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Computer Vision
Chapters

1Foundations of Image Formation

2Low-Level Image Processing

3Feature Detection and Representation

Interest Point Detectors (Harris, FAST)Scale-space and SIFT DetectorDescriptor Design: SIFT, SURF, ORBEdge and Line FeaturesRegion and Blob Detectors (MSER)Local Binary Patterns and Texture FeaturesFeature Matching and Outlier RejectionFeature-based Image AlignmentEvaluating Feature RobustnessFeature Pooling and Mid-level Representations

4Classical Computer Vision: Geometry and Matching

5Machine Learning for Vision

6Deep Learning Foundations for Vision

7Advanced Deep Architectures and Vision Transformers

8Object Detection, Localization, and Recognition

Courses/Computer Vision/Feature Detection and Representation

Feature Detection and Representation

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Study classical feature detectors and descriptors (points, edges, regions), their invariances, and how they support higher-level tasks.

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Scale-space and SIFT Detector

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