The Color Monogenic Signal: Application to Color Edge Detection and Color Optical Flow

Demarcq, Guillaume; Mascarilla, Laurent; Berthier, Michel; Courtellemont, Pierre

doi:10.1007/s10851-011-0262-6

Cited by 23 publications

(8 citation statements)

References 14 publications

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“…Identity ς for all test samples; 1: BEGIN 2: Acquire the monogenic signal of all the original training samples by (8) and (9), from which the V discriminative scales can be selected by Fisher discrimination criterion; 3: Build the weight vector W = (w 1 , w 2 , w 3 ) with global Fisher score of each component; 4: Generate the monogenic component-specific features with scale selection by (31); 5: Build three overcomplete dictionaries X 1 , X 2 , X 3 ; 6: for j = 1 to l do 7: ς j = arg min …”

Section: Inputmentioning

confidence: 99%

SAR Image Recognition with Monogenic Scale Selection-Based Weighted Multi-task Joint Sparse Representation

et al. 2018

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Abstract:The monogenic signal, which is defined as a linear combination of a signal and its Riesz-transformed one, provides a great opportunity for synthetic aperture radar (SAR) image recognition. However, the incredibly large number of components at different scales may result in too much of a burden for onboard computation. There is great information redundancy in monogenic signals because components at some scales are less discriminative or even have negative impact on classification. In addition, the heterogeneity of the three types of components will lower the quality of decision-making. To solve the problems above, a scale selection method, based on a weighted multi-task joint sparse representation, is proposed. A scale selection model is designed and the Fisher score is presented to measure the discriminative ability of components at each scale. The components with high Fisher scores are concatenated to three component-specific features, and an overcomplete dictionary is built. Meanwhile, the scale selection model produces the weight vector. The three component-specific features are then fed into a multi-task joint sparse representation classification framework. The final decision is made in terms of accumulated weighted reconstruction error. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset have proved the effectiveness and superiority of our method.

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Section: Inputmentioning

confidence: 99%

SAR Image Recognition with Monogenic Scale Selection-Based Weighted Multi-task Joint Sparse Representation

et al. 2018

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“…In [6], Demarcq et. al introduce a novel representation for color images called the color monogenic signal.…”

Section: Color Monogenic Signal (Cms)mentioning

confidence: 99%

“…and the angle between f and V is given by ϕ V = atan | f V 2 | f V 0 where f V 0 and f V 2 denote the scalar and bivector part respectively (see [6] for more details). Finally, the polar representation of the CMS is obtained by χ(V ) = A V e Bϕ V , where B = f V 2 /| f V 2 | is a unitary bivector which squares to minus one and thus can be assimilated to the complex number i.…”

Section: Color Monogenic Signal (Cms)mentioning

confidence: 99%

Geodesic interactive segmentation in the color monogenic signal framework

Demarcq¹,

Capitaine

Berthier

2012

2012 19th IEEE International Conference on Image Processing

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In this paper, we present an interactive algorithm for segmentation of color images. The user first draws some scribbles into regions that must be discriminated, and the segmentation is then automatically obtained. The segmentation is based on the computation of geodesic distances within color monogenic signal (CMS) fields. An important difference with stateof-the-art methods is that scribbles, which are often segments, are sample pixels picked up by the user. It results in a much more user-friendly segmentation process. Experimental results and comparisons with recent methods show the effectiveness of the approach.

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“…However, MWT fails to handle color images. Demarcq et al (2011) generalized construction of the monogenic signal to mappings with values in the vector part of the Clifford algebra. Soulard et al (2013) proposed non-marginal color monogenic wavelet transform by extension of the monogenic framework to vector-valued signals which has shown to be shift invariant and sparse representation.…”

Section: Introductionmentioning

confidence: 99%

Color monogenic wavelet transform for multichannel image denoising

Gai

Zhang

Yang

et al. 2016

Multidim Syst Sign Process

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This paper proposes an effective color image denoising algorithm using the combination color monogenic wavelet transform (CMWT) with a trivariate shrinkage filter. The CMWT coefficients are one order of magnitude with three phases: two phases encode the local color information while the third contains geometric information relating to texture within the color image. In the CMWT domain, a trivariate Gaussian distribution is applied to capture statistical dependencies between the CMWT coefficients, and then a trivariate shrinkage filter is derived using a maximum a posteriori estimator. The performance of the proposed algorithm is experimentally verified using a variety of color test images with a range of noise levels in terms of PSNR and visual quality. The experimental results demonstrate that the proposed algorithm is equal to or better than current state-of-the-art algorithms in both visual and quantitative performance.

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The Color Monogenic Signal: Application to Color Edge Detection and Color Optical Flow

Cited by 23 publications

References 14 publications

SAR Image Recognition with Monogenic Scale Selection-Based Weighted Multi-task Joint Sparse Representation

SAR Image Recognition with Monogenic Scale Selection-Based Weighted Multi-task Joint Sparse Representation

Geodesic interactive segmentation in the color monogenic signal framework

Color monogenic wavelet transform for multichannel image denoising

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