Computer vision geo-location, awareness & detail
Bibliographic record
Abstract
Computer vision (i.e., image understanding) involves understanding the 3D scene creating the image. Computer vision is challenging because it is the computer that decides how to act based on an understanding of the image. Key image understanding tasks include depth computation, as well as object detection, localization, recognition and tracking. Techniques up to now have not been able to perform any of these tasks robustly with the precision and accuracy demanded by many real-world applications. Additional complications include operational and environmental factors. For humans, visual recognition is fast and accurate, yet robust against occlusion, clutter, viewpoint variations, and changes in lighting conditions. Moreover, learning new categories requires minimal supervision and a very small set of exemplars. Achieving this level of performance in a wearable portable system would enable a great number of useful applications especially for enhancing mobile cell phone and camera operation. We demonstrate some of the computer vision techniques that we have developed and tested in real environments for applications in the field of automotive navigation, personal navigation, assistive devices and augmented reality. Some of the techniques include object detection and recognition, depth from motion, context recognition and the general task of mapping and localization. Our object detection techniques have shown to have performance close to 100%. We have actually shown that we can triangulate based on objects in the environment using only a camera; which can aid when GPS drops out such as in urban canyons and indoor environments. We argue that all of this potential can be packaged within a smart phone like an iphone.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.039 |
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".