Visual Appearances Of The Metric Shapes Of Three Dimensional Objects
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Visual Appearances of the Metric Shapes of Three-dimensional Objects
The current research program seeks to explain a phenomenal visual experience. Namely, appearances of the shapes of three-dimensional (3D) rigid objects remain to be rigid when we walk around and view them from different angles and distances. This is a hard problem to solve given the ambiguities arising from the optical projection and constantly changing retinal images as we navigate. Two hypotheses were proposed. Hypothesis 1 explains this phenomenon by arguing that the visual system can reconstruct the 3D shape veridically. Alternatively, Hypothesis 2 argues that even though the reconstructed 3D shape is distorted with viewpoint, the resulting nonrigidity in the 3D shape percepts is not detected by the visual system under ordinary circumstances. Eight psychophysical experiments were conducted to test the two hypotheses by investigating the perception of 3D metric shape of well-structured polyhedral objects from binocular stereopsis. In Experiment 1 to 7, participants adjusted the 3D shape of an adjustable object to match the perceived 3D shape of a reference object under a variety of conditions. In Experiment 8, participants discriminated a nonrigid polyhedral object from a rigid one in an immersive virtual reality environment. Results of the eight experiments reported in this thesis reject Hypothesis 1 and support Hypothesis 2. Thus, the phenomenal rigid appearance of rigidly moving objects does not arise from the veridical perception of 3D shape. Rather, the 3D metric shape percepts vary systematically with viewing distance (Experiment 1, 4, 7), object size (Experiment 2), in-plane orientation (Experiment 3), different types of optical projection (Experiment 4, 5, 6), and scene context (Experiment 7). And testing with more symmetric objects or in a more naturalist scene context cannot make the perception more accurate (Experiment 7). However, a comparison between participants' performance in Experiment~8 with their performance in Experiment 1, 4, or 7 suggests that the amount of difference in 3D metric shape that is detectable when comparing two stationary objects synchronically becomes undetectable when it manifests in a single object diachronically during motion. In conclusion, the current research program shows that it is possible to have a change in the appearance of the shape of a 3D object without having any appearance of change of shape of this object.
Shape Perception in Human and Computer Vision
Author: Sven J. Dickinson
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-06-29
This comprehensive and authoritative text/reference presents a unique, multidisciplinary perspective on Shape Perception in Human and Computer Vision. Rather than focusing purely on the state of the art, the book provides viewpoints from world-class researchers reflecting broadly on the issues that have shaped the field. Drawing upon many years of experience, each contributor discusses the trends followed and the progress made, in addition to identifying the major challenges that still lie ahead. Topics and features: examines each topic from a range of viewpoints, rather than promoting a specific paradigm; discusses topics on contours, shape hierarchies, shape grammars, shape priors, and 3D shape inference; reviews issues relating to surfaces, invariants, parts, multiple views, learning, simplicity, shape constancy and shape illusions; addresses concepts from the historically separate disciplines of computer vision and human vision using the same “language” and methods.
Computer Vision Metrics
Computer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. This book provides necessary background to develop intuition about why interest point detectors and feature descriptors actually work, how they are designed, with observations about tuning the methods for achieving robustness and invariance targets for specific applications. The survey is broader than it is deep, with over 540 references provided to dig deeper. The taxonomy includes search methods, spectra components, descriptor representation, shape, distance functions, accuracy, efficiency, robustness and invariance attributes, and more. Rather than providing ‘how-to’ source code examples and shortcuts, this book provides a counterpoint discussion to the many fine opencv community source code resources available for hands-on practitioners.