69,131 research outputs found
Experience-driven formation of parts-based representations in a model of layered visual memory
Growing neuropsychological and neurophysiological evidence suggests that the
visual cortex uses parts-based representations to encode, store and retrieve
relevant objects. In such a scheme, objects are represented as a set of
spatially distributed local features, or parts, arranged in stereotypical
fashion. To encode the local appearance and to represent the relations between
the constituent parts, there has to be an appropriate memory structure formed
by previous experience with visual objects. Here, we propose a model how a
hierarchical memory structure supporting efficient storage and rapid recall of
parts-based representations can be established by an experience-driven process
of self-organization. The process is based on the collaboration of slow
bidirectional synaptic plasticity and homeostatic unit activity regulation,
both running at the top of fast activity dynamics with winner-take-all
character modulated by an oscillatory rhythm. These neural mechanisms lay down
the basis for cooperation and competition between the distributed units and
their synaptic connections. Choosing human face recognition as a test task, we
show that, under the condition of open-ended, unsupervised incremental
learning, the system is able to form memory traces for individual faces in a
parts-based fashion. On a lower memory layer the synaptic structure is
developed to represent local facial features and their interrelations, while
the identities of different persons are captured explicitly on a higher layer.
An additional property of the resulting representations is the sparseness of
both the activity during the recall and the synaptic patterns comprising the
memory traces.Comment: 34 pages, 12 Figures, 1 Table, published in Frontiers in
Computational Neuroscience (Special Issue on Complex Systems Science and
Brain Dynamics),
http://www.frontiersin.org/neuroscience/computationalneuroscience/paper/10.3389/neuro.10/015.2009
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
table
A Framework for Symmetric Part Detection in Cluttered Scenes
The role of symmetry in computer vision has waxed and waned in importance
during the evolution of the field from its earliest days. At first figuring
prominently in support of bottom-up indexing, it fell out of favor as shape
gave way to appearance and recognition gave way to detection. With a strong
prior in the form of a target object, the role of the weaker priors offered by
perceptual grouping was greatly diminished. However, as the field returns to
the problem of recognition from a large database, the bottom-up recovery of the
parts that make up the objects in a cluttered scene is critical for their
recognition. The medial axis community has long exploited the ubiquitous
regularity of symmetry as a basis for the decomposition of a closed contour
into medial parts. However, today's recognition systems are faced with
cluttered scenes, and the assumption that a closed contour exists, i.e. that
figure-ground segmentation has been solved, renders much of the medial axis
community's work inapplicable. In this article, we review a computational
framework, previously reported in Lee et al. (2013), Levinshtein et al. (2009,
2013), that bridges the representation power of the medial axis and the need to
recover and group an object's parts in a cluttered scene. Our framework is
rooted in the idea that a maximally inscribed disc, the building block of a
medial axis, can be modeled as a compact superpixel in the image. We evaluate
the method on images of cluttered scenes.Comment: 10 pages, 8 figure
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Knowledge representation for basic visual categories
This paper reports work on a model of machine learning which is based on the psychological theory of prototypical concepts. This theory is that concepts learnt naturally from interaction with the environment (basic categories) are not structured or defined in logical terms but are clustered in accordance with their similarity to a central prototype, representing the "most typical'' member
Construction of Latent Descriptor Space and Inference Model of Hand-Object Interactions
Appearance-based generic object recognition is a challenging problem because
all possible appearances of objects cannot be registered, especially as new
objects are produced every day. Function of objects, however, has a
comparatively small number of prototypes. Therefore, function-based
classification of new objects could be a valuable tool for generic object
recognition. Object functions are closely related to hand-object interactions
during handling of a functional object; i.e., how the hand approaches the
object, which parts of the object and contact the hand, and the shape of the
hand during interaction. Hand-object interactions are helpful for modeling
object functions. However, it is difficult to assign discrete labels to
interactions because an object shape and grasping hand-postures intrinsically
have continuous variations. To describe these interactions, we propose the
interaction descriptor space which is acquired from unlabeled appearances of
human hand-object interactions. By using interaction descriptors, we can
numerically describe the relation between an object's appearance and its
possible interaction with the hand. The model infers the quantitative state of
the interaction from the object image alone. It also identifies the parts of
objects designed for hand interactions such as grips and handles. We
demonstrate that the proposed method can unsupervisedly generate interaction
descriptors that make clusters corresponding to interaction types. And also we
demonstrate that the model can infer possible hand-object interactions
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