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The standard approach to the estimation of homographies consists in the application of the RANSAC algorithm to a set of tentative matches. More recent strategies based on deep learning, namely convolutional architectures, have become available. In this work, a new algorithm for the estimation of homographies is developed. It is rooted in a convolutional neural network for homography estimation, which is provided with a range of versions of the input pair of pictures. Such versions are generated by perturbation of the color levels of the input images. Each generated pair of images yields a distinct estimation of the homography, and then the estimations are combined together to obtain a final, more robust estimation. Experiments have been designed and carried out to test the validity of our approach, including qualitative and quantitative performance measures. In particular, it is demonstrated that our approach consistently outperforms the baseline approach consisting of using the output of the homography estimation deep network for the original input pair of images.

Most classic approaches to homography estimation are based on the filtering of outliers by means of the RANSAC method. New proposals include deep convolutional neural networks. Here a new method for homography estimation is presented, which supplies a deep neural homography estimator with color perturbated versions of the original image pair. The obtained outputs are combined in order to obtain a more robust estimation of the underlying homography. Experimental results are shown, which demonstrate the adequate performance of our approach, both in quantitative and qualitative terms. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.

The internet security has become the important criteria in the 21st century security applications and its prominence is increasing along with the implementation of the innovative technologies. The data transmission based algorithms has gained popularity in the security domain to provide the protection to the secret information in a reliable way. The secure data transmission is been a concerned area in the field of security and the accidental/incidental attacks on secure data makes it more challenging to provide the ample security without any flaws. The secure image transmission technique paid a lot of attention from the research organizations because of its unique ability to recover the secret image almost losslessly. Although tremendous progress has been made in the past decade on this area but it is consider as unresolved issue in the security domain of the digital image processing domain. The novelty of the proposed method is it automatically transforms the large-volume secret image into a so-called secretfragment- visible mosaic image of the same size. In order to convert the secret image into a mosaic image make use of color transformations is done by dividing the secret image into fragments and transforming their color characteristics to be those of the corresponding blocks of the target image. A scheme namely lossless hiding scheme is utilized along with the key which is used to recover the secret image from the mosaic image by using the information which is embedded into mosaic image. The reliability and efficiency of the proposed method are evaluated by simulation results. https://journalnx.com/journal-article/20150295

We use methods from Riemann geometry to investigate transformations between the color spaces of color-normal and color weak observers. The two main applications are the simulation of the perception of a color weak observer for a color normal observer and the compensation of color images in a way that a color weak observer has approximately the same perception as a color normal observer. The metrics in the color spaces of interest are characterized with the help of ellipsoids defined by the just-noticable-differences between color which are measured with the help of color-matching experiments. The constructed mappings are isometries of Riemann spaces that preserve the perceived color-differences for both observers. Among the two approaches to build such an isometry, we introduce normal coordinates in Riemann spaces as a tool to construct a global color-weak compensation map. Compared to previously used methods this method is free from approximation errors due to local linearizations and it avoids the problem of shifting locations of the origin of the local coordinate system. We analyse the variations of the Riemann metrics for different observers obtained from new color matching experiments and describe three variations of the basic method. The performance of the methods is evaluated with the help of semantic differential (SD) tests., Comment: Full resolution color pictures are available from the authors