24,513 research outputs found
A Neural Network Model for Cursive Script Production
This article describes a neural network model, called the VITEWRITE model, for generating handwriting movements. The model consists of a sequential controller, or motor program, that interacts with a trajectory generator to move a. hand with redundant degrees of freedom. The neural trajectory generator is the Vector Integration to Endpoint (VITE) model for synchronous variable-speed control of multijoint movements. VITE properties enable a simple control strategy to generate complex handwritten script if the hand model contains redundant degrees of freedom. The proposed controller launches transient directional commands to independent hand synergies at times when the hand begins to move, or when a velocity peak in a given synergy is achieved. The VITE model translates these temporally disjoint synergy commands into smooth curvilinear trajectories among temporally overlapping synergetic movements. The separate "score" of onset times used in most prior models is hereby replaced by a self-scaling activity-released "motor program" that uses few memory resources, enables each synergy to exhibit a unimodal velocity profile during any stroke, generates letters that are invariant under speed and size rescaling, and enables effortless. connection of letter shapes into words. Speed and size rescaling are achieved by scalar GO and GRO signals that express computationally simple volitional commands. Psychophysical data concerning band movements, such as the isochrony principle, asymmetric velocity profiles, and the two-thirds power law relating movement curvature and velocity arise as emergent properties of model interactions.National Science Foundation (IRI 90-24877, IRI 87-16960); Office of Naval Research (N00014-92-J-1309); Air Force Office of Scientific Research (F49620-92-J-0499); Defense Advanced Research Projects Agency (90-0083
Switching quantum reference frames in the N-body problem and the absence of global relational perspectives
Given the importance of quantum reference systems to both quantum and
gravitational physics, it is pertinent to develop a systematic method for
switching between the descriptions of physics relative to different choices of
quantum reference systems, which is valid in both fields. Here, we continue
with such a unifying approach, begun in arxiv:1809.00556, whose key ingredients
is a gravity-inspired symmetry principle, which enforces physics to be
relational and leads, thanks to gauge related redundancies, to a
perspective-neutral structure which contains all frame choices at once and via
which frame perspectives can be consistently switched. Formulated in the
language of constrained systems, the perspective-neutral structure turns out to
be the constraint surface classically and the gauge invariant Hilbert space in
the Dirac quantized theory. By contrast, a perspective relative to a specific
frame corresponds to a gauge choice and the associated reduced phase and
Hilbert space. Quantum reference frame switches thereby amount to a symmetry
transformation. In the quantum theory, they require a transformation that takes
one from the Dirac to a reduced quantum theory and we show that it amounts to a
trivialization of the constraints and a subsequent projection onto the
classical gauge fixing conditions. We illustrate this method in the relational
-body problem with rotational and translational symmetry. This model is
particularly interesting because it features the Gribov problem so that
globally valid gauge fixing conditions are impossible which, in turn, implies
also that globally valid relational frame perspectives are absent in both the
classical and quantum theory. These challenges notwithstanding, we exhibit how
one can systematically construct the quantum reference frame transformations
for the three-body problem.Comment: 22 pages, plus appendice
Interpretable Transformations with Encoder-Decoder Networks
Deep feature spaces have the capacity to encode complex transformations of
their input data. However, understanding the relative feature-space
relationship between two transformed encoded images is difficult. For instance,
what is the relative feature space relationship between two rotated images?
What is decoded when we interpolate in feature space? Ideally, we want to
disentangle confounding factors, such as pose, appearance, and illumination,
from object identity. Disentangling these is difficult because they interact in
very nonlinear ways. We propose a simple method to construct a deep feature
space, with explicitly disentangled representations of several known
transformations. A person or algorithm can then manipulate the disentangled
representation, for example, to re-render an image with explicit control over
parameterized degrees of freedom. The feature space is constructed using a
transforming encoder-decoder network with a custom feature transform layer,
acting on the hidden representations. We demonstrate the advantages of explicit
disentangling on a variety of datasets and transformations, and as an aid for
traditional tasks, such as classification.Comment: Accepted at ICCV 201
Ventral-stream-like shape representation : from pixel intensity values to trainable object-selective COSFIRE models
Keywords: hierarchical representation, object recognition, shape, ventral stream, vision and scene understanding, robotics, handwriting analysisThe remarkable abilities of the primate visual system have inspired the construction of computational models of some visual neurons. We propose a trainable hierarchical object recognition model, which we call S-COSFIRE (S stands for Shape and COSFIRE stands for Combination Of Shifted FIlter REsponses) and use it to localize and recognize objects of interests embedded in complex scenes. It is inspired by the visual processing in the ventral stream (V1/V2 → V4 → TEO). Recognition and localization of objects embedded in complex scenes is important for many computer vision applications. Most existing methods require prior segmentation of the objects from the background which on its turn requires recognition.
An S-COSFIRE filter is automatically configured to be selective for an arrangement of contour-based features that belong to a prototype shape specified by an example. The configuration comprises selecting relevant vertex detectors and determining certain blur and shift parameters. The response is computed as the weighted geometric mean of the blurred and shifted responses of the selected vertex detectors. S-COSFIRE filters share similar properties with some neurons in inferotemporal cortex, which provided inspiration for this work.
We demonstrate the effectiveness of S-COSFIRE filters in two applications: letter and keyword spotting in handwritten manuscripts and object spotting in complex scenes for the computer vision system of a domestic robot.
S-COSFIRE filters are effective to recognize and localize (deformable) objects in images of complex scenes without requiring prior segmentation. They are versatile trainable shape detectors, conceptually simple and easy to implement. The presented hierarchical shape representation contributes to a better understanding of the brain and to more robust computer vision algorithms.peer-reviewe
- …