Bibliographic record
Abstract
Haptic image exploration is intriguing because of the need to find an effective method to transfer image content from the visual to the haptic sensing modality. While in the past, the research community has been primarily focused on the exploration of restricted types of images, e.g., maps or mathematical curves, we address the challenging problem of haptic rendering of general photographs. We represent the photographs as 3-D grooves on a 2-D image plane as haptic contours. We discuss our interactive image segmentation tool to extract these contours from an image in an object-level hierarchy, i.e., the contours have a hierarchical relationship that represents how image objects consist of parts. Our haptic image exploration instrument allows a user interact with the image by zooming and panning but our major contribution is the support for browsing multiple levels-of-detail (LODs) of the contour image. We show that multiple LODs help a user to understand an image more readily. We also investigate if ambient encoding of the spatial surrounding of the haptic cursor in the image is beneficial during exploration. We present the results of a user study to evaluate the efficacy of multiple LODs and of ambient encoding of the spatial surrounding through textures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".