Neural-inspired spike-based computing machines often claim to achieve considerable advantages in terms of energy and time efficiency by using spikes for computation and communication. However, fundamental questions about spike-based computation remain unanswered. For instance, how much advantage do spike-based approaches have over conventional methods, and under what circumstances does spike-based computing provide a comparative advantage? Simply implementing existing algorithms using spikes as the medium of computation and communication is not guaranteed to yield an advantage. Here, we demonstrate that spike-based communication and computation within algorithms can increase throughput, and they can decrease energy cost in some cases. We present several spiking algorithms, including sorting a set of numbers in ascending/descending order, as well as finding the maximum or minimum or median of a set of numbers. We also provide an example application: a spiking median-filtering approach for image processing providing a low-energy, parallel implementation. The algorithms and analyses presented here demonstrate that spiking algorithms can provide performance advantages and offer efficient computation of fundamental operations useful in more complex algorithms.

Some next generation computing devices may consist of resistive memory arranged as a crossbar. Currently, the dominant approach is to use crossbars as the weight matrix of a neural network, and to use learning algorithms that require small incremental weight updates, such as gradient descent (for example Backpropagation). Using real-world measurements, we demonstrate that resistive memory devices are unlikely to support such learning methods. As an alternative, we offer a random search algorithm tailored to the measured characteristics of our devices.

This paper presents a novel representation for dynamic scenes composed of multiple rigid objects that may undergo different motions and are observed by a moving camera. Multiview constraints associated with groups of affine-covariant scene patches and a normalized description of their appearance are used to segment a scene into its rigid components, construct three-dimensional models of these components, and match instances of models recovered from different image sequences. The proposed approach has been applied to the detection and matching of moving objects in video sequences and to shot matching, i.e., the identification of shots that depict the same scene in a video clip.

This paper addresses the problem of capturing an arbitrary convex object P in the plane with three congruent disc-shaped robots. Given two stationary robots in contact with P, we characterize the set of positions of a third robot, the so-called capture region, that prevent P from escaping to infinity via continuous rigid motion. We show that the computation of the capture region reduces to a visibility problem. We present two algorithms for solving this problem, and for computing the capture region when P is a polygon and the robots are points (zero-radius discs). The first algorithm is exact and has polynomial time complexity. The second one uses simple hidden surface removal techniques from computer graphics to output an arbitrarily accurate approximation of the capture region; it has been implemented, and examples are presented.

This paper presents a representation for three-dimensional objects in terms of affine-invariant image patches and their spatial relationships. Multi-view constraints associated with groups of patches are combined with a normalized representation of their appearance to guide matching and reconstruction, allowing the acquisition of true three-dimensional affine and Euclidean models from multiple images and their recognition in a single photograph taken from an arbitrary viewpoint. The proposed approach does not require a separate segmentation stage and is applicable to cluttered scenes. Preliminary modeling and recognition results are presented.

This paper addresses the problem of capturing an arbitrary convex object P in the plane with three congruent disc-shaped robots. Given two stationary robots in contact with P, we characterize the set of positions of a third robot that prevent P from escaping to infinity and show that the computation of this so-called capture region reduces to the resolution of a visibility problem. We present two algorithms for solving this problem and computing the capture region when P is a polygon and the robots are points (zero-radius discs). The first algorithm is exact and has polynomial-time complexity. The second one uses simple hidden-surface removal techniques from computer graphics to output an arbitrarily accurate approximation of the capture region; it has been implemented and examples are presented.

This paper addresses the problem of using three disc-shaped robots to manipulate a polygonal object in the plane in the presence of obstacles. The proposed approach is based on the computation of maximal discs (dubbed maximum independent capture discs, or MICaDs) where the robots can move independently while preventing the object from escaping their grasp. It is shown that, in the absence of obstacles, it is always possible to bring a polygonal object from any configuration to any other one with robot motions constrained to lie in a set of overlapping MICaDs. This approach is generalized to the case where obstacles are present by decomposing the corresponding motion planning task into the construction of a collision-free path for a modified form of the object, and the execution of this path by a sequence of simultaneous and independent robot motions within overlapping MICaDs. The proposed algorithm is guaranteed to generate a valid plan, provided a collision-free path exists for the modified form of the object. It has been implemented and experiments with Nomadic Scout mobile robots are presented.

Presents an implementation of a planner that uses three disc-shaped robots to manipulate a polygonal object in the plane in the presence of obstacles. The approach is based on the computation of the maximal discs (maximal independent capture discs or MICaDs) where the robots can move independently while preventing the object from escaping their grasp. It has been shown that, in the absence of obstacles, it is always possible to bring a polygonal object from any configuration to any other one with robot motions constrained to lie in a set of overlapping MICaDs. This approach is generalized to the case where obstacles are present by decomposing the motion planning task into (1) the construction of a collision-free path for a modified form of the object, and (2) the execution of this path by a sequence of simultaneous and independent robot motions within overlapping MICaDs. The approach is guaranteed to work provided a collision free path exists for the modified form of the object. Experiments with Nomadic Scouts and a visual localization system are presented.