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

Most feature point tracking approaches assume that objects are Lambertian and that no lighting change occurs. To some extent, the tracking method with photometric normalization and the approach proposed by Soatto et al. overcome these issues. From a study on specular reflection models, we explain explicitly the assumptions on which these techniques are implicitly based. Then, we propose an original tracking algorithm based on a more appropriate modeling of the illumination changes. Experimental results demonstrate the robustness of our approach against specular highlights and lighting changes.

http://documents.irevues.inist.fr/bitstream/handle/2042/13982/A366_26636.pdf;sequence=1

Suivi de points robuste aux spécularités et aux changements d'éclairage

Fuzzy Color Image Segmentation via multi-scale edge/texture based watershed

From the discret Meyer wavelet transform, we propose in this paper an extension to a Meyer wavelet paquet decomposition and its application to the image. This new approch based on a projection, which has good properties of frequential discrimination, permits one to have a representation adapted to a segmentation algorithm 1 Rappel sur les ondelettes de Meyer Dé nition Le principe des ondelettes de Meyer est de lisser l'ondelette sinc, considéré comme le cas idéal en fréquentiel. Pour cela, une fonction de coupure de régularité variable est dé nie par r(t) = 0 si t 0 1 si t 1 , et r(t) + r(1 t) = 1 pour tout t: La fonction d'échelle de Meyer, , s'exprime alors en fonction de r [2]. Nous notons b , la transformée de Fourier de : b ( ) = <: 1 si j j 1=3 cos( 2 r(3 j j 1) si 1=3 < j j 2=3 0 sinon : (1) A la fonction , introduite dans l'équation (1), correspond une analyse multirésolution [4]. On peut alors trouver une fonction périodique H( ) telle que b (2 ) = (1= p 2)H( )b ( ): Ce ltre passe-bas H( ) peut s'écrire [4]

Paquets d'ondelettes de Meyer pour le traitement d'images

Our article presents a new way to characterize texture : Wavelet Geometrical Features, that extracts structural measurements from wavelet sub-bands, when most of the wavelet-based methods found in the litterature use only statistical ones. We first describe the method used to compute our features, and thereafter compare them to thirteen other standard texture features in a classification experiment on the whole Brodatz texture database. We showed that our method produces the best results, especially over the wavelet energy signature and the method it originated from, the Statistical Geometrical Features of Chen.

Christine Fernandez-Maloigne

Papers

Suivi de points robuste aux spécularités et aux changements d'éclairage

Fuzzy Color Image Segmentation via multi-scale edge/texture based watershed

Paquets d'ondelettes de Meyer pour le traitement d'images

A new wavelet sub-band characterization for texture recognition

Fundamental Study for Evaluating Image Quality