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An Action-tuned Neural Network Architecture for Hand Pose Estimation
No full textThere is a growing interest in developing computational models of grasping action recognition. This interest is increasingly motivated by a wide range of applications in robotics, neuroscience, HCI, motion capture and other research areas. In many cases, a vision-based approach to grasping action recognition appears to be more promising. For example, in HCI and robotic applications, such an approach often allows for simpler and more natural interaction. However, a vision-based approach to grasping action recognition is a challenging problem
due to the large number of hand self-occlusions which make the mapping from hand visual appearance to the hand pose an inverse ill-posed problem. The approach proposed here builds on the work of Santello
and co-workers which demonstrate a reduction in hand variability within a given class of grasping actions.
The proposed neural network architecture introduces specialized modules for each class of grasping actions and viewpoints, allowing for a more robust hand pose estimation. A quantitative analysis of the proposed
architecture obtained by working on a synthetic data set is presented and discussed as a basis for further work
A linear approach for sparse coding by a two-layer neural network
No full textMany approaches to transform classification problems from non-linear to linear by feature transformation have been recently presented in the literature. These notably include sparse coding methods and deep neural networks. However, many of these approaches require the repeated application of a learning process upon the presentation of unseen data input vectors, or else involve the use of large numbers of parameters and hyper-parameters, which must be chosen through cross-validation, thus increasing running time dramatically. In this paper, we propose and experimentally investigate a new approach for the purpose of overcoming limitations of both kinds. The proposed approach makes use of a linear auto-associative network (called SCNN) with just one hidden layer. The combination of this architecture with a specific error function to be minimized enables one to learn a linear encoder computing a sparse code which turns out to be as similar as possible to the sparse coding that one obtains by re-training the neural network. Importantly, the linearity of \{SCNN\} and the choice of the error function allow one to achieve reduced running time in the learning phase. The proposed architecture is evaluated on the basis of two standard machine learning tasks. Its performances are compared with those of recently proposed non-linear auto-associative neural networks. The overall results suggest that linear encoders can be profitably used to obtain sparse data representations in the context of machine learning problems, provided that an appropriate error function is used during the learning phase
Designing Structured Sparse Dictionaries for Sparse Representation Modeling
No full textLinear approaches to the problem of unsupervised data dimensionality reduction consist in finding a suitable set of factors, which is usually called dictionary, on the basis of which data can be represented as a linear combination of the dictionary elements. In recent years there have been relevant efforts for searching data representation which are based on sparse dictionary elements or a sparse linear combination of the dictionary elements. Here we investigate the possibility to combine the advantages of both sparse dictionary elements and sparse linear combination. Notably, we also impose a structure on the dictionary elements. We compare our algorithm with two other different approaches presented in literature which impose either sparse structured dictionary elements or sparse linear combination. These (preliminary) results suggests that our approach presents some promising advantages, in particular a greater possibility of interpreting the data representation
A connectionist architecture for view-independent grip-aperture computation
No full textAuthors explore the problem of extracting invariants from visual stimuli and how the brain performs this task
How direct is Perception of Affordances? A Computational investigation of Grasping Affordances
No full textThe computational model presented here, Grasping Affordances
(GA) model, provides a precise explication of the notion
of affordance in the context of grasping actions carried
out by monkeys. This explication is consistent with both direct
perception theories and neuroscientific models of monkey
brains, insofar as the identification of grasping affordances
requires, according to this model, neither object recognition
processes nor access to semantic memory. Nevertheless, this
model posits a cascade of complicated computational processes,
in the way of visuo-motor transformations, which suggest
the advisability of qualifying and re-interpreting the claim
that (grasping) affordances are directly available to an acting
biological system. This re-interpretation undermines the alleged
alternative between direct and indirect perception theories,
to the extent that substantive visuo-motor transformations
have to be posited in order to identify grasping affordance
From motor to sensory processing in mirror neuron computational modelling
No full textTypical patterns of hand-joint covariation arising
in the context of grasping actions enable one to provide simplified
descriptions of these actions in terms of small sets of
hand-joint parameters. The computational model of mirror
mechanisms introduced here hypothesizes that mirror neurons
are crucially involved in coding and making this simplified
motor information available for both action recognition
and control processes. In particular, grasping action recognition
processes are modeled in terms of a visuo-motor loop
enabling one to make iterated use of mirror-coded motor
information. In simulation experiments concerning the classification
of reach-to-grasp actions, mirror-coded information
was found to simplify the processing of visual inputs
and to improve action recognition results with respect to recognition
procedures that are solely based on visual processing.
The visuo-motor loop involved in action recognition is
a distinctive feature of this model which is coherent with the
direct matching hypothesis. Moreover, the visuo-motor loop
sets the model introduced here apart from those computational
models that identify mirror neuron activity in action
observation with the final outcome of computational processes
unidirectionally flowing from sensory (and usually
visual) to motor systems
Perceiving affordances: a computational investigation of grasping affordances
No full textThe Grasping Affordance Model (GAM) introduced here provides a computational account of perceptual processes enabling one to
identify grasping action possibilities from visual scenes. GAM identifies the core of affordance perception with visuo-motor transformations
enabling one to associate features of visually presented objects to a collection of hand grasping configurations. This account is
coherent with neuroscientific models of relevant visuo-motor functions and their localization in the monkey brain. GAM differs from
other computational models of biological grasping affordances in the way of modeling focus, functional account, and tested abilities.
Notably, by learning to associate object features to hand shapes, GAM generalizes its grasp identification abilities to a variety of previously
unseen objects. Even though GAM information processing does not involve semantic memory access and full-fledged object recognition,
perceptions of (grasping) affordances are mediated there by substantive computational mechanisms which include learning of
object parts, selective analysis of visual scenes, and guessing from experience
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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