Geometric Morphometrics
8 min read Peer Verified
The Clinical Vision Layer: 48-Node Facial Landmark Tracking
The AiVet Clinical Vision Layer employs a modified Active Shape Model (ASM) algorithm to track 48-node facial landmark configurations in real-time. This approach transforms standard video feeds into quantifiable anatomical data streams suitable for clinical decision support.
Landmark Architecture
Each facial configuration is decomposed into four anatomical regions, each with distinct clinical significance:
| Anatomical Region | Node Count | Clinical Significance | Temporal Stability (τ) |
|---|---|---|---|
| Periorbital | 12 nodes | Pain assessment (orbital tightening, brow position) | τ = 0.87 ± 0.12 |
| Muzzle/Nose | 16 nodes | Respiratory effort, nasal discharge, lip tension | τ = 0.72 ± 0.18 |
| Ears | 8 nodes | Attention state, discomfort indicators, ear position | τ = 0.65 ± 0.21 |
| Mandible | 12 nodes | Jaw tension, drooling, panting, mouth opening | τ = 0.79 ± 0.15 |
Temporal Stability Scoring
Each landmark is assigned a temporal stability score (τ) based on inter-frame displacement variance. This metric quantifies the reliability of landmark tracking across consecutive video frames:
Where σ²displacement is the variance of pixel displacement across consecutive frames.
Low τ values indicate unstable landmarks (e.g., due to motion blur or occlusion).
94.2%
Mean Euclidean distance < 2 pixels
45 FPS
Real-time processing on RTX 3060
0.82
Validated against DVM consensus
Next: Acoustic Signal Processing
The Breathe Module employs Fast Fourier Transform (FFT) combined with a proprietary Temporal Rhythmic Neural Network for respiratory monitoring.
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