Object Recognition From Haptic Glance at Visually Salient Locations
Bibliographic record
Abstract
Reproducing the visuo-haptic interaction demonstrated by humans to sense and interpret visual and tactile stimuli is the main motivation of this research on tactile object recognition. An enhanced model of visual attention, consisting of information about color, contrast, curvature, entropy, symmetry, intensity, and orientation is developed to assist in selection of only relevant regions to be probed over the surface of an object. We aim to demonstrate that this visual information can be successfully employed to recognize the object from local tactile data, following the idea of haptic glance exhibited by humans (i.e., object recognition by a limited number of static contacts between the object and a finger). Due to the time-consuming nature of real tactile data acquisition, the process is first simulated and tested over a set of virtual objects prior to testing it on real rigid objects. A series of classifiers is trained and tested for the object recognition task and their performance is compared in terms of accuracy. The k-nearest neighbor classifier is shown to be the most promising algorithm among the tested classifiers for the object recognition task, both for simulated data (85.42% for four objects and 76.37% for six objects) and for real tactile sensor data (66.76% for four objects and 58.97% for six objects) when single imprints are used as a basis for recognition. Employing multiple imprints for object recognition yields a 100% accuracy for support vector machines (SVM) and k-nearest neighbors (kNN) classifiers when at least four imprints are considered.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".