Name
Abstract | ||
|---|---|---|
The term space-variant vision was introduced in the late 1980s to refer to sensor architectures based on a smooth variation of resolution across the workspace, like that of the human visual system. The use of such sensor architectures is rapidly becoming an important factor in machine vision in which the constraints of size weight, cost and performance must be jointly optimized. The structure of this paper consists of four parts. A review of the four generic architectures for vision will be presented, providing a context for the term “ active vision”, and a justification for the importance, and the connection between, space-variant architectures and active vision methods. A brief quantitative review of the specific space-variant properties of primate visual cortex topography will be provided, in the cortex of sensor design. The engineering and algorithmic problems that are associated with exploiting space-variant systems will be stated. Examples of several recently constructed miniature space-variant active vision systems will be briefly reviewed, along with a brief discussion of solutions to the basic problem areas in space-variant vision. |
Year | DOI | Venue |
|---|---|---|
1995 | 10.1016/0893-6080(95)00092-5 | Neural Networks |
Keywords | Field | DocType |
computer vision,space-variant active vision,space-variant,visual cortex,pyramid,active vision,fovea,human visual system,machine vision | Active vision,Machine vision,Human visual system model,Computer science,Workspace,Human–computer interaction,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
8 | 7-8 | Neural Networks |
Citations | PageRank | References |
38 | 3.38 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| E L Schwartz | 1 | 563 | 78.66 |
| Douglas N. Greve | 2 | 777 | 49.66 |
| Giorgio Bonmassar | 3 | 159 | 33.51 |