Existence Detection of Objects in Images for Robot Vision Using Saliency Histogram Features
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Bibliographic record
Abstract
In robotics and computer vision, saliency maps are frequently used to identify regions that contain potential objects of interest and to restrict object detection to those regions only. However, common saliency approaches do not provide information as to whether there really is an interesting object triggering saliency and therefore tend to highlight needless background as potential regions of interest. This paper addresses the problem by exploiting histogram features extracted from saliency maps to predict the existence of interesting objects in images and to quickly prune uninteresting images. To validate our approach, we constructed a database that consists of 1000 background and object images captured in the working environment of our robot. Experimental results demonstrate that our approach achieves good detection performance and outperforms an existing existence detection approach [1].
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it