Measure of similarity between images based on the codispersion coefficient

Ojeda, Silvia María; Vallejos, Ronny; Lamberti, Pedro W.

doi:10.1117/1.jei.21.2.023019

Cited by 23 publications

(25 citation statements)

References 13 publications

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“…The method, called Algorithm 5, can generate a wide range of textures using four different local approximations of AR-2D processes, corresponding to the four strongly causal regions on the plane. Using two image quality index that are extensively used in image similarity analysis, we carried out experiments that support our algorithm ( [19], [20], [16]). Specifically, a set of images belonging to the image database ( [12]) were processed and provided satisfactory results in order to reproduce and represent image textures.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…To gain insight on the quality of each image representation, the images produced by the five algorithms were compared with the original image using the similarity index ( [19]) and ( [16]), described in section IV. The results are shown in TABLE I.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Coefficient is defined as a function of the correlation coefficient; hence, it is able to capture only the linear association between and . The index was suggested by [16] and it is defined as:…”

Section: Spatial Arma Modelsmentioning

confidence: 99%

“…Similarity, denote the output corresponding to step 3 -Algorithm . The computation of the distance between each filtered image and the original was be done by using and image quality measures ( [18], [19], [16]). We described briefly this measures:…”

Section: Spatial Arma Modelsmentioning

confidence: 99%

See 3 more Smart Citations

A New Algorithm to Represent Texture Images

Ojeda¹,

Britos²

2013

IJACSA

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Abstract-In recent times the spatial autoregressive models have been extensively used to represent images. In this paper we propose an algorithm to represent and reproduce texture images based on the estimation of spatial autoregressive processes. The image intensity is locally modeled by a first spatial autoregressive model with support in a strongly causal prediction region on the plane. A basic criteria to quantify similarity between two images is used to locally select this region among four different possibilities, corresponding to the four strongly causal regions on the plane. Two global image similarity measures are used to evaluate the performance of our proposal.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Spatial Arma Modelsmentioning

confidence: 99%

Section: Spatial Arma Modelsmentioning

confidence: 99%

See 2 more Smart Citations

A New Algorithm to Represent Texture Images

Ojeda¹,

Britos²

2013

IJACSA

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“…Among recent research based on the measure of similarity between images, we can also cite the work of Silvia M. Ojeda (Ojeda, 2012).Ojeda proposes to use the codispersion coefficient to define a measure of similarity between images, which has been widely used in spatial statistics to quantify the association between two spatial processes. In this paper, we will describe a comparison algorithm to test the degree of resemblance between images, having an advantage in computation time and memory space since only specific points (or pixels) of the image will be extracted and used for the comparison.…”

Section: Introductionmentioning

confidence: 99%

Calculation of the similarity rate between images based on the local minima present Therein

Hourany¹,

Benmeddour²,

Moulin³

et al. 2016

LSJ

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Image processing is a very vast field that includes both IT and applied mathematics. It is a discipline that studies the improvement and transformations of digital images hence permitting the improvement of the quality of these images and the extraction of information. The comparison of digital images is a paramount issue that has been discussed in several researches because of its various applications especially in the field of control and surveillance such as the Structural HealthMonitoring using acoustic waves. The IT support of the images serves especially for comparing them notably in distinguishing differences between these images and quantifying them automatically. In this study we will present an algorithm, allowing us to calculate the similarity rate between images based on the local minima present therein. This algorithm is divided into two main parts. In the first part we will explain how to extract the local minima from an image and in the second part we will show how to calculate the similarity rate between two images.

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Assessing the significance of the correlation between the components of a bivariate Gaussian random field

2015

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Assessing the significance of the correlation between the components of a bivariate random field is of great interest in the analysis of spatial data. This problem has been addressed in the literature using suitable hypothesis testing procedures or using coefficients of spatial association between two sequences. In this paper, testing the association between autocorrelated variables is addressed for the components of a bivariate Gaussian random field using the asymptotic distribution of the maximum likelihood estimator of a specific parametric class of bivariate covariance models. Explicit expressions for the Fisher information matrix are given for a separable and a nonseparable version of the parametric class, leading to an asymptotic test. The empirical evidence supports our proposal, and as a result, in most of the cases, the new test performs better than the modified t test even when the bivariate covariance model is misspecified or the distribution of the bivariate random field is not Gaussian. Finally, to illustrate how the proposed test works in practice, we study a real dataset concerning the relationship between arsenic and lead from a contaminated area in Utah, USA.

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Measure of similarity between images based on the codispersion coefficient

Cited by 23 publications

References 13 publications

A New Algorithm to Represent Texture Images

A New Algorithm to Represent Texture Images

Calculation of the similarity rate between images based on the local minima present Therein

Assessing the significance of the correlation between the components of a bivariate Gaussian random field

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