72,960 research outputs found
Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields
This work presents a first evaluation of using spatio-temporal receptive
fields from a recently proposed time-causal spatio-temporal scale-space
framework as primitives for video analysis. We propose a new family of video
descriptors based on regional statistics of spatio-temporal receptive field
responses and evaluate this approach on the problem of dynamic texture
recognition. Our approach generalises a previously used method, based on joint
histograms of receptive field responses, from the spatial to the
spatio-temporal domain and from object recognition to dynamic texture
recognition. The time-recursive formulation enables computationally efficient
time-causal recognition. The experimental evaluation demonstrates competitive
performance compared to state-of-the-art. Especially, it is shown that binary
versions of our dynamic texture descriptors achieve improved performance
compared to a large range of similar methods using different primitives either
handcrafted or learned from data. Further, our qualitative and quantitative
investigation into parameter choices and the use of different sets of receptive
fields highlights the robustness and flexibility of our approach. Together,
these results support the descriptive power of this family of time-causal
spatio-temporal receptive fields, validate our approach for dynamic texture
recognition and point towards the possibility of designing a range of video
analysis methods based on these new time-causal spatio-temporal primitives.Comment: 29 pages, 16 figure
Combining Static and Dynamic Contract Checking for Curry
Static type systems are usually not sufficient to express all requirements on
function calls. Hence, contracts with pre- and postconditions can be used to
express more complex constraints on operations. Contracts can be checked at run
time to ensure that operations are only invoked with reasonable arguments and
return intended results. Although such dynamic contract checking provides more
reliable program execution, it requires execution time and could lead to
program crashes that might be detected with more advanced methods at compile
time. To improve this situation for declarative languages, we present an
approach to combine static and dynamic contract checking for the functional
logic language Curry. Based on a formal model of contract checking for
functional logic programming, we propose an automatic method to verify
contracts at compile time. If a contract is successfully verified, dynamic
checking of it can be omitted. This method decreases execution time without
degrading reliable program execution. In the best case, when all contracts are
statically verified, it provides trust in the software since crashes due to
contract violations cannot occur during program execution.Comment: Pre-proceedings paper presented at the 27th International Symposium
on Logic-Based Program Synthesis and Transformation (LOPSTR 2017), Namur,
Belgium, 10-12 October 2017 (arXiv:1708.07854
Data-driven Soft Sensors in the Process Industry
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work
Applied Type System: An Approach to Practical Programming with Theorem-Proving
The framework Pure Type System (PTS) offers a simple and general approach to
designing and formalizing type systems. However, in the presence of dependent
types, there often exist certain acute problems that make it difficult for PTS
to directly accommodate many common realistic programming features such as
general recursion, recursive types, effects (e.g., exceptions, references,
input/output), etc. In this paper, Applied Type System (ATS) is presented as a
framework for designing and formalizing type systems in support of practical
programming with advanced types (including dependent types). In particular, it
is demonstrated that ATS can readily accommodate a paradigm referred to as
programming with theorem-proving (PwTP) in which programs and proofs are
constructed in a syntactically intertwined manner, yielding a practical
approach to internalizing constraint-solving needed during type-checking. The
key salient feature of ATS lies in a complete separation between statics, where
types are formed and reasoned about, and dynamics, where programs are
constructed and evaluated. With this separation, it is no longer possible for a
program to occur in a type as is otherwise allowed in PTS. The paper contains
not only a formal development of ATS but also some examples taken from
ats-lang.org, a programming language with a type system rooted in ATS, in
support of employing ATS as a framework to formulate advanced type systems for
practical programming
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