Extracting information from a digital image often depends on first identifying desired objects or breaking down the image into homogenous regions (a process called ‘segmentation’) and then assigning these objects to particular classes (a process called ‘classification’). This is a fundamental part of computer vision, combining image processing and pattern recognition techniques. Homogeneous may refer to the color of the object or region, but it also may use other features such as texture and shape. The methodology can be used to identify tumours in medical images, crops in satellite imagery, cells in biological tissue, or human faces in standard digital images or video. Each segmentation/classification implementation has the same fundamental approach; however, specific objects and imagery often require dedicated techniques for improved success. In the VIP lab, a dedicated example of segmentation is our advanced work in decoupled active contours. A dedicated example of classification is the automated identification of sea ice in satellite SAR images.