This paper compares image motion estimation with asynchronous event-based cameras to Computer Vision approaches using as input frame-based video sequences. Since dynamic events are triggered at significant intensity changes, which often are at the border of objects, we refer to the event-based image motion as “contour motion.” Algorithms are presented for the estimation of accurate contour motion from local spatio-temporal information for two camera models: the dynamic vision sensor (DVS), which asynchronously records temporal changes of the luminance, and a family of new sensors which combine DVS data with intensity signals. These algorithms take advantage of the high temporal resolution of the DVS and achieve robustness using a multiresolution scheme in time. It is shown that, because of the coupling of velocity and luminance information in the event distribution, the image motion estimation problem becomes much easier with the new sensors which provide both events and image intensity than with the DVS alone. Experiments on synthesized data from computer vision benchmarks show that our algorithm on combined data outperforms computer vision methods in accuracy and can achieve real-time performance, and experiments on real data confirm the feasibility of the approach. Given that current image motion (or so-called optic flow) methods cannot estimate well at object boundaries, the approach presented here could be used complementary to optic flow techniques, and can provide new avenues for computer vision motion research.

Standardized benchmarks in Computer Vision have greatly contributed to the advance of approaches to many problems in the field. If we want to enhance the visibility of event-driven vision and increase its impact, we will need benchmarks that allow comparison among different neuromorphic methods as well as comparison to Computer Vision conventional approaches. We present datasets to evaluate the accuracy of frame-free and frame-based approaches for tasks of visual navigation. Similar to conventional Computer Vision datasets, we provide synthetic and real scenes, with the synthetic data created with graphics packages, and the real data recorded using a mobile robotic platform carrying a dynamic and active pixel vision sensor (DAVIS) and an RGB+Depth sensor. For both datasets the cameras move with a rigid motion in a static scene, and the data includes the images, events, optic flow, 3D camera motion, and the depth of the scene, along with calibration procedures. Finally, we also provide simulated event data generated synthetically from well-known frame-based optical flow datasets.

The proposed work presents a highly parallel architecture for motion estimation. Our system implements the well-known Lucas and Kanade algorithm with the multi-scale extension for the computation of large motion estimations in a dedicated device [field-programmable gate array (FPGA)]. Our system achieves 270 frames per second for a 640 × 480 resolution in the best case of the mono-scale implementation and 32 frames per second for the multi-scale one, fulfilling the requirements for a real-time system. We describe the system architecture, address the evaluation of the accuracy with well-known benchmark sequences (including a comparative study), and show the main hardware resources used.

Messaging platforms are applications, generally mediated by an app, desktop program or the web, mainly used for synchronous communication among users. As such, they have been widely adopted officially by higher education establishments, after little or no study of their impact and perception by the teachers. We think that the introduction of these new tools and the opportunities and challenges they have needs to be studied carefully in order to adopt the model, as well as the tool, that is the most adequate for all parties involved. We already studied the perception of these tools by students, in this paper we examine the teachers’ experiences and perceptions through a survey that we validated with peers, and what they think these tools should make or serve so that it enhances students learning and helps them achieve their learning objectives. The survey has been distributed among tertiary education teachers, both in universitary and other kind of tertiary establishments, based in Spain (mainly) and Spanish-speaking countries. We have focused on collecting teachers’ preferences and opinions on the introduction of messaging platforms in their day-to-day work, as well as other services attached to them, such as chatbots. What we intend with this survey is to understand their needs and to gather information about the various educational use cases where these tools could be valuable. In addition, an analysis of how and when teachers’ opinions towards the use of these tools varies across gender, experience, and their discipline of specialization is presented. The key findings of this study highlight the factors that can contribute to the advancement of the adoption of messaging platforms and chatbots in higher education institutions to achieve the desired learning outcomes.

With the fast and unstoppable evolution of robotics and artificial intelligence, effective autonomous navigation in real-world scenarios has become one of the most pressing challenges in the literature. However, demanding requirements, such as real-time operation, energy and computational efficiency, robustness, and reliability, make most current solutions unsuitable for real-world challenges. Thus, researchers are fostered to seek innovative approaches, such as bio-inspired solutions. Indeed, animals have the intrinsic ability to efficiently perceive, understand, and navigate their unstructured surroundings. To do so, they exploit self-motion cues, proprioception, and visual flow in a cognitive process to map their environment and locate themselves within it. Computational neuroscientists aim to answer “how” and “why” such cognitive processes occur in the brain, to design novel neuromorphic sensors and methods that imitate biological processing. This survey aims to comprehensively review the application of brain-inspired strategies to autonomous navigation. The paper delves into areas such as neuromorphic perception, asynchronous event processing, energy-efficient and adaptive learning, and the emulation of brain regions vital for navigation, such as the hippocampus and entorhinal cortex.