Tasuta kohaletoimetamine tellimustele üle 29 €
  • check 10+ miljonit raamatut
  • check Uued tooted iga päev
  • check Meid usaldab üle 1 miljoni kliendi
  • check Hea hind ja allahindlused
  • check Tarne üle kogu Euroopa

Probabilistic and Biologically Inspired Feature Representations - Michael Felsberg

inglise keel
2018-05-29
57,23 € 95,38 €

-40% koodiga BOOKS

Meie tarnija laos

Saadetis 12-18 tööpäeva jooksul

30-päevane tagastamisõigus

Under the title "Probabilistic and Biologically Inspired Feature Representations," this text collects a substantial amount of work on the topic of channel representations. Channel representations are a biologically motivated, wavelet-like approach to visual feature descriptors: they are local and compact, they form a computational framework, and the represented information can be reconstructed. The first pr ... Täielik kirjeldus

Võib-olla meeldib sulle ka

Kirjeldus

Under the title "Probabilistic and Biologically Inspired Feature Representations," this text collects a substantial amount of work on the topic of channel representations. Channel representations are a biologically motivated, wavelet-like approach to visual feature descriptors: they are local and compact, they form a computational framework, and the represented information can be reconstructed. The first property is shared with many histogram- and signature-based descriptors, the latter property with the related concept of population codes. In their unique combination of properties, channel representations become a visual Swiss army knife¿they can be used for image enhancement, visual object tracking, as 2D and 3D descriptors, and for pose estimation. In the chapters of this text, the framework of channel representations will be introduced and its attributes will be elaborated, as well as further insight into its probabilistic modeling and algorithmic implementation will be given. Channel representations are a useful toolbox to represent visual information for machine learning, as they establish a generic way to compute popular descriptors such as HOG, SIFT, and SHOT. Even in an age of deep learning, they provide a good compromise between hand-designed descriptors and a-priori structureless feature spaces as seen in the layers of deep networks.

Lisateave

Autor Michael Felsberg
Kirjastaja Springer Nature Switzerland
Series Synthesis Lectures on Computer Vision
Väljalaskeaasta 2018
Kaanetüüp Pehme kaanega
EAN 9783031006944
Kirjuta oma arvustus
Te vaatate: Probabilistic and Biologically Inspired Feature Representations
Teie hinnang:

Goodreads'i arvustused

57,23 € 95,38 €