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In Structure From Motion (SFM), image features are matched in either an extended number of frames or only in pairs of consecutive frames. Traditionally, SFM filters have been applied using only one of the two matching paradigms, with the Long Range (LR) feature technique being more popular because of the fact that features that are matched across multiple frames provide stronger constraints on structure...
Feature detection is a crucial step in many Computer Vision applications such as matching, tracking, visual odometry and object recognition, etc. Detecting robust features that are persistent, rotation-invariant, and quickly calculated is a major problem in computer vision. Feature detectors using the difference of Gaussian (DoG) are computationally expensive, however, if the DoG is used with image...
Underground pipes constitute the backbone of the infrastructure of a country. Dirty, broken, or clogged pipes have direct implications on the health hazards of humans. It is therefore no surprise that fault assessment of pipes is an important topic, which has received considerable attention in the past. While most pipe analysis systems rely on active sensors such as laser or sonar, the use of passive...
Tracking and understanding human gait is an important step towards improving elderly mobility and safety. Our research team is developing a vision-based tracking system that estimates the 3D pose of a wheeled walker user's lower limbs with a depth sensor, Kinect, mounted on the moving walker. Our tracker estimates 3D poses from depth images of the lower limbs in the coronal plane in a dynamic, uncontrolled...
Filter-based Structure from Motion (SfM) approaches work usually in two steps: prediction and update. Prediction is the process of determining a prior distribution of the state vector at time t + 1 from the previous distribution at time t. Update is the process of adjusting the predicted distribution so it complies with the new received measurements at time t + 1. A key issue in those two steps is...
The Extended Kalman Filter (EKF) is still one of the most widely used approaches for small scale Structure from Motion (SFM) and Simultaneous Localization And Mapping (SLAM) problems. However, the EKF does not have the ability to take into account the motion information carried by features matched only between two consecutive frames. This information is valuable because, when used appropriately, it...
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