Core Computer Vision Tasks

Core computer vision tasks form the foundation of visual perception systems, enabling machines to understand and interpret visual information from the world. These tasks range from classifying entire images to detecting and segmenting individual objects within them.

Image Classification #

Image classification assigns a label or category to an entire image based on its visual content.

Approaches #

• Traditional Machine Learning: Uses hand-crafted features (e.g., SIFT, HOG) with classifiers like SVMs or Random Forests.
• Convolutional Neural Networks (CNNs): Deep learning approach that automatically learns hierarchical features.
• Transfer Learning: Adapting pre-trained models (e.g., ResNet, VGG) to new classification tasks.
• Ensemble Methods: Combining multiple models to improve performance.
• Self-Supervised Learning: Learning useful representations without explicit labels.
• Few-Shot Learning: Classification with limited labeled examples.

Key Architectures #

• AlexNet: Early CNN that popularized deep learning for computer vision.
• VGG: Simple architecture with small filters and deep layers.
• ResNet: Introduced residual connections to train very deep networks.
• Inception/GoogLeNet: Used parallel convolutional blocks with different filter sizes.
• DenseNet: Creates dense connections between layers to improve feature reuse.
• EfficientNet: Optimized architecture scaling for better efficiency.
• Vision Transformer (ViT): Applies transformer architecture to image patches.

Challenges #

• Intra-class Variation: Objects within the same class can look very different.
• Inter-class Similarity: Different classes may have similar visual appearances.
• Viewpoint Variation: Objects can be viewed from different angles.
• Scale Variation: Objects can appear at different sizes.
• Illumination Changes: Lighting conditions can significantly alter appearance.
• Background Clutter: Complex backgrounds can make object recognition difficult.
• Occlusion: Objects may be partially hidden.