13,689 research outputs found
Human and Object Recognition with a High-resolution tactile sensor
This paper 1 describes the use of two artificial intelligence methods for object
recognition via pressure images from a high-resolution tactile sensor. Both meth-
ods follow the same procedure of feature extraction and posterior classification
based on a supervised Supported Vector Machine (SVM). The two approaches
differ on how features are extracted: while the first one uses the Speeded-Up
Robust Features (SURF) descriptor, the other one employs a pre-trained Deep
Convolutional Neural Network (DCNN). Besides, this work shows its applica-
tion to object recognition for rescue robotics, by distinguishing between differ-
ent body parts and inert objects. The performance analysis of the proposed
methods is carried out with an experiment with 5-class non-human and 3-class
human classification, providing a comparison in terms of accuracy and compu-tational load. Finally, it is discussed how feature-extraction based on SURF can be obtained up to five times faster compared to DCNN. On the other hand, the
accuracy achieved using DCNN-based feature extraction can be 11.67% superior
to SURF.Proyecto DPI2015-65186-R
European Commission under grant agreement BES-2016-078237.
Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
Constructing Krall-Hahn orthogonal polynomials
Given a sequence of polynomials , an algebra of operators acting in the linear space of polynomials and an operator with , where is any arbitrary eigenvalue,
we construct a new sequence of polynomials by considering a linear
combination of consecutive :
. Using the concept of
-operator, we determine the structure of the sequences
in order that the polynomials are
eigenfunctions of an operator in the algebra . As an application,
from the classical discrete family of Hahn polynomials we construct orthogonal
polynomials which are also eigenfunctions of higher-order difference
operators.Comment: 26 pages. arXiv admin note: text overlap with arXiv:1307.1326,
arXiv:1407.697
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