This thesis investigates ways in which attention, both temporal and spatial, as measured by gaze and head motion can be used to alleviate complexity in computer vision tasks. Humans mostly express attention by using their eyes, but also their head gaze. These gaze patterns often indicate an object or area of interest. This thesis first presents a method to extract the user's attention from eye gaze fixation and to filter outliers, then considers head gaze as source of information. The first approach of user's attention estimation considered uses a combination of eye gaze fixations, observed region's visual appearance, 3D pose information, and the user's motion. The proposed method then employs the user's attention to identify the object of interest and produce a 3D model reconstruction of that object. The approach is evaluated for both indoor and outdoor objects, and compares against baseline image segmentation alternatives. Secondly, a method to discover task-relevant objects by using the attention extracted from users performing daily life activities is presented. A graphical model representing an activity is generated and used to demonstrate how the method can predict the next object to be interacted with. In addition, 3D models of the task-relevant objects are reconstructed using the gaze collected from the users. This method shows that the information gathered from eye gaze can be used to teach the computer about the tasks being performed. Thirdly, a method to estimate the user's visual temporal and spatial attention using the user's ego-motion is presented. The method allows the ego-motion to be determined from scene motion features obtained from optical flow and a head-mounted Inertial Measurement Unit (IMU). Threshold values for the temporal attention model, which states the 'when' the user is paying attention to a task, are selected using an ROC-evaluated approach. The spatial attention model, which indicates 'where' the user is looking at in the environment, is built using a data-driven approach via kernel regression. Comparative results using different motion features extracted from the optical flow and the IMU are provided, and results show the advantage of the proposed IMU-based model. Finally, the thesis proposes a mixed reality system named GlaciAR, which aims to augment users for task guidance applications. The system combines the ideas presented before and uses an automated and unsupervised information collection approach to generate video guides. It is a self-contained system and runs onboard an eye-wear computer (Google Glass). It is able to automatically determine the user's attention using an IMU-based head motion model that collects video snippet information. It then is able to also trigger video guides to help inexperienced users performing the task. The evaluation of the system compares to video guides manually edited by experts and shows validation of the proposed attention model for collecting and triggering guidance.