A new infrared 3D camera for Gesture Control
Bibliographic record
Abstract
Gesture Control is a subject which has been investigated almost from the beginning of using terminals to interact with the computer central unit. The advent of Kinect, has sparked a series of efforts to apply gesture control not only in gaming, but rather in controlling TVs or set-top boxes, PCs, laptops, and others. Gestures have been captured by various sensors, either triggering some binary events using primitive methods like mounting diodes around the bezel of the monitor and sensing the passage of the hand over them, or trying to interpret gestures using a camera and complex image processing algorithms based on learning machines techniques. By using special infrared (IR) illumination, it is now possible to obtain robust and stable real-time interaction between the user and the computer. Existing 3D cameras, however, require exotic hardware components, multiple image sensors, or structured IR light projected onto the user. In this paper, a novel real-time depth-mapping principle and IR camera is introduced. The new IR camera architecture comprises an illuminator module which is pulsed and modulated via a monotonic function using a phaselocked loop control for the laser intensity, while the reflected infrared light is captured in “slices” of the space in which the object of interest is situated. A reconfigurable hardware architecture unit calculates the depth slices and combines them in a depth-map of the object. The depth map is further used in the detection, tracking, and recognition of the gesture made by the user. The resolution is variable depending on the resolution and gating possibilities of the image sensor. A sensor of 1 megapixel is used, providing a resolution of 1024×1024. Images of real objects are reconstructed in 3D based on the data obtained by the laser slicing technique, and a corresponding image processing algorithm builds the 3D map of the object in real-time. As this paper will show through a series of experiments, the camera can be used in a variety of domains, including for gesture control of 3D objects in virtual environments.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".