26,997 research outputs found
Learning and Matching Multi-View Descriptors for Registration of Point Clouds
Critical to the registration of point clouds is the establishment of a set of
accurate correspondences between points in 3D space. The correspondence problem
is generally addressed by the design of discriminative 3D local descriptors on
the one hand, and the development of robust matching strategies on the other
hand. In this work, we first propose a multi-view local descriptor, which is
learned from the images of multiple views, for the description of 3D keypoints.
Then, we develop a robust matching approach, aiming at rejecting outlier
matches based on the efficient inference via belief propagation on the defined
graphical model. We have demonstrated the boost of our approaches to
registration on the public scanning and multi-view stereo datasets. The
superior performance has been verified by the intensive comparisons against a
variety of descriptors and matching methods
A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems
We propose a set of compositional design patterns to describe a large variety
of systems that combine statistical techniques from machine learning with
symbolic techniques from knowledge representation. As in other areas of
computer science (knowledge engineering, software engineering, ontology
engineering, process mining and others), such design patterns help to
systematize the literature, clarify which combinations of techniques serve
which purposes, and encourage re-use of software components. We have validated
our set of compositional design patterns against a large body of recent
literature.Comment: 12 pages,55 reference
Recommended from our members
Examination of Bayesian belief network for safety assessment of nuclear computer-based systems
We report here on a continuation of work on the Bayesian Belief Network (BBN)model described in [Fenton, Littlewood et al. 1998]. As explained in the previous deliverable, our model concerns one part of the safety assessment task for computer and software based nuclear systems. We have produced a first complete, functioning version of our BBN model by eliciting a large numerical node probability table (NPT) required for our ‘Design Process Performance’ variable. The requirement for such large numerical NPTs poses some difficult questions about how, in general, large NPTs should be elicited from domain experts. We report about the methods we have devised to support the expert in building and validating a BBN. On the one hand, we have proceeded by eliciting approximate descriptions of the expert’s probabilistic beliefs, in terms of properties like stochastic orderings among distributions; on the other hand, we have explored ways of presenting to the expert visual and algebraic descriptions of relations among variables in the BBN, to assist the expert in an ongoing assessment of the validity of the BBN
Higher-level Knowledge, Rational and Social Levels Constraints of the Common Model of the Mind
In his famous 1982 paper, Allen Newell [22, 23] introduced the notion of knowledge level to
indicate a level of analysis, and prediction, of the rational behavior of a cognitive articial agent.
This analysis concerns the investigation about the availability of the agent knowledge, in order
to pursue its own goals, and is based on the so-called Rationality Principle (an assumption
according to which "an agent will use the knowledge it has of its environment to achieve its
goals" [22, p. 17]. By using the Newell's own words: "To treat a system at the knowledge level
is to treat it as having some knowledge, some goals, and believing it will do whatever is within
its power to attain its goals, in so far as its knowledge indicates" [22, p. 13].
In the last decades, the importance of the knowledge level has been historically and system-
atically downsized by the research area in cognitive architectures (CAs), whose interests have
been mainly focused on the analysis and the development of mechanisms and the processes
governing human and (articial) cognition. The knowledge level in CAs, however, represents
a crucial level of analysis for the development of such articial general systems and therefore
deserves greater research attention [17]. In the following, we will discuss areas of broad agree-
ment and outline the main problematic aspects that should be faced within a Common Model
of Cognition [12]. Such aspects, departing from an analysis at the knowledge level, also clearly
impact both lower (e.g. representational) and higher (e.g. social) levels
- …