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
Image segmentation using a texture gradient based watershed transform
TL;DR: A novel marker location algorithm is subsequently used to locate significant homogeneous textured or non textured regions and a marker driven watershed transform is then used to segment the identified regions properly.
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A theory of proximity based clustering: structure detection by optimization
TL;DR: A set of axioms is proposed and discussed to distinguish different cluster compactness and separation criteria, and is extended to nested partitionings for hierarchical data clustering in the case of sparse proximity matrices.
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Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images
TL;DR: A new method for extracting roads from high-resolution imagery based on hierarchical graph-based image segmentation, which demonstrates the validity and superior performance of the proposed method for road extraction in urban areas.
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Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor.
TL;DR: CoLlAGe’s utility in three clinically challenging classification problems is demonstrated: distinguishing radiation necrosis, a benign yet confounding effect of radiation treatment, from recurrent tumors on T1-w MRI in 42 brain tumor patients, and for each of these classification problems, CoLlAGE in conjunction with a random forest classifier outperformed state of the art radiomic descriptors.
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Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology
Rohit Bhargava,Anant Madabhushi +1 more
TL;DR: This work discusses some emerging innovations in both computational image analytics and imaging instrumentation in DP, and describes multimodal DP, which is enabled by computational algorithms and combines the best characteristics of structural and molecular pathology.
References
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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.
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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.