Steerable Wavelet Machines (SWM): Learning Moving Frames for Texture Classification
نویسندگان
چکیده
منابع مشابه
Learning Steerable Wavelet Frames
We present a functional framework for the adaptive design of dictionaries where the invariance to translation, dilation, and rotation is built upfront into the primary representation space. Our key idea is to build an invariant signal representation prior to the learning stage. By doing so, we focus our effort on adapting the dictionary to the distinctive features of the signal, rather than to ...
متن کاملTexture classification and segmentation using wavelet frames
This paper describes a new approach to the characterization of texture properties at multiple scales using the wavelet transform. The analysis uses an overcomplete wavelet decomposition, which yields a description that is translation invariant. It is shown that this representation constitutes a tight frame of l(2) and that it has a fast iterative algorithm. A texture is characterized by a set o...
متن کاملSteerable Pyramids and Tight Wavelet Frames in 2707
We present a functional framework for the design of tight steerable wavelet frames in any number of dimensions. The 2-D version of the method can be viewed as a generalization of Simoncelli’s steerable pyramid that gives access to a larger palette of steerable wavelets via a suitable parametrization. The backbone of our construction is a primal isotropic wavelet frame that provides the multires...
متن کاملMaximum Likelihood Texture Classification and Bayesian Texture Segmentation Using Discrete Wavelet Frames
In this work a new approach is presented for the classification and segmentation of texture images, where a different statistical methodology and criterion for texture characterization is proposed. The scheme, in both problems, uses the concept of Discrete Wavelet Frames for the appropriate frequency decompositions, as applied to 2-D signals, and a distance measure based on the evaluation of pa...
متن کاملWavelet Based Texture Classification
Textures are one of the basic features in visual searching and computational vision. In the literature, most of the attention has been focussed on the texture features with minimal consideration of the noise models. In this paper we investigated the problem of texture classification from a maximum likelihood perspective. We took into account the texture model, the noise distribution, and the in...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Image Processing
سال: 2017
ISSN: 1057-7149,1941-0042
DOI: 10.1109/tip.2017.2655438