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Enregistrement W1985735875 · doi:10.1097/00006324-200309000-00004

Seeing In Depth, Vol. 1: Basic Mechanisms,

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

Notice bibliographique

RevueOptometry and Vision Science · 2003
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueOptics and Image Analysis
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBinocular rivalryDepth perceptionPerceptionBinocular visionSet (abstract data type)StereopsisVergence (optics)PsychologyComputer scienceCognitive scienceArtificial intelligenceVisual perception

Résumé

récupéré en direct d'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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,040
Score d'incertitude au seuil0,134

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,003
Communication savante0,0050,006
Science ouverte0,0020,002
Intégrité de la recherche0,0020,004
Charge utile insuffisante (le modèle a refusé de juger)0,0400,018

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,012
Tête enseignante GPT0,335
Écart entre enseignants0,323 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations45
Publié2003
Routes d'admission1
Résumé présentoui

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