The PASCAL Visual Object Classes Challenge

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

This paper presents a new reflectance model for rendering computer synthesized images. The model accounts for the relative brightness of different materials and light sources in the same scene. It describes the directional distribution of the reflected light and a color shift that occurs as the reflectance changes with incidence angle. The paper presents a method for obtaining the spectral energy distribution of the light reflected from an object made of a specific real material and discusses a procedure for accurately reproducing the color associated with the spectral energy distribution. The model is applied to the simulation of a metal and a plastic.

A reflectance model for computer graphics

Quality evaluation of underwater images is a key goal of underwater video image retrieval and intelligent processing. To date, no metric has been proposed for underwater color image quality evaluation (UCIQE). The special absorption and scattering characteristics of the water medium do not allow direct application of natural color image quality metrics especially to different underwater environments. In this paper, subjective testing for underwater image quality has been organized. The statistical distribution of the underwater image pixels in the CIELab color space related to subjective evaluation indicates the sharpness and colorful factors correlate well with subjective image quality perception. Based on these, a new UCIQE metric, which is a linear combination of chroma, saturation, and contrast, is proposed to quantify the non-uniform color cast, blurring, and low-contrast that characterize underwater engineering and monitoring images. Experiments are conducted to illustrate the performance of the proposed UCIQE metric and its capability to measure the underwater image enhancement results. They show that the proposed metric has comparable performance to the leading natural color image quality metrics and the underwater grayscale image quality metrics available in the literature, and can predict with higher accuracy the relative amount of degradation with similar image content in underwater environments. Importantly, UCIQE is a simple and fast solution for real-time underwater video processing. The effectiveness of the presented measure is also demonstrated by subjective evaluation. The results show better correlation between the UCIQE and the subjective mean opinion score.

An Underwater Color Image Quality Evaluation Metric

Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.

Medical Image Fusion: A survey of the state of the art

This paper presents a multiple resolution algorithm for the segmentation of three-dimensional magnetic resonance (MR) images. The algorithm consists in the unsupervised segmentation of the MR volume into regions of different statistical behavior. Firstly, an unsupervised merging algorithm estimates a block segmentation of the volume while determining the region number and the parameters of those regions. This estimation is computed by minimizing a global information criterion. Next, the small regions are eliminated using statistic criteria. Finally, the segmentation is performed using the neighboring relationships between voxels via hidden Markov random fields and a multiple resolution iterated conditional mode algorithm. Some results on volumetric brain MR images are presented and discussed.

Unsupervised algorithm for the segmentation of three-dimensional magnetic resonance brain images

With the multiplicity of imaging devices, the color quality and portability have become a very challenging problem. Moreover, a color is perceived with regards to its environment. So, if this environment changes it implies a change in the perceived color. In order to address this influence, the CIE (Commission Internationale de I'eclairage) has standardized a tool named color appearance model (CIECAM97*, CIECAM02). These models are able to take into account many phenomena related to human vision of color and can predict the color of a stimulus, function of its observations conditions. However, these models do not deal with the influence of spatial frequencies which can have a big impact on our perception. In this paper, we present an extended version of the CIECAM02 that integrates a spatial model correcting the color in relation to its spatial frequency. Moreover, the previous model has been modified to deal with images and not only single stimulus. The main difference with the rendering models (e.g. iCAM) lies in the fact that the proposed model, takes into account the spatial repartition of a pixel in addition to its environment. The obtained results are sound and demonstrate the efficiency of the proposed extension. This has been checked thanks to a psychophysical study where observers were assigned the task of assessing the quality of the improved version in comparison to the original.

Image Rendering Based on a Spatial Extension of the CIECAM02

Durant les dernieres annees, la prise en compte de modeles du Systeme Visuel Humain (SVH) dans l'evaluation de la qualite visuelle des images couleur compressees, est devenu un sujet de recherche majeur. II semble naturel d'integrer davantage les connaissances recentes sur la perception et la modelisation de la vision humaine, dans les systemes de compression d'images. Ainsi, dans cet article, nous proposons une methode d'integration de ces connaissances pour l'augmentation de la qualite visuelle d'images compressees JPEG2000. Cette methode consiste en deux parties: une evaluation de laboratoire pour la modelisation du SVH par la Fonction de Sensibilite au Contraste (CSF) et une technique de calcul de facteurs de ponderation visuelle pour la compression JPEG2000, utilisant le modele SVH evalue, dans le domaine de Fourier de l'image couleur.

Amélioration de la qualité visuelle d'images couleur dans le cadre du standard de compression JPEG2000

The presented method is based on a hypothesis witch the verification necessitates the segmentation of the measured signals by a mobile receiver and by a software for the electromagnetic analysis of the geographic studied area. The objective is to segment the received signal in intervals corresponding to particular combinations of physical phenomena. For this, a representation suggested by Mallat and Zhong called "Wavelet maxima representation" is studied. This decomposition allows the study of the function derives at different scales. We present a segment method of signals based on the maxima chaining through the scales. This chaining allows to select the greatest discontinuities of the signal and thus to segments it.

Segmentation de signaux par maxima d'ondelettes : application à la prédiction de zones de couverture radioélectrique

The changing of TV systems from 2D to 3D mode is the next expected step in the telecommunication world. Some works have already been done to perform this progress technically, but interaction of the third dimension with humans is not yet clear. Previously, it was found that any increased load of visual system can create visual fatigue, like prolonged TV watching, computer work or video gaming. But watching S3D can cause another nature of visual fatigue, since all S3D technologies creates illusion of the third dimension based on characteristics of binocular vision. In this work we propose to evaluate and compare the visual fatigue from watching 2D and S3D content. This work shows the difference in accumulation of visual fatigue and its assessment for two types of content. In order to perform this comparison eye-tracking experiments using six commercially available movies were conducted. Healthy naive participants took part into the test and gave their answers feeling the subjective evaluation. It was found that watching stereo 3D content induce stronger feeling of visual fatigue than conventional 2D, and the nature of video has an important effect on its increase. Visual characteristics obtained by using eye-tracking were investigated regarding their relation with visual fatigue.

Christine Fernandez-Maloigne

Papers

Unsupervised algorithm for the segmentation of three-dimensional magnetic resonance brain images

Image Rendering Based on a Spatial Extension of the CIECAM02

Amélioration de la qualité visuelle d'images couleur dans le cadre du standard de compression JPEG2000

Segmentation de signaux par maxima d'ondelettes : application à la prédiction de zones de couverture radioélectrique

On the comparison of visual discomfort generated by S3D and 2D content based on eye-tracking features