We introduce a novel approach for keypoint detection that combines handcrafted and
learned CNN filters within a shallow multi-scale architecture. Handcrafted filters provide …

Abstract Deep Metric Learning (DML) proposes to learn metric spaces which encode
semantic similarities as embedding space distances. These spaces should be transferable …

Image keypoint descriptions that are discriminative and matchable over large changes in
viewpoint are vital for 3D reconstruction. However descriptions output by learned descriptors …

Deep-learning-based localization and mapping approaches have recently emerged as a
new research direction and receive significant attention from both industry and academia …

Detecting robust keypoints from an image is an integral part of many computer vision
problems, and the characteristic orientation and scale of keypoints play an important role for …

Image matching is a key prerequisite for image fusion. Currently, deep learning methods
had shown great potential in matching. However, these methods mainly focus on optical …

Local features detection and description are widely used in many vision applications with
high industrial and commercial demands. With large-scale applications, these tasks raise …

Limited by the locality of convolutional neural networks, most existing local features
description methods only learn local descriptors with local information and lack awareness …

For a long time, the local descriptors learning benefited from the use of L2 normalization,
which projects the descriptor space onto the hypersphere. However, there is no free lunch in …

In the light of recent analyses on privacy-concerning scene revelation from visual
descriptors, we develop descriptors that conceal the input image content. In particular, we …