A Novel Visual Positioning Algorithm for Massage Acupoints Based on Image Registration
Bibliographic record
Abstract
The conventional method to locate acupuncture points (acupoints) on human body requires the massagists to have rich experience and skillful performance, and the learning cost is always high.The visual positioning technology of massage acupoints based on image registration can lower the technical difficulty, thereby allowing more people to enjoy and benefit from massage therapy.However, existing algorithms for this technology generally have a series of shortcomings including the unstable matching results, the inaccurate image registration effect, and the unsatisfactory results in case of obvious local deformation or occlusion.In view of these matters, this paper studied a novel visual positioning algorithm for acupoints based on image registration.At first, an Image Acupoints Positioning algorithm was proposed based on Convolution Neural Network (CNN-based IAP algorithm), the algorithm can combine the prior information of acupoint positions in visual images with 3D CNN, which has a stronger feature expression ability, and maintain high positioning accuracy under unfavorable conditions such as image noise, illumination change, or occlusion.Then, based on the structure of Fully Convolutional Network (FCN), a multi-scale parallel FCN was constructed, which has introduced the techniques of multiscale parallel downsampling, spatial pyramid of dilated convolutions, adaptive channel attention mechanism, direction perception, and upsampling, intending to improve the model's performance in non-rigid registration of the visual images of massage acupoints.At last, the validity of the proposed model was verified by experimental results.
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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) | 0.002 | 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".