10,372 research outputs found
Feature-driven Emergence of Model Graphs for Object Recognition and Categorization
An important requirement for the expression of cognitive structures
is the ability to form mental objects by rapidly binding together
constituent parts. In this sense, one may conceive the brain\u27s data
structure to have the form of graphs whose nodes are labeled with
elementary features. These provide a versatile data format with the
additional ability to render the structure of any mental object.
Because of the multitude of possible object variations the graphs
are required to be dynamic. Upon presentation of an image a
so-called model graph should rapidly emerge by binding together
memorized subgraphs derived from earlier learning examples driven by the
image features. In this model, the richness and flexibility of the
mind is made possible by a combinatorical game of immense
complexity. Consequently, the emergence of model graphs is a
laborious task which, in computer vision, has most often been
disregarded in favor of employing model graphs tailored to specific
object categories like, for instance, faces in frontal pose.
Recognition or categorization of arbitrary objects, however, demands
dynamic graphs.
In this work we propose a form of graph dynamics, which proceeds in
two steps. In the first step component classifiers, which decide
whether a feature is present in an image, are learned from training
images. For processing arbitrary objects, features are small
localized grid graphs, so-called parquet graphs, whose nodes are
attributed with Gabor amplitudes. Through combination of these
classifiers into a linear discriminant that conforms to Linsker\u27s
infomax principle a weighted majority voting scheme is implemented.
It allows for preselection of salient learning examples, so-called
model candidates, and likewise for preselection of categories the
object in the presented image supposably belongs to. Each model
candidate is verified in a second step using a variant of elastic
graph matching, a standard correspondence-based technique for face
and object recognition. To further differentiate between model
candidates with similar features it is asserted that the features be
in similar spatial arrangement for the model to be selected. Model
graphs are constructed dynamically by assembling model features into
larger graphs according to their spatial arrangement. From the
viewpoint of pattern recognition, the presented technique is a
combination of a discriminative (feature-based) and a generative
(correspondence-based) classifier while the majority voting scheme
implemented in the feature-based part is an extension of existing
multiple feature subset methods.
We report the results of experiments on standard databases for
object recognition and categorization. The method achieved high
recognition rates on identity, object category, pose, and
illumination type. Unlike many other models the presented
technique can also cope with varying background, multiple objects,
and partial occlusion
Self-organization via active exploration in robotic applications
We describe a neural network based robotic system. Unlike traditional robotic systems, our approach focussed on non-stationary problems. We indicate that self-organization capability is necessary for any system to operate successfully in a non-stationary environment. We suggest that self-organization should be based on an active exploration process. We investigated neural architectures having novelty sensitivity, selective attention, reinforcement learning, habit formation, flexible criteria categorization properties and analyzed the resulting behavior (consisting of an intelligent initiation of exploration) by computer simulations. While various computer vision researchers acknowledged recently the importance of active processes (Swain and Stricker, 1991), the proposed approaches within the new framework still suffer from a lack of self-organization (Aloimonos and Bandyopadhyay, 1987; Bajcsy, 1988). A self-organizing, neural network based robot (MAVIN) has been recently proposed (Baloch and Waxman, 1991). This robot has the capability of position, size rotation invariant pattern categorization, recognition and pavlovian conditioning. Our robot does not have initially invariant processing properties. The reason for this is the emphasis we put on active exploration. We maintain the point of view that such invariant properties emerge from an internalization of exploratory sensory-motor activity. Rather than coding the equilibria of such mental capabilities, we are seeking to capture its dynamics to understand on the one hand how the emergence of such invariances is possible and on the other hand the dynamics that lead to these invariances. The second point is crucial for an adaptive robot to acquire new invariances in non-stationary environments, as demonstrated by the inverting glass experiments of Helmholtz. We will introduce Pavlovian conditioning circuits in our future work for the precise objective of achieving the generation, coordination, and internalization of sequence of actions
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Transitivity Recovering Decompositions: Interpretable and Robust Fine-Grained Relationships
Recent advances in fine-grained representation learning leverage
local-to-global (emergent) relationships for achieving state-of-the-art
results. The relational representations relied upon by such methods, however,
are abstract. We aim to deconstruct this abstraction by expressing them as
interpretable graphs over image views. We begin by theoretically showing that
abstract relational representations are nothing but a way of recovering
transitive relationships among local views. Based on this, we design
Transitivity Recovering Decompositions (TRD), a graph-space search algorithm
that identifies interpretable equivalents of abstract emergent relationships at
both instance and class levels, and with no post-hoc computations. We
additionally show that TRD is provably robust to noisy views, with empirical
evidence also supporting this finding. The latter allows TRD to perform at par
or even better than the state-of-the-art, while being fully interpretable.
Implementation is available at https://github.com/abhrac/trd.Comment: Neural Information Processing Systems (NeurIPS) 202
Common spatiotemporal processing of visual features shapes object representation
none10Biological vision relies on representations of the physical world at different levels of complexity. Relevant features span from simple low-level properties, as contrast and spatial frequencies, to object-based attributes, as shape and category. However, how these features are integrated into coherent percepts is still debated. Moreover, these dimensions often share common biases: for instance, stimuli from the same category (e.g., tools) may have similar shapes. Here, using magnetoencephalography, we revealed the temporal dynamics of feature processing in human subjects attending to objects from six semantic categories. By employing Relative Weights Analysis, we mitigated collinearity between model-based descriptions of stimuli and showed that low-level properties (contrast and spatial frequencies), shape (medial-axis) and category are represented within the same spatial locations early in time: 100-150 ms after stimulus onset. This fast and overlapping processing may result from independent parallel computations, with categorical representation emerging later than the onset of low-level feature processing, yet before shape coding. Categorical information is represented both before and after shape, suggesting a role for this feature in the refinement of categorical matching.nonePapale, Paolo; Betta, Monica; Handjaras, Giacomo; Malfatti, Giulia; Cecchetti, Luca; Rampinini, Alessandra; Pietrini, Pietro; Ricciardi, Emiliano; Turella, Luca; Leo, AndreaPapale, Paolo; Betta, Monica; Handjaras, Giacomo; Malfatti, Giulia; Cecchetti, Luca; Rampinini, Alessandra; Pietrini, Pietro; Ricciardi, Emiliano; Turella, Luca; Leo, Andre
06031 Abstracts Collection -- Organic Computing -- Controlled Emergence
Organic Computing has emerged recently as a challenging vision for
future information processing systems, based on the insight that we
will soon be surrounded by large collections of autonomous systems
equipped with sensors and actuators to be aware of their environment,
to communicate freely, and to organize themselves in order to perform
the actions and services required. Organic Computing Systems will
adapt dynamically to the current conditions of its environment, they
will be self-organizing, self-configuring, self-healing,
self-protecting, self-explaining, and context-aware.
From 15.01.06 to 20.01.06, the Dagstuhl Seminar 06031 ``Organic
Computing -- Controlled Emergence\u27\u27 was held in the International
Conference and Research Center (IBFI), Schloss Dagstuhl.
The seminar was characterized by the very constructive search for
common ground between engineering and natural sciences, between
informatics on the one hand and biology, neuroscience, and chemistry
on the other. The common denominator was the objective to build
practically usable self-organizing and emergent systems or their
components.
An indicator for the practical orientation of the seminar was the
large number of OC application systems, envisioned or already under
implementation, such as the Internet, robotics, wireless sensor
networks, traffic control, computer vision, organic systems on chip,
an adaptive and self-organizing room with intelligent sensors or
reconfigurable guiding systems for smart office buildings. The
application orientation was also apparent by the large number of
methods and tools presented during the seminar, which might be used as
building blocks for OC systems, such as an evolutionary design
methodology, OC architectures, especially several implementations of
observer/controller structures, measures and measurement tools for
emergence and complexity, assertion-based methods to control
self-organization, wrappings, a software methodology to build
reflective systems, and components for OC middleware.
Organic Computing is clearly oriented towards applications but is
augmented at the same time by more theoretical bio-inspired and
nature-inspired work, such as chemical computing, theory of complex
systems and non-linear dynamics, control mechanisms in insect swarms,
homeostatic mechanisms in the brain, a quantitative approach to
robustness, abstraction and instantiation as a central metaphor for
understanding complex systems.
Compared to its beginnings, Organic Computing is coming of age. The OC
vision is increasingly padded with meaningful applications and usable
tools, but the path towards full OC systems is still complex. There is
progress in a more scientific understanding of emergent processes. In
the future, we must understand more clearly how to open the
configuration space of technical systems for on-line
modification. Finally, we must make sure that the human user remains
in full control while allowing the systems to optimize
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