Core Computer Vision Topics
1. Image Classification
Assign a label to an image from a fixed set of categories.
Example: Identifying whether an image contains a cat or a dog.
🔧 Tools: CNNs, ResNet, VGG, EfficientNet
2. Object Detection
Detect and localize multiple objects in an image with bounding boxes.
Example: Detecting cars, pedestrians, and traffic signs in a street image.
🔧 Models: YOLO, SSD, Faster R-CNN, DETR
3. Image Segmentation
Classify each pixel of the image.
Semantic segmentation: Groups all pixels of the same class.
Instance segmentation: Separates objects individually.
Example: Segmenting different organs in medical images.
🔧 Tools: U-Net, Mask R-CNN, DeepLab
4. Image Generation
Create realistic images from noise or input data.
Example: Deepfakes, art generation.
🔧 Models: GANs (Generative Adversarial Networks), Diffusion Models, StyleGAN
5. Face Recognition
Identify or verify a person from an image.
Example: Unlocking phones, surveillance systems.
🔧 Tools: FaceNet, Dlib, OpenCV, DeepFace
6. Optical Character Recognition (OCR)
Convert images of text into machine-readable text.
Example: Digitizing scanned documents or receipts.
🔧 Tools: Tesseract OCR, EasyOCR, Google Vision API
7. Pose Estimation
Detect human body joints and estimate posture.
Example: Fitness tracking, motion capture.
🔧 Models: OpenPose, MediaPipe, PoseNet
🤖 Advanced Topics in Computer Vision
8. 3D Computer Vision
Understand 3D shape, structure, or motion from 2D images or videos.
Example: 3D reconstruction, AR/VR applications.
🔧 Tools: COLMAP, Meshroom, PointNet
9. Image Captioning
Automatically generate a textual description of an image.
Combines computer vision and NLP.
🔧 Models: CNN + RNN, Show and Tell, Transformer-based models (BLIP, Flamingo)
10. Self-Supervised Learning in Vision
Learn representations from unlabeled images.
Example: Pretraining vision models using contrastive loss.
🔧 Models: SimCLR, MoCo, DINO, MAE
11. Vision Transformers (ViTs)
Transformer-based models for image tasks.
Competing with or replacing CNNs in many vision benchmarks.
🔧 Models: ViT, DeiT, Swin Transformer
12. Video Analysis
Includes action recognition, video summarization, and tracking.
Example: Identifying activities like walking or jumping in a video.
🔧 Tools: SlowFast, I3D, Temporal Segment Networks (TSN)
📱 Applications of Computer Vision
Autonomous Vehicles – Object detection, lane detection, depth estimation
Healthcare – Tumor detection, X-ray/CT analysis, diabetic retinopathy screening
Retail & E-commerce – Visual search, product recommendation
Agriculture – Crop monitoring, disease detection
Security – Surveillance, biometric identification
Augmented Reality (AR) – Marker tracking, scene understanding
🧠 Popular Computer Vision Libraries & Tools
Library Use
OpenCV Image processing, real-time computer vision
PyTorch/TensorFlow Model training and deployment
Detectron2 Facebook’s object detection library
MMDetection OpenMMLab’s detection toolbox
Albumentations Fast image augmentations
LabelImg / CVAT Image annotation tools
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