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Seeing In Depth, Vol. 1: Basic Mechanisms,

2003· article· en· W1985735875 on OpenAlexaboutno aff
Scott B. Stevenson

Bibliographic record

VenueOptometry and Vision Science · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOptics and Image Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBinocular rivalryDepth perceptionPerceptionBinocular visionSet (abstract data type)StereopsisVergence (optics)PsychologyComputer scienceCognitive scienceArtificial intelligenceVisual perception

Abstract

fetched live from OpenAlex

Seeing In Depth, Vol. 1: Basic Mechanisms, Ian P. Howard. Toronto: I. Porteous, 2002. Pages: 650. ISBN 0-9730873-0-7, and Seeing in Depth, Vol. 2: Depth Perception, Ian P. Howard and Brian J. Rogers. Toronto: I. Porteous, 2002. Pages: 625. ISBN 0-9730873-1-5. Price: $80.00/set. Who among us isn’t daunted by the explosion of scientific literature in our chosen field, and the challenge of seeing how it fits into a bigger picture? But who has time to read it all, make careful notes, and sort it all out? Lucky for the rest of us, Ian Howard and Brian Rogers have done just that for the field of depth perception and all its related topics. The result is a 1300 page book on binocular vision, stereopsis and depth perception called ‘Seeing in Depth.‘ This two volume set updates and greatly expands on the 1995 book by the same authors titled ‘Binocular Vision and Stereopsis.‘ It retains the focus and general organization of the previous book, but includes several new sections, including non-stereoscopic cues to depth. The book begins with historical background on the study of depth perception, one of its strongest sections. Volume 1 continues with general considerations on psychophysical methods, considerations of how information is coded by our senses, and an overview of visual physiology and binocular disparity processing. The binocular combination of images, including fusion, rivalry, and summation, are given two chapters. Vergence eye movements, along with related topics such as accommodation, are covered in a single chapter. The remainder of Volume 1 covers visual development and pathology, with a final chapter surveying depth detection across species. The first half of Volume 2 is devoted to the many issues surrounding stereoscopic depth perception, including binocular correspondence, the extraction of matching elements, and the many factors that determine how well a normal observer can make judgments of depth from disparity. This is followed by a broader treatment of the various binocular and monocular cues to depth and how they are combined to form an integrated percept. The book ends with a section on techniques and applications of stereoscopy. Although there is a chapter on binocular pathology and a short section describing strabismus types, there is relatively little in the book regarding clinical conditions involving binocular vision. This is a basic science book that describes the state of our knowledge of how normal binocular vision and depth perception operate. It is most useful as a reference to point the reader to the appropriate primary literature on any aspect of normal binocular vision, but it also includes a number of clearly written tutorials on how this science is done. Historical treatment of the research into depth perception is one of the great strengths of this book. It was fascinating to read this comprehensive and well-organized discussion of where our modern concepts originated. In particular, the authors have made a great effort throughout the book to give credit to the earliest writings on each aspect of binocular vision and depth perception. Like their 1995 book, ‘Seeing in Depth‘ has a remarkably comprehensive review of the binocular vision literature, with references to several thousand books and articles spanning from antiquity up to 2001. The authors provide a well-organized narrative to tie together all these papers and to put them into a useful context. Often this includes specific suggestions for experiments that have not been, but should be done. Although Howard and Rogers have obviously taken great care in staying true to the original articles, it is inevitable that some errors occur in the description of procedures and findings of this vast literature. Readers should of course use the usual caution when reading this secondary source and should consult the original papers to verify specifics. The two volumes include more than 800 figures, and a great many of them are reproduced or redrawn from original papers. This gives the reader direct access to the data and stimuli as presented by the original authors, in addition to Howard and Rogers’s description and analysis of the results. Not surprisingly, many of the figures in this book are stereograms. A section in the introduction describes how to achieve free fusion to view the divergent stereograms, but a set of plastic Prism glasses is included with the book for the divergence-challenged. The authors have included portraits and short biographies of 137 researchers, past and present, who have made a contribution in the study of depth perception (and agreed to provide a photo). Thus, in addition to everything else this book has to offer, this collection of photos provides a limited ‘who’s who‘ of researchers in this sub-field of vision science. Those who work in the area of binocular vision should consider adding this two volume set to their personal libraries, even if they already own the 1995 book. The historical section alone makes the book worth checking out. FIGUREFigure

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0050.006
Open science0.0020.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0400.018

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.335
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations45
Published2003
Admission routes1
Has abstractyes

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