We propose a learning approach to corner detection for event-based cameras that is stable even under fast and abrupt motions. Event-based cameras offer high temporal resolution, power efficiency, and high dynamic range. However, the properties of event-based data are very different compared to standard intensity images, and simple extensions of corner detection methods designed for these images do not perform well on event-based data. We first introduce an efficient way to compute a time surface that is invariant to the speed of the objects. We then show that we can train a Random Forest to recognize events generated by a moving corner from our time surface. Random Forests are also extremely efficient, and therefore a good choice to deal with the high capture frequency of event-based cameras-our implementation processes up to 1.6Mev/s on a single CPU. Thanks to our time surface formulation and this learning approach, our method is significantly more robust to abrupt changes of direction of the corners compared to previous ones. Our method also naturally assigns a confidence score for the corners, which can be useful for postprocessing. Moreover, we introduce a high-resolution dataset suitable for quantitative evaluation and comparison of corner detection methods for event-based cameras. We call our approach SILC, for Speed Invariant Learned Corners, and compare it to the state-of-the-art with extensive experiments, showing better performance.

Texture classification is used for many vision systems; in this paper we focus on improving the reliability of the classification through the so-called imprecise (or credal) classifiers, which suspend the judgment on the doubtful instances by returning a set of classes instead of a single class. Our view is that on critical instances it is more sensible to return a reliable set of classes rather than an unreliable single class. We compare the traditional naive Bayes classifier (NBC) against its imprecise counterpart, the naive credal classifier (NCC); we consider a standard classification dataset, when the problem is made progressively harder by introducing different image degradations or by providing smaller training sets. Experiments show that on the instances for which NCC returns more classes, NBC issues in fact unreliable classifications; the indeterminate classifications of NCC preserve reliability but at the same time also convey significant information, reducing the set of possible classes (on most critical instances) from 24 to some 2-3.

Recent works have shown that it is possible to solve the Simultaneous Localization And Mapping problem using an Extended Kalman Filter and a single perspective camera. The principal drawback of these works is an inaccurate modeling of measurement uncertainties, which therefore causes inconsistencies in the filter estimations. A possible solution to proper uncertainty modeling is the Unified Inverse Depth parametrization. In this paper we propose the Inverse Scaling parametrization that still allows an un-delayed initialization of features, while reducing the number of needed parameters and simplifying the measurement model. This novel approach allows a better uncertainty modeling of both low and high parallax features and reduces the likelihood of inconsistencies. Experiments in simulation demonstrate that the use of the Inverse Scaling solution improves the performance of the monocular EKF SLAM filter when compared with the Unified Inverse Depth approach; experiment on real data confirm the applicability of the idea.

The recent literature has shown that it is possible to solve the monocular Simultaneous Localization And Mapping using both undelayed features ini- tialization and an Extedend Kalman Filter. The key concept, to achieve this result, was the introduction of a new parametrization called Unified Inverse Depth that produces measurements equations with a high degree of linear- ity and allows an efficient and accurate modeling of uncertainties. In this paper we present a monocular EKF SLAM filter based on an alternative parametrization, i.e., the Inverse Scaling Parametrization, characterized by a reduced number of parameters, a more linear measurement model, and a bet- ter modeling of features uncertainty for both low and high parallax features. Experiments in simulation demonstrate that the use of the Inverse Scaling solution improves the monocular EKF SLAM filter when compared with the Unified Inverse Depth approach, while experiments on real data show the system working in practice as well.