Computer vision relies on several foundational algorithms that address tasks like image classification, object detection, and feature extraction. These algorithms fall into three broad categories: traditional methods, deep learning-based approaches, and hybrid techniques. Each has distinct strengths and is suited to specific use cases, depending on factors like computational resources, accuracy needs, and real-time requirements.
Traditional algorithms, developed before the rise of deep learning, focus on handcrafted feature extraction. Examples include SIFT (Scale-Invariant Feature Transform) and SURF (Speeded-Up Robust Features), which identify and match keypoints in images for tasks like panorama stitching or object recognition. The Viola-Jones algorithm, used for face detection, employs Haar-like features and AdaBoost to efficiently detect objects in real time. HOG (Histogram of Oriented Gradients) is another classic method, often paired with SVMs for pedestrian detection. Edge detection algorithms like Canny or Sobel filters are also foundational, helping to outline shapes in images. While these methods are less dominant today, they remain useful in resource-constrained environments or when labeled training data is scarce.
Deep learning-based algorithms, particularly Convolutional Neural Networks (CNNs), revolutionized computer vision by automating feature learning. Architectures like AlexNet, VGG, and ResNet excel at image classification, while YOLO (You Only Look Once) and Faster R-CNN dominate real-time object detection. For segmentation tasks, U-Net and Mask R-CNN are widely adopted to label pixels at a granular level. Generative Adversarial Networks (GANs) enable image synthesis and enhancement, such as creating realistic faces or restoring old photos. More recently, Vision Transformers (ViTs) have challenged CNNs by applying self-attention mechanisms to image patches, achieving state-of-the-art results in classification. These models require significant computational power but offer superior accuracy for complex tasks.
Modern approaches often combine traditional ideas with deep learning. For example, DETR (Detection Transformer) integrates transformers with CNNs for end-to-end object detection, eliminating manual anchor boxes. EfficientNet optimizes model scaling to balance accuracy and computational cost, making deployment on edge devices practical. Hybrid techniques like SLAM (Simultaneous Localization and Mapping) fuse sensor data with vision algorithms for robotics and AR/VR applications. Developers today often leverage pre-trained models from frameworks like PyTorch or TensorFlow, fine-tuning them for specific tasks. Understanding these algorithms’ trade-offs—such as speed versus accuracy or data requirements—helps in selecting the right tool for a given problem.