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Record W2048100210 · doi:10.1109/have.2012.6374433

Haptic rendering of photographs

2012· article· en· W2048100210 on OpenAlexafffund
David Lareau, Jochen Lang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHaptic technologyComputer scienceComputer visionZoomComputer graphics (images)WorkspaceArtificial intelligenceRendering (computer graphics)Panning (audio)Cursor (databases)Image-based modeling and renderingRobot

Abstract

fetched live from OpenAlex

Exploration of 2-D images is a challenging problem in computer haptics and research has primarily been focused on the exploration of restricted types of images, e.g., maps or mathematical curves. In contrast, this paper describes the design and implementation of a system for the haptic exploration of general photographs. We transform the photograph to a set of contours which are then rendered as three-dimensional grooves on a 2D image plane in the haptic workspace. Our haptic image explorer supports various user interactions including zooming and panning. Our major contribution is the support for browsing multiple levels-of-detail of the contour image in order to help users understand the image more readily. The levels-of-detail are derived from an object-level segmentation hierarchy of the source photograph. We also investigate if ambient encoding of the spatial surrounding of the haptic cursor in the image is beneficial during exploration. We briefly discuss a user study to evaluate the efficacy of multiple levels-of-detail and of ambient encoding of the spatial surrounding through textures.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.576
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.200
Teacher spread0.186 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations13
Published2012
Admission routes2
Has abstractyes

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