Titlebook: Geometrical Multiresolution Adaptive Transforms; Theory and Applicati Agnieszka Lisowska Book 2014 Springer International Publishing Switze

团结

https://doi.org/10.1007/978-94-007-6455-2 one is based on sliding multismoothlets. Both methods were compared to the state-of-the-art methods. As follows from the performed experiments, the method based on sliding multismoothlets leads to the best results of edge detection.

扩张

Felix Müller,Nicola Fohrer,Luis Chicharo In this book, the theory of multismoothlets was presented. This theory is the generalization of the concepts of geometrical multiresolution adaptive methods of image approximation proposed so far. Such generalization leads to new possibilities and applications to image processing and analysis. Ther

MIME

Harbor

鼓掌

Power of Collective Human Consciousness,In this chapter, the motivation of this book was presented based on the human visual system. Then, the state-of-the-art review was given of the geometrical multiresolution methods of image approximation together with the contribution of this book. The chapter ends with the outline of this book.

有毒

Introduction,In this chapter, the motivation of this book was presented based on the human visual system. Then, the state-of-the-art review was given of the geometrical multiresolution methods of image approximation together with the contribution of this book. The chapter ends with the outline of this book.

协议

1860-949X ems in the area of geometrical multiresolution methods of im.Modern image processing techniques are based on multiresolution geometrical methods of image representation. These methods are efficient in sparse approximation of digital images. There is a wide family of functions called simply ‘X-lets’,

analogous

Marcel Meinders,Nico Van Breemenally, a notion of sliding multismoothlet was introduced. It is the multismoothlet with location and size defined freely within an image. Based on that, the shift invariant multismoothlet transform was proposed as well. The Rate-Distortion dependency and the .-term approximation of multismoothlets were also discussed.

肮脏

Biological Diversity: Where Is It?as followed by the results of numerical experiments. These results were further compared to the known state-of-the-art methods. The proposed algorithm assures the best denoising results in the most cases.

注入

Felix Müller,Nicola Fohrer,Luis Chicharomethods of image approximation proposed so far. Such generalization leads to new possibilities and applications to image processing and analysis. There is still plenty of work that can be done in this field. Some open problems are described in this section.