Kirjojen hintavertailu – 12 903 725 kirjaa ja 27 kauppaa

Kirjailija

Chen Ke

Kirjat ja teokset yhdessä paikassa: 3 kirjaa, julkaisuja vuosilta 2011–2026, suosituimpiin kuuluu Fancy Dream. Vertaile teosten hintoja ja tarkista saatavuus suomalaisista kirjakaupoista.

3 kirjaa

Kirjojen julkaisuvuodet: 2011–2026.

Fancy Dream

Fancy Dream

Eleonora Battiston; Zhu Tong; Chen Ke; Feng Zhengquan; Feng Shu; Han Jajuan; Jiang Zhi; Liu Ding; Liu Wei; Wang Qiang; Wang Wei

Damiani
2011
sidottu
This collective work recently showed in Beijing, merges into a multicoloured playground: installations, videos, photographs and traditional forms entwine so that the barriers and the borders among them melt away. Work from 10 young artists.
Mathematical Modeling and Essential Regularization for Imaging Applications
To deal with an increasingly large and sophisticated class of real life problems, image processing methods range from the traditional filtering and thresholding techniques to advanced variational models and deep learning algorithms. Regularization is a key concept in developing a variational model to ensure that a model has at least one solution and hence efforts in devising efficient algorithms worthwhile. High order and nonlocal regularization is particularly important, especially when the underlying problem (i.e. input image) requires one to minimize intensity differences within a large neighbourhood (e.g. beyond immediate voxels) for smoothness consideration. This Element aims to survey, review and discuss the state of the art techniques towards the latter kind of methods, emphasizing foundations, algorithms (and codes) and open challenges of high order and nonlocal regularizers for imaging tasks in commonly practised application scenarios.
Mathematical Modeling and Essential Regularization for Imaging Applications
To deal with an increasingly large and sophisticated class of real life problems, image processing methods range from the traditional filtering and thresholding techniques to advanced variational models and deep learning algorithms. Regularization is a key concept in developing a variational model to ensure that a model has at least one solution and hence efforts in devising efficient algorithms worthwhile. High order and nonlocal regularization is particularly important, especially when the underlying problem (i.e. input image) requires one to minimize intensity differences within a large neighbourhood (e.g. beyond immediate voxels) for smoothness consideration. This Element aims to survey, review and discuss the state of the art techniques towards the latter kind of methods, emphasizing foundations, algorithms (and codes) and open challenges of high order and nonlocal regularizers for imaging tasks in commonly practised application scenarios.