Effectiveness of video object segmentation based on MPEG like motion vectors for 3D depth estimation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper discusses the effectiveness of video image segmentation based upon macro blocks associated with the motion vectors defined and embedded in MPEG codec, for the purpose of estimating the 3D structure of a moving object in the video scene. Sequential video frames do not necessarily meet the rigidity assumed in the 3D estimation/reconstruction techniques for stereoscopic pair images of epipolar geometry, thus requiring extraction of the moving objects. To compare the effectiveness of the image segmentation, the robust RANSAC algorithm, which calculates the fundamental matrix of epipolar geometry, was used to statistically examine the deviation of epipoles between two consecutive video images. Test was conducted for a video sequence without image segmentation and for the case where a moving object was extracted based on the macro blocks that have a common specified motion vector. The case with the image segmentation was favored in terms of more consistent values of the epipoles as well as the fundamental matrix.
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.
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 | 0.001 | 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 | 0.000 | 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