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Viewpoint Invariant Texture Description Using Fractal Analysis

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TLDR
The multifractal spectrum (MFS) is introduced, a new texture signature that is invariant under the bi-Lipschitz map, which includes view-point changes and non-rigid deformations of the texture surface, as well as local affine illumination changes.
Abstract
Image texture provides a rich visual description of the surfaces in the scene. Many texture signatures based on various statistical descriptions and various local measurements have been developed. Existing signatures, in general, are not invariant to 3D geometric transformations, which is a serious limitation for many applications. In this paper we introduce a new texture signature, called the multifractal spectrum (MFS). The MFS is invariant under the bi-Lipschitz map, which includes view-point changes and non-rigid deformations of the texture surface, as well as local affine illumination changes. It provides an efficient framework combining global spatial invariance and local robust measurements. Intuitively, the MFS could be viewed as a "better histogram" with greater robustness to various environmental changes and the advantage of capturing some geometrical distribution information encoded in the texture. Experiments demonstrate that the MFS codes the essential structure of textures with very low dimension, and thus represents an useful tool for texture classification.

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Citations
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Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

David Forsyth, +1 more
TL;DR: Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance and describes numerous important application areas such as image based rendering and digital libraries.
Proceedings ArticleDOI

Describing Textures in the Wild

TL;DR: This work identifies a vocabulary of forty-seven texture terms and uses them to describe a large dataset of patterns collected "in the wild", and shows that they both outperform specialized texture descriptors not only on this problem, but also in established material recognition datasets.
Journal ArticleDOI

From BoW to CNN: Two Decades of Texture Representation for Texture Classification

TL;DR: More than 250 major publications are cited in this survey covering different aspects of the research, including benchmark datasets and state-of-the-art results as discussed by the authors, in retrospect of what has been achieved so far and open challenges and directions for future research.
Posted Content

Deep filter banks for texture recognition, description, and segmentation

TL;DR: In this article, a human-interpretable vocabulary of texture attributes is proposed to describe common texture patterns, complemented by a new describable texture dataset for benchmarking, and the problem of recognizing materials and texture attributes in realistic imaging conditions is addressed.
Journal ArticleDOI

Deep Filter Banks for Texture Recognition, Description, and Segmentation

TL;DR: The authors proposed a human-interpretable vocabulary of texture attributes to describe common texture patterns, complemented by a new describable texture dataset for benchmarking, and showed that these have excellent efficiency and generalization properties if the convolutional layers of a deep model are used as filter banks.
References
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Journal ArticleDOI

Distinctive Image Features from Scale-Invariant Keypoints

TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Book

The Fractal Geometry of Nature

TL;DR: This book is a blend of erudition, popularization, and exposition, and the illustrations include many superb examples of computer graphics that are works of art in their own right.
Journal ArticleDOI

A theory for multiresolution signal decomposition: the wavelet representation

TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.

Distinctive Image Features from Scale-Invariant Keypoints

TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Journal ArticleDOI

Shape matching and object recognition using shape contexts

TL;DR: This paper presents work on computing shape models that are computationally fast and invariant basic transformations like translation, scaling and rotation, and proposes shape detection using a feature called shape context, which is descriptive of the shape of the object.
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