Reconstructed Slides: Harnesses in AI: A Deep Dive — Tejas Kumar, IBM

Source Video

Harnesses in AI: A Deep Dive — Tejas Kumar, IBM

Method

This deck is reconstructed from the existing video frame captures by detecting likely slide regions with OpenCV, cropping/upscaling those regions, deduplicating similar crops, and OCRing the cropped slide images locally. It is a cleaner companion to the full-stage frame deck.

Reconstructed Slides

• Source frame: slide-001.jpg
• Crop: full [0, 0, 960, 540] score 157.8

• Source frame: slide-002.jpg
• Crop: full [0, 0, 960, 540] score 160.55

• Source frame: slide-003.jpg
• Crop: full [0, 0, 960, 540] score 160.7

• Source frame: slide-004.jpg
• Crop: full [0, 0, 960, 540] score 161.54

• Source frame: slide-005.jpg
• Crop: full [0, 0, 960, 540] score 161.24

• Source frame: slide-006.jpg
• Crop: full [0, 0, 960, 540] score 162.42

• Source frame: slide-007.jpg
• Crop: contour [0, 0, 960, 540] score 162.56

• Source frame: slide-008.jpg
• Crop: contour [0, 0, 960, 540] score 176.74

• Source frame: slide-009.jpg
• Crop: contour [0, 0, 960, 540] score 176.77

• Source frame: slide-010.jpg
• Crop: contour [0, 0, 960, 540] score 162.33

Dense Scene-Detected Slide Candidates

• youtube C_GG5g38vLU dense slides