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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 computed using simple intensity difference tests. Furthermore, the descriptor similarity can be evaluated using the Hamming distance, which is very efficient to compute ,(More)
Binary descriptors are becoming increasingly popular as a means to compare feature points very fast while requiring comparatively small amounts of memory. The typical approach to creating them is to first compute floating-point ones, using an algorithm such as SIFT, and then to binarize them. In this paper, we show that we can directly compute a binary(More)
While feature point recognition is a key component of modern approaches to object detection, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. In this paper, we show that formulating the problem in a naive Bayesian classification framework makes such preprocessing unnecessary and produces an(More)
Robotic systems that can create and use visual maps in real-time have obvious advantages in many applications, from automatic driving to mobile manipulation in the home. In this paper we describe a mapping system based on retaining stereo views of the environment that are collected as the robot moves. Connections among the views are formed by consistent(More)
Statistical learning techniques have been used to dramatically speed-up keypoint matching by training a classifier to recognize a specific set of keypoints. However, the training itself is usually relatively slow and performed offline. Although methods have recently been proposed to train the classifier online, they can only learn a very limited number of(More)
Prominent feature point descriptors such as SIFT and SURF allow reliable real-time matching but at a computational cost that limits the number of points that can be handled on PCs, and even more on less powerful mobile devices. A recently proposed technique that relies on statistical classification to compute signatures has the potential to be much faster(More)
This paper addresses the problem of identifying gene modules on the basis of different types of biological data such as gene expression and protein-protein interaction data. Given one or several genes of interest, the aim is to find a group of genes—containing the prespecified genes—that are maximally similar with respect to all data types and sets under(More)
Biologists are interested in the discovery of co-regulated genes since such genes are likely to share a common biological function. This work describes an evolutionary algorithm to find groups of genes in expression data that exhibit expression profiles similar to that of a given query gene. We are clustering over multiple data sets, such that the task is(More)
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