Spatial/joint Space-Time Motion Segmentation Of Image Sequences By Level Set Pursuit
Bibliographic record
Abstract
this paper is the following: Given a set of sparse point correspondences, how does one obtain a dense motion field and a motion-based segmentation of the image sequence ? We propose a novel solution to this problem by formulating it as pursuit in segmentation space. This segmentation is defined by level set evolution equations, allowing changes in segmentation topology [4]. The main novelty of our proposed algorithm is that the number of distinct motion regions and their parameters need not be known prior to segmentation and are determined by the segmentation itself. Furthermore, the algorithm we propose applies equally to (frame-by-frame) spatial motion-based segmentation and to (multiframe) joint space-time motion-based segmentation. This extends our prior work on motion-based image segmentation with level sets in both the spatial [3] and spatio-temporal domains [2] where the number of distinct motion regions as well as their precise motion parameters need to be computed through a complex clustering operation prior to segmentation. We formulate motion-based image segmentation as pursuit in the space of image segmentations, in analogy to classical matching pursuit, the basic idea consisting of iteratively segmenting the image by focusing on residual regions. The segmentation obtained by our algorithm is based on motion alone and does not use intensity boundaries as an auxiliary. We formulate our algorithm for the case of spatial motion segmentation and illustrate it on a real image sequence with natural motion; our proposed algorithm applies verbatim to the case of joint space-time motion segmentation with level sets, and we refer the reader to [2] for details of the latter. This work was supported by the Natural Sciences and Engineering Research Council of Canada unde...
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".