Unsupervised texture segmentation using Gabor filters
Anil K. Jain,Farshid Farrokhnia +1 more
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TLDR
A texture segmentation algorithm inspired by the multi-channel filtering theory for visual information processing in the early stages of human visual system is presented, which is based on reconstruction of the input image from the filtered images.About:
This article is published in Pattern Recognition.The article was published on 1991-12-01 and is currently open access. It has received 2351 citations till now. The article focuses on the topics: Image texture & Texture filtering.read more
Citations
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Journal ArticleDOI
A robust region-adaptive dual image watermarking technique
TL;DR: A novel watermarking technique is proposed using a region-adaptive approach to further improve upon criteria and shows the robustness of the watermark data to image processing attacks and geometric attacks is improved, validating the earlier hypothesis.
Journal ArticleDOI
Wavelet-based fractal signature analysis for automatic target recognition
TL;DR: A new multichannel texture model is introduced that characterizes patterns as 2-D functions in a Besov space and generates an n-dimensional surface, which is used for classification.
Proceedings ArticleDOI
Convolutional networks for voting-based anomaly classification in metal surface inspection
TL;DR: This work proposes to use deep Convolutional Neural Networks learnt from the large ImageNet dataset for image representations via transfer learning, and proposes a Majority Voting Mechanism (MVM), which fuses the features extracted from the last three layers of ConvNets using Support Vector Machine (SVM) classifiers.
Journal ArticleDOI
Independent component analysis for texture segmentation
Robert Jenssen,Torbjørn Eltoft +1 more
TL;DR: It is shown that the ICA filters are able to capture the inherent properties of textured images that enable them to be used to create energy features for effective texture segmentation.
Journal ArticleDOI
Multiresolution segmentation of natural images: from linear to nonlinear scale-space representations
TL;DR: A framework that merges classical ideas borrowed from scale-space and multiresolution segmentation with nonlinear partial differential equations is introduced and a tree of coherent segments is constructed based on relationships between different scale layers.
References
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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.
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Statistical and structural approaches to texture
TL;DR: This survey reviews the image processing literature on the various approaches and models investigators have used for texture, including statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models.
Journal ArticleDOI
Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters.
TL;DR: Evidence is presented that the 2D receptive-field profiles of simple cells in mammalian visual cortex are well described by members of this optimal 2D filter family, and thus such visual neurons could be said to optimize the general uncertainty relations for joint 2D-spatial-2D-spectral information resolution.