Motor learning.

Although learning a motor skill, such as a tennis stroke, feels like a unitary experience, researchers who study motor control and learning break the processes involved into a number of interacting components. These components can be organized into four main groups. First, skilled performance requires the effective and efficient gathering of sensory information, such as deciding where and when to direct one's gaze around the court, and thus an important component of skill acquisition involves learning how best to extract task-relevant information. Second, the performer must learn key features of the task such as the geometry and mechanics of the tennis racket and ball, the properties of the court surface, and how the wind affects the ball's flight. Third, the player needs to set up different classes of control that include predictive and reactive control mechanisms that generate appropriate motor commands to achieve the task goals, as well as compliance control that specifies, for example, the stiffness with which the arm holds the racket. Finally, the successful performer can learn higher-level skills such as anticipating and countering the opponent's strategy and making effective decisions about shot selection. In this Primer we shall consider these components of motor learning using as an example how we learn to play tennis.

Efficient block segmentation algorithm for true motion estimation

A block-based motion estimation technique is proposed which permits a less general segmentation performed using an efficient deterministic algorithm. Applied to image pairs from the Flower Garden and Table Tennis sequences, the algorithm successfully localizes motion discontinuities and detects uncovered regions. The algorithm is implemented in C on a Sun Sparcstation 20. The gradient-based motion estimation required 28.8 s CPU time, and 500 iterations of the segmentation algorithm required 32.6 s.