Image SegNet | Mastering Image Segmentation with SegNet: From Pixels to Boundaries |Skilldux Courses

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In this in-depth session, we explore SegNet, one of the most powerful deep learning architectures for semantic image segmentation.

This video provides a complete breakdown of SegNet, from the encoder–decoder structure to pixel-level classification and boundary refinement. You will understand how SegNet transforms raw pixels into meaningful segmented regions using deep convolutional layers.

Ideal for AI students, ML engineers, researchers, and computer vision enthusiasts, this video covers conceptual understanding, step-by-step breakdowns, and example scenarios to help you master segmentation tasks.

🚀 What You Will Learn
✔ Fundamentals of Image Segmentation
✔ Encoder–Decoder Architecture of SegNet
✔ Role of Max-Pooling Indexes
✔ Pixel-wise Classification
✔ Boundary Refinement Techniques
✔ SegNet vs U-Net vs FCN
✔ Use cases in medical imaging, autonomous vehicles, and satellite imagery
✔ Performance challenges & real-world applications
Join us and take your computer vision skills to the next level!

🎓 About Skilldux Courses
Skilldux Courses delivers high-quality, industry-oriented education in:
AI, Deep Learning, Machine Learning, Cloud Computing, Data Science, IoT, Robotics & Core Engineering.
We help students and professionals build real-world skills with practical and structured learning.

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🏷️ Tags
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❓ Frequently Asked Questions (FAQ)

1. What is SegNet and where is it used?
2. How does SegNet differ from U-Net and FCN?
3. What role do max-pooling indices play in SegNet?
4. Is SegNet suitable for real-time segmentation tasks?
5. What datasets are best for training SegNet?

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Location: Skilldux Edutech Private Limited, Nagercoil, Tamil Nadu, India.
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