Ivan Donadello
3. A.S. d'Avila Garcez

In Signal Processing

1. Luciano Serafini
2. Ivan Donadello
3. A.S. d'Avila Garcez

Citations in the corpus

• Logic Tensor Networks for Semantic Image Interpretation (2017, methodology): “In [Marszalek and Schmid, 2007; Forestier et al., 2013], a symbolic Knowledge-base is used to improve object detection, but only the subsumption relation between labels is explored, and it is not possible to inject complex knowledge expressed using logical axioms.”
• Integration of numeric and symbolic information for semantic image interpretation (2016, unknown): “Theapproachesof[25]and[13] improvedobjectdetectionbyexploitingtheseman-ticdependenciesbetweenobjecttypesencodedina knowledgebase.”