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 …

We introduce a loss for metric learning, which is inspired by the Lowe's matching criterion for
SIFT. We show that the proposed loss, that maximizes the distance between the closest …

SIFT is a classical hand-crafted, histogram-based descriptor that has deeply influenced
research on image matching for more than a decade. In this paper, a critical review of the …

In this paper, we present a novel approach that exploits the information within the descriptor
space to propose keypoint locations. Detect then describe, or detect and describe jointly are …

Feature based image matching has been a research focus in photogrammetry and computer
vision for decades, as it is the basis for many applications where multi-view geometry is …

We propose a kernelized deep local-patch descriptor based on efficient match kernels of
neural network activations. Response of each receptive field is encoded together with its …

We present AMOS Patches, a large set of image cut-outs, intended primarily for the
robustification of trainable local feature descriptors to illumination and appearance changes …

In the thesis we consider the problem of local feature descriptor learning for wide baseline
stereo focusing on the HardNet descriptor, which is close to state-of-the-art. AMOS Patches …

The ability to accurately and efficiently match between sets of items has always been
fundamental in computer vision pipelines and applications with a wide variety of realizations …

Feature descriptor matching is a critical step is many computer vision applications such as
image stitching, image retrieval and visual localization. However, it is often affected by many …