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

Visual Quality Enhancement for Colour Images in the Framework of the JPEG2000 Compression Standard

This paper deals with Computer Aided Diagnosis for skin cancers (melanomas). The diagnosis is based on some rules called the ABCD mnemonics. They take into account color distribution, lesion's diameter, etc. The goal isn't to classify the lesion but to find those, which are the most similar, in order to help the expert to confirm his diagnosis and to avoid any useless excision. This is done thanks to an indexation system, which compare the signatures of previously diagnosed lesions contained in a database and patient's lesion signature. This last is constructed by translating the rules into image processing attributes. We have divided then into three families: Color attributes (color and fuzzy histograms), Texture attributes (co-occurrence matrix and Haralick indices) and Shape attributes (lesion surface and maximum included circle). Image quantization permits us to keep only the most significant colors, thus giving a light structure. Finally, we define a distance for each attribute and use weighted combination for the similarity measure.

Using combination of color, texture, and shape features for image retrieval in melanomas databases

Objective: To provide an approach to facial contrast, analysing CIELAB colour differences ΔE*ab,10 and its components in women's faces from two different ethnic groups, illuminated by modern white light‐emitting diodes (LEDs) or traditional illuminants recommended by the International Commission on Illumination (CIE).



Methods: We performed spectrophotometric measurements of spectral reflectance factors on forehead and cheek of 87 young healthy women (50 Caucasians and 37 Orientals), plus five commercial red lipsticks. We considered a set of 10 white LED illuminants, representative of technologies currently available on the market, plus eight main illuminants currently recommended by the CIE, representative of conventional incandescent, daylight and fluorescent light sources. Under each of these 18 illuminants, we analysed the magnitude and components of ΔE*ab,10 between Caucasian and Oriental women (considering cheek and forehead), as well as for cheek–forehead and cheek–lipsticks in Caucasian and Oriental women. Colour‐inconstancy indices for cheek, forehead and lipsticks were computed, assuming D65 and A as reference illuminants.



Results: ΔE*ab,10 between forehead and cheek were quantitatively and qualitatively different in Orientals and Caucasians, but discrepancies with respect to average values for 18 illuminants were small (1.5% and 5.0% for Orientals and Caucasians, respectively). ΔE*ab,10 between Caucasians and Orientals were also quantitatively and qualitatively different both for forehead and cheek, and discrepancies with respect to average values were again small (1.0% and 3.9% for forehead and cheek, respectively). ΔE*ab,10 between lipsticks and cheek were at least two times higher than those between forehead and cheek. Regarding ΔE*ab,10 between lipsticks and cheeks, discrepancies with respect to average values were in the range 1.5–12.3%, although higher values of up to 54.2% were found for a white RGB LED. This white RGB LED provided the highest average colour‐inconstancy indices: 17.1 and 11.5 CIELAB units, under reference illuminants D65 and A, respectively.



Conclusion: Colour contrasts in women's faces under CIE standard illuminants for outdoor and indoor conditions may be strongly altered using specific white LEDs. More research needs to be performed on the impact of spectral power distribution of light sources with high colour rendering indices on visual colour appearance of cosmetic products.

/pdf/colour-differences-in-caucasian-and-oriental-women-s-faces-1qkc6o35s8.pdf

Colour differences in Caucasian and Oriental women's faces illuminated by white light-emitting diode sources.

https://hal.inria.fr/hal-00726513/document

A study on local photometric models and their application to robust tracking

Saliency is one of the most important features in human visual perception. It is widely used nowadays for perceptually optimizing image processing algorithms. Several models have been proposed for 2D images and only few attempts can be observed for 3D ones. In this paper, we propose a stereoscopic 3D saliency model relying on 2D saliency features jointly with depth obtained from monocular cues. On the one hand, the use of 2D saliency features is justified psychophysically by the similarity observed between 2D and 3D attention maps. On the other hand, 3D perception is significantly based on monocular cues. The validation of our model using state-of-the-art procedures including Kullback-Leibler divergence (KLD), area under the curve (AUC) and correlation coefficient (CC) in comparison with attention maps showed very good performance.

Christine Fernandez-Maloigne

Papers

Visual Quality Enhancement for Colour Images in the Framework of the JPEG2000 Compression Standard

Using combination of color, texture, and shape features for image retrieval in melanomas databases

Colour differences in Caucasian and Oriental women's faces illuminated by white light-emitting diode sources.

A study on local photometric models and their application to robust tracking

Using monocular depth cues for modeling stereoscopic 3D saliency