Texture Classification Using Sparse Frame-Based Representations
نویسندگان
چکیده
منابع مشابه
Texture Classification Using Sparse Frame-Based Representations
A new method for supervised texture classification, denoted by frame texture classification method (FTCM), is proposed. The method is based on a deterministic texture model in which a small image block, taken from a texture region, is modeled as a sparse linear combination of frame elements. FTCM has two phases. In the design phase a frame is trained for each texture class based on given textur...
متن کاملFrame representations for texture segmentation
We introduce a novel method of feature extraction for texture segmentation that relies on multichannel wavelet frames and 2-D envelope detection. We describe and compare two algorithms for envelope detection based on (1) the Hilbert transform and (2) zero crossings. We present criteria for filter selection and discuss quantitatively their effect on feature extraction. The performance of our met...
متن کاملLaine and Fan : Frame Representations for Texture
| We introduce a novel method of feature extraction for texture segmentation that relies on multi-channel wavelet frames and two-dimensional envelope detection. We describe and compare two algorithms for envelope detection based on (1) the Hilbert transform and (2) zero-crossings. We present criteria for lter selection and discuss quantitatively their eeect on feature extraction. The performanc...
متن کاملMultiple Sparse Representations Classification
Sparse representations classification (SRC) is a powerful technique for pixelwise classification of images and it is increasingly being used for a wide variety of image analysis tasks. The method uses sparse representation and learned redundant dictionaries to classify image pixels. In this empirical study we propose to further leverage the redundancy of the learned dictionaries to achieve a mo...
متن کاملCorrection: Multiple Sparse Representations Classification
Copyright: © 2015 Plenge et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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: EURASIP Journal on Advances in Signal Processing
سال: 2006
ISSN: 1687-6172,1687-6180
DOI: 10.1155/asp/2006/52561