Multimodal image matching, which refers to identifying and then corresponding the same or
similar structure/content from two or more images that are of significant modalities or …

As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …

Interest point detection and local feature description are fundamental steps in many
computer vision applications. Classical approaches are based on a detect-then-describe …

We present a novel deep architecture and a training strategy to learn a local feature pipeline
from scratch, using collections of images without the need for human supervision. To do so …

We address the problem of finding reliable dense correspondences between a pair of
images. This is a challenging task due to strong appearance differences between the …

Estimating depth from RGB images is a long-standing ill-posed problem, which has been
explored for decades by the computer vision, graphics, and machine learning communities …

In this work we address the task of semantic image segmentation with Deep Learning and
make three main contributions that are experimentally shown to have substantial practical …

We introduce a novel Deep Network architecture that implements the full feature point
handling pipeline, that is, detection, orientation estimation, and feature description. While …

The research focus of designing local patch descriptors has gradually shifted from
handcrafted ones (eg, SIFT) to learned ones. In this paper, we propose to learn high per …

We address the problem of determining correspondences between two images in
agreement with a geometric model such as an affine or thin-plate spline transformation, and …