In this paper, we use the Bayesian framework to design an improved algorithm for detecting edges from non-uniform Fourier data. In particular, we employ what is known as type-II Bayesian estimation, specifically a method called sparse Bayesian learning.

Feb 7, 2017 · In this paper, we propose a new synthetic aperture radar (SAR) image detection algorithm based on the de-noising algorithm via the sparse representation and a new morphology edge detector. Firstly, we apply the Shearlet transform to the SAR image to get the sparse representation of it.

Apr 1, 2014 · Two model-based algorithms for edge detection in spectral imagery are developed that specifically target capturing intrinsic features such as isoluminant edges that are characterized by a jump in color but not in intensity.

Here, a sparse coding algorithm, that has previously been used to account of the response properties of orientation tuned cells in primary visual cortex, is applied to the task of perceptually salient boundary detection.

Jan 3, 2025 · The Canny edge detection algorithm operates by locating the local maxima of image gradients, using high and low thresholds to differentiate between strong and weak edges. This approach effectively minimizes the effects of noise and ensures the …

Jan 1, 2015 · This paper presents the application of sparse banded filter matrices in edge detection. The filter design is formulated in terms of banded matrices. The sparsity property of the designed filter leads to efficient computation.

Feb 1, 2014 · In this paper, we consider the problem of detecting edges in a Gaussian graphical model. The problem is equivalent to the identification of non-zero entries of the concentration matrix of a normally distributed random vector.

Abstract In this paper, we propose a new synthetic aper-ture radar (SAR) image detection algorithm based on the de-noising algorithm via the sparse representation and a new morphology edge detector. Firstly, we apply the Shearlet transform to the SAR image to get the sparse representa-tion of it.

Aug 19, 2010 · In this paper, we present a novel wavelet-based algorithm for multi-scale edge detection. Firstly, calculate wavelet transform coefficients of the image according to the direction of the gradient. Then scan the neighborhood of the corresponding wavelet transform coefficients separately at three-scales, in order to position edges at a small ...

A method of image edge detection based on Sparse Autoencoder neural work is proposed in this paper. This method uses Berkeley Segmentation data set to extract the highdimensional edge features of sample data by training the sparse autoencoder.