CMOS image sensor camera with focal plane edge detection
Bibliographic record
Abstract
We present a simple, yet robust, VLSI implementation of sampled-method edge detection. Our technique adopts the well-known correlated double sampling (CDS), usually used for fixed pattern noise (FPN) reduction, to perform a sampled differentiation of the captured image to detect visual edges. This circuit is usually an integral part of most CMOS image sensors; therefore no additional area is required to include the proposed edge detection functionality in the image sensor. The imager array was implemented using active pixel sensor (APS) technology with dual mode of operation: a logarithmic (continuous) mode with wide optical dynamic range and a linear (integrating) mode with higher image quality. The real-time edge detection was demonstrated in the both modes of operation. This technique can be easily extended to perform temporal differentiation, providing a simple method for motion detection. The prototype chip was fabricated using standard 0.5 /spl mu/m CMOS process with an array of 64/spl times/64 pixels and pixel size of 30/spl times/30 /spl mu/m. The fill factor is /spl sim/60% and the system working voltage is 3.3 V. Results indicate that the proposed architecture is suitable for applications such as security, and industrial inspection, where integrated functionalities are advantageous.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".