11,493 research outputs found
Building and Refining Abstract Planning Cases by Change of Representation Language
ion is one of the most promising approaches to improve the performance of
problem solvers. In several domains abstraction by dropping sentences of a
domain description -- as used in most hierarchical planners -- has proven
useful. In this paper we present examples which illustrate significant
drawbacks of abstraction by dropping sentences. To overcome these drawbacks, we
propose a more general view of abstraction involving the change of
representation language. We have developed a new abstraction methodology and a
related sound and complete learning algorithm that allows the complete change
of representation language of planning cases from concrete to abstract.
However, to achieve a powerful change of the representation language, the
abstract language itself as well as rules which describe admissible ways of
abstracting states must be provided in the domain model. This new abstraction
approach is the core of Paris (Plan Abstraction and Refinement in an Integrated
System), a system in which abstract planning cases are automatically learned
from given concrete cases. An empirical study in the domain of process planning
in mechanical engineering shows significant advantages of the proposed
reasoning from abstract cases over classical hierarchical planning.Comment: See http://www.jair.org/ for an online appendix and other files
accompanying this articl
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Methods of conceptual clustering and their relation to numerical taxonomy
Artificial Intelligence (AI) methods for machine learning can be viewed as forms of exploratory data analysis, even though they differ markedly from the statistical methods generally connoted by the term. The distinction between methods of machine learning and statistical data analysis is primarily due to differences in the way techniques of each type represent data and structure within data. That is, methods of machine learning are strongly biased toward symbolic (as opposed to numeric) data representations. We explore this difference within a limited context, devoting the bulk of our paper to the explication of conceptual clustering, an extension to the statistically based methods of numerical taxonomy. In conceptual clustering the formation of object clusters is dependent on the quality of 'higher-level' characterizations, termed concepts, of the clusters. The form of concepts used by existing conceptual clustering systems (sets of necessary and sufficient conditions) is described in some detail. This is followed by descriptions of several conceptual clustering techniques, along with sample output. We conclude with a discussion of how alternative concept representations might enhance the effectiveness of future conceptual clustering systems
An original framework for understanding human actions and body language by using deep neural networks
The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour.
By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way.
These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively.
While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements;
both are essential tasks in many computer vision applications, including event recognition, and video surveillance.
In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided.
The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements.
All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods
A Framework for Designing 3d Virtual Environments
The process of design and development of virtual environments can be supported by tools and frameworks, to save time in technical aspects and focusing on the content. In this paper we present an academic framework which provides several levels of abstraction to ease this work. It includes state-of-the-art components we devised or integrated adopting open-source solutions in order to face specific problems. Its architecture is modular and customizable, the code is open-source.\u
Correcting curvature-density effects in the Hamilton-Jacobi skeleton
The Hainilton-Jacobi approach has proven to be a powerful and elegant method for extracting the skeleton of two-dimensional (2-D) shapes. The approach is based on the observation that the normalized flux associated with the inward evolution of the object boundary at nonskeletal points tends to zero as the size of the integration area tends to zero, while the flux is negative at the locations of skeletal points. Nonetheless, the error in calculating the flux on the image lattice is both limited by the pixel resolution and also proportional to the curvature of the boundary evolution front and, hence, unbounded near endpoints. This makes the exact location of endpoints difficult and renders the performance of the skeleton extraction algorithm dependent on a threshold parameter. This problem can be overcome by using interpolation techniques to calculate the flux with subpixel precision. However, here, we develop a method for 2-D skeleton extraction that circumvents the problem by eliminating the curvature contribution to the error. This is done by taking into account variations of density due to boundary curvature. This yields a skeletonization algorithm that gives both better localization and less susceptibility to boundary noise and parameter choice than the Hamilton-Jacobi method
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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