Feature Detection and Matching
Feature detection and matching is an important task in many computer vision applications, such as structure-from-motion, image retrieval, object detection, and more. Challenges in this problem encompass identifying what features are, in a detection step, and further describing those features for other tasks such as feature matching. At SE(3), we are interested in developing better and more robust techniques in feature detection and description.
Works
- Learning to Match Aerial Images with Deep Attentive Architectures
Image matching is a fundamental problem in Computer Vision. In the context of feature-based matching, SIFT and its variants have long excelled in a wide array of applications. However, for ultra-wide baselines, as in the case of aerial images captured under large camera rotations, the appearance variation goes beyond the reach of SIFT and RANSAC. ... - Learning to Detect and Match Keypoints with Deep Architectures
Feature detection and description is a pivotal step in many computer vision pipelines. Traditionally, human engineered features have been the main workhorse in this domain. In this paper, we present a novel approach for learning to detect and describe keypoints from images leveraging deep architectures. To allow for a learning based approach, we collect a ...
