Object-independent structural image analysis: History and modern approaches

Lutsiv, V. R.; Malashin, R. O.

doi:10.1364/jot.81.000642

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2015

Publication Types

Select...

Article3

Other1

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

(1 citation statement)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4 This is currently one of the leading approaches in structural image analysis. 5 Generative models, however, are important in that they divide the specifics of the object into parts, separating them from the specifically computational (although also fundamental) problem of solving the reverse problems.…”

Section: Introductionmentioning

confidence: 99%

Generative and probability models in image processing and computer vision

Potapov

2015

J. Opt. Technol.

View full text Add to dashboard Cite

This paper gives an analysis of the role of generative models in image processing and computer vision. Oriented and unoriented graphical models (Bayesian and Markov networks) are considered, along with the possibilities of using them in image processing, in particular, to solve problems of noise filtering, segmentation, and stereo vision. Probability programming is considered as a method of specifying arbitrary generative models that possess substantially larger expressive force than graphical models. It is shown that the main limitation of probability programming is associated with the problem of the output efficiency on arbitrary generative models, for the solution of which it is necessary to develop methods of automatic specialization of general output procedures.

show abstract

Section: Introductionmentioning

confidence: 99%