Category and depth discrimination in real-world scenes

Abstract

Visual understanding of real-world scenes is near-instantaneous. Humans can extract a wealth of information, including spatial structure, semantic category, and the identity of embedded objects, from images viewed for fewer than 100 msecs. Visual processing has capacity limits, and, as a result, the computational processes that underlie this behaviour must be highly efficient. Computational theories of realworld scene perception model early image processing in various ways. In Chapter 1, I review these theories, and in Chapter 2, I review the role of depth cues in rapid visual processing. This discussion reveals three problems: (i) Tests of the agreement between model predictions and human responses may be biased by the arbitrary choice of category system, (ii) Current models posit that scene semantics is estimated from spatial structure properties, but empirical support for this position is inconsistent, and (iii) The time-course of depth estimation in real-world scenes is poorly understood. To address these problems, three empirical papers are presented in Chapters 3, 4, and 5. In Chapter 3, I propose and validate a novel clustering algorithm that can be applied to image databases to derive category systems for visual experiments. In Chapters 3 and 4, I examine the relationship between spatial structure and semantic information, and find little support for the position that spatial
structure properties inform semantic discrimination. In Chapters 4 and 5, I
characterize the time-course of depth processing for images presented for <267
msecs, and conclude that binocular disparity and elevation cues contribute to realworld perception shortly after image onset (<50 msecs). These findings are discussed together in Chapter 6. This thesis contributes to the evaluation of modern models of real-world scene perception, and helps to characterize how visual understanding unfolds over time.

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Identifiers

ORCID for Erich Graf: orcid.org/0000-0002-3162-4233

ORCID for Wendy Adams: orcid.org/0000-0002-5832-1056

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