CAP5415 - Semantic Segmentation Part 1 - Lecture 17
Offered By: University of Central Florida via YouTube
Course Description
Overview
Explore semantic segmentation techniques in computer vision through this comprehensive lecture. Delve into key concepts including pretrained layers, skip layers, and up-sampling methods such as nearest neighbor, bilinear interpolation, max unpooling, and deconvolution. Examine sample images and results to understand the practical applications of these techniques in image analysis and object recognition.
Syllabus
Introduction
Semantic Segmentation
Sample Image
First Paper
Pretrained Layers
Skip Layers
Sample Results
Up Sampling
Nearest Neighbor
Bilinear Interpolation
Max Unpooling
Deconvolution
Taught by
UCF CRCV
Tags
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