Calibration of a saccadic camera system to adapt to lens distortions
Bibliographic record
Abstract
The human vision system (HVS) is remarkably robust against eye distortions. Through a combination of eye movements and visual feedback, the HVS can often appropriately interpret scene information acquired from flawed optics. Inspired by biological systems, we have built an electronically and mechanically reconfigurable "saccadic" camera system. The saccadic camera is designed to efficiently examine scenes through foveated imaging, where scrutiny is reserved for salient regions of interest. The system's "eye" is an electronic image sensor used in multiple modes of resolution. We use a subwindow set at high resolution as the system's fovea, and capture the remaining visual field at a lower resolution. The ability to program the subwindow's size and position provides an analog to biological eye movements. Similarly, we can program the system's mechanical components to provide the "neck's" locomotion for modified perspectives. In this work, we use the saccadic camera to develop a "work-around" routine in response to possible degradations in the camera's lens. This is particularly useful in situations where the camera's optics are exposed to harsh conditions, and cannot be easily repaired or replaced. By exploiting our knowledge of the image sensor's electronic coordinates relative to the camera's mechanical movement, the system is able to develop an empirical distortion model of the image formation process. This allows the saccadic camera to dynamically adapt to changes in its image quality.
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 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 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".