Journal ArticleDOI
Advanced modeling of visual information processing: A multi-resolution directional-oriented image transform based on Gaussian derivatives
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TLDR
It is shown how the proposed transform can be applied to the problems of image coding, noise reduction and image fusion, and the advantages of this scheme are both analysis and synthesis operators are Gaussian derivatives.Abstract:
In this work, a multi-channel model for image representation is derived based on the scale-space theory. This model is inspired in biological insights and includes some important properties of human vision such as the Gaussian derivative model for early vision proposed by Young [The Gaussian derivative theory of spatial vision: analysis of cortical cell receptive field line-weighting profiles, General Motors Res. Labs. Rep. 4920, 1986]. The image transform that we propose in this work uses analysis operators similar to those of the Hermite transform at multiple scales, but the synthesis scheme of our approach integrates the responses of all channels at different scales. The advantages of this scheme are: (1) Both analysis and synthesis operators are Gaussian derivatives. This allows for simplicity during implementation. (2) The operator functions possess better space-frequency localization, and it is possible to separate adjacent scales one octave apart, according to Wilson's results on human vision channels. [H.R. Wilson, J.R. Bergen, A four mechanism model for spatial vision. Vision Res. 19 (1979) 19–32). (3) In the case of two-dimensional (2-D) signals, it is easy to analyze local orientations at different scales. A discrete approximation is also derived from an asymptotic relation between the Gaussian derivatives and the discrete binomial filters. We show in this work how the proposed transform can be applied to the problems of image coding, noise reduction and image fusion. Practical considerations are also of concern.read more
Citations
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Journal ArticleDOI
The Hermite transform as an efficient model for local image analysis: An application to medical image fusion
TL;DR: The Hermite transform is introduced as an image representation model that can be used to tackle the problem of fusion in multimodal medical imagery and results are compared with a competitive wavelet-based technique, proving that the Hermitetransform provides better reconstruction of relevant image structures.
Journal ArticleDOI
An algorithm for multi-sensor image fusion using maximum a posteriori and nonsubsampled contourlet transform ☆
D. Anandhi,S. Valli +1 more
TL;DR: The proposed fusion algorithm based on Synthetic Aperture Radar and Panchromatic images in Nonsubsampled Contourlet Transform (NSCT) domain outperforms the existing NSCT methods by preserving maximum features.
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A Contactless Respiratory Rate Estimation Method Using a Hermite Magnification Technique and Convolutional Neural Networks
TL;DR: A new non-contact strategy to estimate respiratory rate based on Eulerian motion video magnification technique using Hermite transform and a system based on a Convolutional Neural Network (CNN).
Journal ArticleDOI
2D tight framelets with orientation selectivity suggested by vision science
Hitoshi Arai,Shinobu Arai +1 more
TL;DR: This paper constructs compactly supported tight framelets with orientation selectivity and Gaussian derivative like filters similar to one of simple cells in V1 revealed by recent vision science.
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Optical flow estimation in cardiac CT images using the steered Hermite transform
TL;DR: An approach for optical flow estimation that incorporates image structure information extracted from the steered Hermite coefficients that is later used as local motion constraints in a differential estimation method that involves several of the constraints seen in the current differential methods, which allows obtaining accurate flows.
References
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