Object recognition

Object recognition refers to the ability to identify and locate objects within visual data like images or videos. It is a core problem in computer vision and visual AI systems. Key techniques involve:

Extracting visual features from image pixels and transforms.
Using machine learning like convolutional neural networks.
Building robust models by learning from labeled training data.
Recognizing objects even under variations like viewpoint, scale, lighting.

Applications include:

Automated image tagging and captioning.
Self-driving vehicle systems identifying pedestrians, signs, etc.
Robots recognizing items for grasping and manipulation.
Augmented reality overlaying information about detected objects.
Image search based on recognizing objects of interest.

Challenges involve handling occlusion, clutter, and diversity of visual appearances. Deep learning driven by large annotated datasets like ImageNet has revolutionized object recognition capabilities.