Object Discovery through Motion, Appearance and Shape
Bibliographic record
Abstract
In this thesis we examine the problem of Object Discovery, the autonomous acquisition of object models, using a combination of shape, appearance and motion. We propose a new technique for detecting rigidly moving objects and constructing models of their appearance and shape called the ODMAS (Object Discovery through Motion, Appearance and Shape) system. Our technique is a multi-stage approach. First, a stereo camera is used to find a sequence of images and shape maps of a given scene. Then the scene is oversegmented using normalized cuts based on a combination of shape and appearance. SIFT image features are matched between sequential pairs of images to identify groups of moving features and the three dimensional location of these moving features in the scene is identified using the shape map from the stereo camera. The moving features are then further individuated into objects by identifying groups where the motion of the features is rigid. These rigid feature groups are used to determine which regions in the segmentation of the scene correspond to objects,
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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 | 0.002 |
| 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 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".