Computer Vision (CV) enables machines to see, interpret, and understand images or videos like humans do.
1️⃣ What is Computer Vision?
It’s a field of AI that trains computers to extract meaningful info from visual inputs (images/videos).
2️⃣ Common Applications:
• Facial recognition (Face ID)
• Object detection (Self-driving cars)
• OCR (Reading text from images)
• Medical imaging (X-rays, MRIs)
• Surveillance security
• Augmented Reality (AR)
3️⃣ Key CV Tasks:
• Image classification: What’s in the image?
• Object detection: Where is the object?
• Segmentation: What pixels belong to which object?
• Pose estimation: Detect body/face positions
• Image generation enhancement
4️⃣ Popular Libraries Tools:
• OpenCV
• TensorFlow Keras
• PyTorch
• Mediapipe
• YOLO (You Only Look Once)
• Detectron2
5️⃣ Image Classification Example:
from tensorflow.keras.applications import MobileNetV2
model = MobileNetV2(weights="imagenet")
6️⃣ Object Detection:
Uses bounding boxes to detect and label objects.
YOLO, SSD, and Faster R-CNN are top models.
7️⃣ Convolutional Neural Networks (CNNs):
Core of most vision models. They detect patterns like edges, textures, shapes.
8️⃣ Image Preprocessing Steps:
• Resizing
• Normalization
• Grayscale conversion
• Data Augmentation (flip, rotate, crop)
9️⃣ Challenges in CV:
• Lighting variations
• Occlusions
• Low-resolution inputs
• Real-time performance
🔟 Real-World Use Cases:
• Face unlock
• Number plate recognition
• Virtual try-ons (glasses, clothes)
• Smart traffic systems
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