Artificial neural networks simulating visual texture segmentation and target detection in line-element images

Andrew John Schofield, David H. Foster

Research output: Contribution to journalArticle

Abstract

Measurements were made of human observers’ performance both in segmenting regions of line-elements and in detecting line-element targets in stimuli containing several orientations. Performance was modelled by four artificial neural networks constructed from processing units trained to mimic the gross functionality of certain loosely defined classes of cortical cells. Model 1 contained modules sensitive to absolute orientation only, and it provided a poor fit to the human-performance data. Model 2 contained modules sensitive to orientation contrast: the outputs of these modules could be suppressed with fields of uniformly oriented line-elements. Model 3 contained orientation-contrast-sensitive modules of a different type: their outputs could be suppressed with fields of randomly oriented line-elements. Models 2 and 3 both successfully processed line-element arrays with orientation heterogeneities, but these models still provided inadequate fits to the human-performance data. Model 4 contained both types of orientation contrast-sensitive modules; this model was able to account for human performance in the segmentation and detection tasks, both qualitatively and quantitatively.
Original languageEnglish
Pages (from-to)401-412
JournalPilosophical Transactions B
Volume350
DOIs
Publication statusPublished - 1995

Fingerprint Dive into the research topics of 'Artificial neural networks simulating visual texture segmentation and target detection in line-element images'. Together they form a unique fingerprint.

  • Cite this