Journal ArticleDOI
Description of interest regions with local binary patterns
TLDR
A new texture feature called center-symmetric local binary pattern (CS-LBP) is introduced that is a modified version of the well-known localbinary pattern (LBP), and is computationally simpler than the SIFT.About:
This article is published in Pattern Recognition.The article was published on 2009-03-01. It has received 1172 citations till now. The article focuses on the topics: Local binary patterns & GLOH.read more
Citations
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Journal ArticleDOI
A Completed Modeling of Local Binary Pattern Operator for Texture Classification
TL;DR: It is shown that CLBP_S preserves more information of the local structure thanCLBP_M, which explains why the simple LBP operator can extract the texture features reasonably well and can be made for rotation invariant texture classification.
Journal ArticleDOI
A survey of the recent architectures of deep convolutional neural networks
TL;DR: Deep Convolutional Neural Networks (CNNs) as mentioned in this paper are a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing.
Proceedings ArticleDOI
Salient Object Detection: A Discriminative Regional Feature Integration Approach
TL;DR: This paper regards saliency map computation as a regression problem, which is based on multi-level image segmentation, and uses the supervised learning approach to map the regional feature vector to a saliency score, and finally fuses the saliency scores across multiple levels, yielding the salency map.
Journal ArticleDOI
WLD: A Robust Local Image Descriptor
TL;DR: Experimental results on the Brodatz and KTH-TIPS2-a texture databases show that WLD impressively outperforms the other widely used descriptors (e.g., Gabor and SIFT), and experimental results on human face detection also show a promising performance comparable to the best known results onThe MIT+CMU frontal face test set, the AR face data set, and the CMU profile test set.
Journal ArticleDOI
Local Binary Patterns and Its Application to Facial Image Analysis: A Survey
TL;DR: As a typical application of the LBP approach, LBP-based facial image analysis is extensively reviewed, while its successful extensions, which deal with various tasks of facial imageAnalysis, are also highlighted.
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.
Journal ArticleDOI
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.
Book ChapterDOI
SURF: speeded up robust features
TL;DR: A novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
BookDOI
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
TL;DR: Learning with Kernels provides an introduction to SVMs and related kernel methods that provide all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms.
Journal ArticleDOI
A performance evaluation of local descriptors
TL;DR: It is observed that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT-based descriptors perform best and Moments and steerable filters show the best performance among the low dimensional descriptors.