Learned local descriptors based on Convolutional Neural Networks (CNNs) have achieved
significant improvements on patch-based benchmarks, whereas not having demonstrated …

Image feature matching is an integral task for many computer vision applications such as
object tracking, image retrieval, etc. The images can be matched no matter how the image …

Current best local descriptors are learned on a large data set of matching and non-matching
keypoint pairs. However, data of this kind are not always available, since the detailed …

Fast binary descriptors build the core for many vision based applications with real-time
demands like object detection, visual odometry or SLAM. Commonly it is assumed, that the …

Many operations of vision require image regions to be isolated and inter-related. This is
challenging when they are different in detail and extent. Practical methods of Computer …

For image matching, the scale invariant feature transform (SIFT) algorithm is a commonly
used one. They are invariant to image rotation, scale zooming, and partially invariant to …

We combine the near‐sensor image processing concept with address‐event representation
leading to an intensity‐ranking image sensor (IRIS) and show the benefits of using this type …