Alexander V. Terekhov

Haptic perception is contingent to hemispaces, not to hands

Jess Hartcher-O’Brien, Alexander V. Terekhov, Vincent Hayward

Absract It is known that human haptic perception is lateralised, for example, object shape is felt differently according to the hand used to explore objects. Here we show that it is not the hand but the hemispace in which the exploring hand …

us, gb, fr (code pays fourni par la source)

Estimating the Position of an Image with Unknown Intensity Shape

Yu. É. Korchagin, Вячеслав Верещагин, Alexander V. Terekhov, Kirill A. Melnikov

The problem considered is to estimate the image position of a spatially extended object. It is assumed that the shape of the image intensity is a priori unknown, but it can be predicted with some error. In order to synthesize the estimate …

ru (code pays fourni par la source)

The effective algorithms for estimating the signal and image shift

Alexander V. Terekhov, А. А. Макаров, Kirill A. Melnikov

Abstract In the paper, there are introduced simple algorithms for the estimation of the shift parameter of signals and images synthesized by means of the spectral analysis carried out in the various orthogonal bases formed by the polynomials. It is demonstrated that …

ru (code pays fourni par la source)

VibeRo: Vibrotactile Stiffness Perception Interface for Virtual Reality

Adilzhan Adilkhanov, Amir Yelenov, Ramakanth Singal Reddy, Alexander V. Terekhov et autres

Haptic interfaces allow a more realistic experience with Virtual Reality (VR). They are used to manipulate virtual objects. These objects can be rigid and soft. In this letter, we have designed and evaluated a vibrotactile hand-held device (VibeRo) to achieve haptic cues …

kz, fr (code pays fourni par la source)

Learning abstract perceptual notions: the example of space

Alexander V. Terekhov, John O'Regan

Humans are extremely swift learners. We are able to grasp highly abstract notions, whether they come from art perception or pure mathematics. Current machine learning techniques demonstrate astonishing results in extracting patterns in information. Yet the abstract notions we possess are more …

fr (code pays fourni par la source)

Knowledge transfer in deep block-modular neural networks

Alexander V. Terekhov, Guglielmo Montone, John O'Regan

Although deep neural networks (DNNs) have demonstrated impressive results during the last decade, they remain highly specialized tools, which are trained -- often from scratch -- to solve each particular task. The human brain, in contrast, significantly re-uses existing capacities when learning …

fr (code pays fourni par la source)

Superposition of many models into one

Brian Cheung, Alexander V. Terekhov, Yubei Chen, Pulkit Agrawal et autres

We present a method for storing multiple models within a single set of parameters. Models can coexist in superposition and still be retrieved individually. In experiments with neural networks, we show that a surprisingly large number of models can be effectively stored …

us (code pays fourni par la source)

Learning an internal representation of the end-effector configuration space

Alban Laflaquière, Alexander V. Terekhov, Bruno Gas, John O'Regan

Current machine learning techniques proposed to automatically discover a robot kinematics usually rely on a priori information about the robot's structure, sensors properties or end-effector position. This paper proposes a method to estimate a certain aspect of the forward kinematics model with …

fr (code pays fourni par la source)

Learning agent's spatial configuration from sensorimotor invariants

Alban Laflaquière, John O'Regan, Sylvain Argentieri, Bruno Gas et autres

The design of robotic systems is largely dictated by our purely human intuition about how we perceive the world. This intuition has been proven incorrect with regard to a number of critical issues, such as visual change blindness. In order to develop …

fr (code pays fourni par la source)

Discovering space — Grounding spatial topology and metric regularity in a naive agent’s sensorimotor experience

Alban Laflaquière, John O'Regan, Bruno Gas, Alexander V. Terekhov

fr (code pays fourni par la source)

Discovering space - Grounding spatial topology and metric regularity in a naive agent's sensorimotor experience

Alban Laflaquière, John O'Regan, Bruno Gas, Alexander V. Terekhov

In line with the sensorimotor contingency theory, we investigate the problem of the perception of space from a fundamental sensorimotor perspective. Despite its pervasive nature in our perception of the world, the origin of the concept of space remains largely mysterious. For …

fr (code pays fourni par la source)

Why Early Tactile Speech Aids May Have Failed: No Perceptual Integration of Tactile and Auditory Signals

Aurora Rizza, Alexander V. Terekhov, Guglielmo Montone, Marta Olivetti Belardinelli et autres

Tactile speech aids, though extensively studied in the 1980's and 1990's, never became a commercial success. A hypothesis to explain this failure might be that it is difficult to obtain true perceptual integration of a tactile signal with information from auditory speech: …

it, fr (code pays fourni par la source)