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# Multimodal Slide Synchronization (Original Algorithm)

[Read Article](https://tablerus.es/articles/aimlas.md)

**Date:** February 2026
**Technologies:** Python, OpenCV, HuggingFace

---

## Project Overview

A hybrid computer vision algorithm designed to synchronize presentation video recordings with their corresponding slide decks. The system addresses the challenge of tracking which slide is visible at any given moment in classroom recordings where traditional geometric methods fail due to occlusions, projector glare, and clothing patterns.

The algorithm combines classical computer vision (SIFT keypoint matching with RANSAC homography filtering), multimodal neural embeddings (CLIP for semantic layout similarity), and dynamic programming (Viterbi for temporal coherence). This fusion achieves **98.3% accuracy** on manually annotated ground truth, a critical capability for the AIMLaS presentation feedback system, where contextualizing a speaker's gestures against the actual slide content is essential for meaningful automated feedback.

## Motivation

Existing presentation analysis tools operate in a semantic vacuum: they can detect that a speaker is looking at a screen, but they cannot determine _which_ content is being displayed. This disconnect prevents automated feedback systems from anchoring behavioral observations to specific topics (e.g., "During the conclusions slide, you maintained eye contact with the audience").

Pure geometric approaches like SIFT collapse in real classroom conditions: plaid shirts, grid patterns, and background objects generate catastrophic false positives. Pure semantic approaches like CLIP lack the spatial precision to distinguish between two text-heavy slides with similar layouts. The design goal was a system robust enough for production academic environments without requiring specialized hardware.

## Core Architecture

The algorithm operates on three complementary axes:

| Component             | Role                          | Solves                                                                   |
| --------------------- | ----------------------------- | ------------------------------------------------------------------------ |
| **SIFT**              | Geometric keypoint extraction | Precise corner/edge matching on structured slides                        |
| **RANSAC Homography** | Spatial consistency filtering | Eliminates false matches from background noise (e.g., clothing patterns) |
| **CLIP**              | Semantic embedding similarity | Distinguishes slides with sparse visual features via layout semantics    |
| **Viterbi**           | Dynamic programming           | Enforces temporal monotonicity, preventing impossible slide jumps        |

### SIFT with Normalization

Classical SIFT matching is sensitive to text-heavy slides dominating empty title slides simply because they contain more keypoints. The system normalizes raw match counts against each slide's total feature count:

```python
sift_norm = (sift_raw / pdf_num_keypoints) * 1000
```

This prevents a bibliography slide with dense text from unfairly outscoring a minimal title slide.

### RANSAC Filtering

SIFT alone accumulates false positives from high-frequency environmental patterns. A homography check via `cv2.findHomography(..., cv2.RANSAC, 5.0)` filters matches that do not conform to a valid planar perspective transform. This mechanism discards **73.02%** of incorrect-slide matches while preserving **68.5%** of correct-slide matches, a highly selective noise gate.

### CLIP Semantic Fusion

For slides with insufficient keypoints (diagrams, sparse layouts), the system falls back to semantic similarity. Both the video frame and the rendered PDF slide are encoded via `clip-ViT-B-32`, and cosine similarity provides a layout-aware score independent of geometric detail.

### Combined Scoring

The final per-frame, per-slide score balances both modalities:

```python
combined_score = sift_norm + (clip_score * 100)
```

Top candidates are selected via a multi-threshold consensus: a slide must score within 75% of the maximum on at least one of the three metrics (combined, SIFT, or CLIP) to be considered.

## Temporal Coherence via Viterbi

Individual frame matching remains noisy. The system post-processes all frame scores globally using the Viterbi algorithm, which finds the optimal slide sequence by penalizing illogical transitions:

- **Same slide**: Forbidden (a presentation must advance)
- **Natural flow** (slide _n_ → _n_+1): No penalty
- **Skipping slides**: Penalized by `-80 × skipped_count`
- **Going backwards**: Penalized by `-80 × distance`

This transforms the problem from independent per-frame classification into a globally optimal path through the presentation timeline.

## Accuracy & Ablation

The system was validated against **702 manually annotated slide transitions** from classroom recordings. An ablation study quantifies the contribution of each component:

![Ablation study showing accuracy progression from 29.2% (CLIP only) to 98.3% (SIFT + RANSAC + CLIP + Viterbi).](/assets/articles/aimlas/pruebas/ablacion_sincro.webp)

**Figure:** Accuracy of slide matching across component combinations. C = CLIP, S = SIFT, R = RANSAC, V = Viterbi.

| Configuration     | Accuracy  | Notes                                                       |
| ----------------- | --------- | ----------------------------------------------------------- |
| C (CLIP only)     | 29.2%     | Semantic alone insufficient for fine-grained discrimination |
| S (SIFT only)     | 77.4%     | Geometric precision but swamped by environmental noise      |
| S + R             | 79.1%     | RANSAC removes background false positives                   |
| S + R + C         | 77.8%     | Semantic backup fills SIFT's sparse-slide gaps              |
| **S + R + C + V** | **98.3%** | Temporal coherence eliminates remaining jumps               |

The Viterbi layer provides the largest single accuracy gain in the final stage, enforcing presentation monotonicity and cleaning sporadic misclassifications.

## Optimization: Event-Gated Sampling

Rather than analyzing every video frame (theoretical complexity $\mathcal{O}(T \times S^2)$ with $T \approx 18{,}000$ frames for a 15 minute presentation at 20fps), the system leverages existing `Slide_change` event logs to sample only around known transition times, plus a final capture 3 seconds after the last change. This reduces operational complexity to approximately $\mathcal{O}(S^3)$ - roughly a **1,000× reduction** for a 18-slide presentation, typical for a talk of this duration, while maintaining full accuracy.

## Technical Stack

- **OpenCV** (`cv2.SIFT_create`, `BFMatcher`, `findHomography`) for geometric pipeline
- **PyMuPDF** (`fitz`) for high-resolution PDF slide rendering at 2× zoom
- **SentenceTransformers** (`clip-ViT-B-32`) for neural embeddings
- **NumPy / SciPy** for Viterbi dynamic programming matrices
- **Pandas** for temporal synchronization against Excel-grounded video start times