Accession Number:

AD1050673

Title:

Improving Image Segmentation with Adaptive, Recurrent, Spiking Neural Network Models of the Primary Visual Cortex

Descriptive Note:

[Technical Report, Final Report]

Corporate Author:

Emory University

Personal Author(s):

Report Date:

2017-05-19

Pagination or Media Count:

5

Abstract:

. Automatic object recognition from still imagery, insensitive to clutter and partial occlusion, is an unsolved computer vision problem with countless applications to military readiness. Ambiguity of segmentation of complex images into objects is the major stumbling block. Incorporation of certain structural features of the primate early visual system into computational models has been suggested as a potential solution. However, little is known about effects of these features on segmentation performance of either humans or computational models

Subject Categories:

  • Cybernetics

Distribution Statement:

[A, Approved For Public Release]