1.

Record Nr.

UNIORUON00036358

Autore

ASRORI, V.

Titolo

Ejodieti dahanakii xalqi tojik / V. Asrori, R. Amonov

Pubbl/distr/stampa

Dusanbe, : Maorif, 1980 303 p. ; 22 cm

Classificazione

AC VI AD

Altri autori (Persone)

AMONOV, R.

Soggetti

LETTERATURA POPOLARE - ASIA CENTRALE (TAGIKISTAN)

Lingua di pubblicazione

Tajik

Formato

Materiale a stampa

Livello bibliografico

Monografia

2.

Record Nr.

UNINA9910299297103321

Autore

Liu Huaping

Titolo

Robotic Tactile Perception and Understanding : A Sparse Coding Method / / by Huaping Liu, Fuchun Sun

Pubbl/distr/stampa

Singapore : , : Springer Singapore : , : Imprint : Springer, , 2018

ISBN

981-10-6171-8

Edizione

[1st ed. 2018.]

Descrizione fisica

1 online resource (220 pages)

Disciplina

629.892

Soggetti

Artificial intelligence

Pattern perception

Optical data processing

Artificial Intelligence

Pattern Recognition

Image Processing and Computer Vision

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia



Sommario/riassunto

This book introduces the challenges of robotic tactile perception and task understanding, and describes an advanced approach based on machine learning and sparse coding techniques. Further, a set of structured sparse coding models is developed to address the issues of dynamic tactile sensing. The book then proves that the proposed framework is effective in solving the problems of multi-finger tactile object recognition, multi-label tactile adjective recognition and multi-category material analysis, which are all challenging practical problems in the fields of robotics and automation. The proposed sparse coding model can be used to tackle the challenging visual-tactile fusion recognition problem, and the book develops a series of efficient optimization algorithms to implement the model. It is suitable as a reference book for graduate students with a basic knowledge of machine learning as well as professional researchers interested in robotic tactile perception and understanding, and machine learning.