Chapter 6: Recognition
Instance recognition, image classification, object detection, semantic segmentation, and face recognition.
Chapter Overview
Recognition is where computer vision meets understanding. Given an image, can we determine what's in it? This seemingly simple question has driven decades of research.
What is this chapter about? We explore the major recognition tasks: classifying whole images, detecting and localizing objects, segmenting images pixel-by-pixel, and recognizing faces. Each builds on the deep learning foundations from Chapter 5.
Why does this matter? Recognition powers countless real-world applications:
- Image classification: Organizing photos, medical diagnosis, content moderation
- Object detection: Self-driving cars, security cameras, retail analytics
- Segmentation: Medical imaging, autonomous navigation, photo editing
- Face recognition: Biometric authentication, photo organization
How the topics connect: We start with image classification—the simplest task where we assign one label to an entire image. Then object detection adds localization—where are objects? Semantic segmentation provides pixel-level understanding. Finally, face recognition shows how these ideas apply to a specific, important domain.
Chapter Roadmap
Click any topic to jump in
Image Classification
Feature extraction, softmax, and cross-entropy — assigning a single category label to an entire image.
Where are objects? What label per pixel?
Object Detection
IoU, anchor boxes, NMS, and mAP — localizing and classifying multiple objects with bounding boxes.
Semantic Segmentation
Pixel-wise classification with encoder-decoder networks — assigning a class label to every pixel in the image.
Face Recognition
Embeddings, triplet loss, and ArcFace — mapping faces to a metric space for verification and identification.
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