Computer vision applications have come to rely increasingly on superpixels in recent years,
but it is not always clear what constitutes a good superpixel algorithm. In an effort to …

State-of-the-art methods for counting people in crowded scenes rely on deep networks to
estimate crowd density. They typically use the same filters over the whole image or over large …

We propose to use binary strings as an efficient feature point descriptor, which we call
BRIEF.We show that it is highly discriminative even when using relatively few bits and can be …

We propose a non-iterative solution to the PnP problem—the estimation of the pose of a
calibrated camera from n 3D-to-2D point correspondences—whose computational complexity …

Superpixels are becoming increasingly popular for use in computer vision applications.
However, there are few algorithms that output a desired number of regular, compact superpixels …

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

In many 3D object-detection and pose-estimation problems, runtime performance is of critical
importance. However, there usually is time to train the system, which we would show to be …

While feature point recognition is a key component of modern approaches to object detection,
existing approaches require computationally expensive patch preprocessing to handle …

In earlier work, we proposed treating wide baseline matching of feature points as a classification
problem, in which each class corresponds to the set of all possible views of such a point…

In this paper, we introduce a local image descriptor, DAISY, which is very efficient to compute
densely. We also present an EM-based algorithm to compute dense depth and occlusion …