A note on the gradient of a multi-image
Computer Vision, Graphics, and Image Processing - Lectures notes in computer science, Vol. 201 (G. Goos and J. Hartmanis, Eds.)
A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Computer Vision, Graphics, and Image Processing
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital topology: introduction and survey
Computer Vision, Graphics, and Image Processing
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space for Discrete Signals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multichannel Texture Analysis Using Localized Spatial Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Localization Performance Measure and Optimal Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
Text segmentation using Gabor filters for automatic document processing
Machine Vision and Applications - Special issue: document image analysis techniques
3D imaging in medicine
An introduction to wavelets
3D digital topology under binary transformation with applications
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Multiscale Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Scale Control for Edge Detection and Blur Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Zoom-invariant vision of figural shape: the mathematics of cores
Computer Vision and Image Understanding
User-steered image segmentation paradigms: live wire and live lane
Graphical Models and Image Processing
Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Representation of Image Structures via Scale Space Entropy Conditions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-based fuzzy connected image segmentation: theory, algorithms, and validation
Computer Vision and Image Understanding - Special issue on analysis of volumetric image
Clustering by Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Curvature Estimation in Oriented Patterns Using Curvilinear Models Applied to Gradient Vector Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Robot Vision
Computer Vision: Advances and Applications
Computer Vision: Advances and Applications
Digital Image Processing
Digital Picture Processing
Computer Vision
Texture Segmentation using 2-D Gabor Elementary Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Optimal edge detection in two-dimensional images
IEEE Transactions on Image Processing
Multiscale image segmentation by integrated edge and region detection
IEEE Transactions on Image Processing
Space-frequency quantization for wavelet image coding
IEEE Transactions on Image Processing
Integer wavelet transform for embedded lossy to lossless image compression
IEEE Transactions on Image Processing
Curvature of n-dimensional space curves in grey-value images
IEEE Transactions on Image Processing
Strongly normal sets of contractible tiles in N dimensions
Pattern Recognition
Shape feature extraction and description based on tensor scale
Pattern Recognition
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Scale is a widely used notion in image analysis that evolved in the form of scale-space theory whose key idea is to represent and analyze an image at various resolutions. Recently, the notion of localized scale--a space-variant resolution scheme--has drawn significant research interest. Previously, we reported local morphometric scale using a spherical model. A major limitation of the spherical model is that it ignores structure orientation and anisotropy, and therefore fails to be optimal in many imaging applications including biomedical ones where structures are inherently anisotropic and have mixed orientations. Here, we introduce a new concept called "tensor scale"--a local morphometric parameter yielding a unified representation of structure size, orientation, and anisotropy. Also, a few applications of tensor scale in computer vision and image analysis, especially, in image filtering are illustrated. At any image point, its tensor scale is the parametric representation of the largest ellipse (in 2D) or ellipsoid (in 3D) centered at that point and contained in the same homogeneous region. An algorithmic framework to compute tensor scale at any image point is proposed and results of its application on several real images are presented. Also, performance of the tensor scale computation method under image rotation, varying pixel size, and background inhomogeneity is studied. Results of a quantitative analysis evaluating performance of the method on 2D brain phantom images at various levels of noise and blur, and a fixed background inhomogeneity are presented. Agreement between tensor scale images computed on matching image slices from two 3D magnetic resonance data acquired simultaneously using different protocols are demonstrated. Finally, the application of tensor scale in anisotropic diffusive image filtering is presented that encourages smoothing inside a homogeneous region and also along edges and elongated structures while discourages blurring across them. Both qualitative and quantitative results of application of the new filtering method have been presented and compared with the results obtained by spherical scale-based and standard diffusive filtering methods.