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

Artificial intelligence has become pervasive across disciplines and fields, and biomedical image and signal processing is no exception. The growing and widespread interest on the topic has triggered a vast research activity that is reflected in an exponential research effort. Through study of massive and diverse biomedical data, machine and deep learning models have revolutionized various tasks such as modeling, segmentation, registration, classification and synthesis, outperforming traditional techniques. However, the difficulty in translating the results into biologically/clinically interpretable information is preventing their full exploitation in the field. Explainable AI (XAI) attempts to fill this translational gap by providing means to make the models interpretable and providing explanations. Different solutions have been proposed so far and are gaining increasing interest from the community. This paper aims at providing an overview on XAI in biomedical data processing and points to an upcoming Special Issue on Deep Learning in Biomedical Image and Signal Processing of the IEEE Signal Processing Magazine that is going to appear in March 2022.

Explainable AI (XAI) In Biomedical Signal and Image Processing: Promises and Challenges

Color edge detection has been normally based on the vector extension of classical (scalar) derivative operators. This extension has been done either by the independent processing of each color component and aggregation of the partial results or by the synthesis of a special color space, featuring a single highly relevant component. These approaches fail to embed any visual perception information. This paper proposes the use of a perceptually relevant dissimilarity measure, based on a fuzzy color model and on a spatial symmetry color spread index as the basis of a color edge detector.

Perceptual fuzzy multiscale color edge detection

Texture discrimination was the second more important task studied after colour perception and characterization.
Nevertheless, few works explore the colour extension of these works and none for vectorial processing of
this important visual information. In this work we propose a novel and vector processing for colour texture
characterization, the color contrast occurrence matrix C2O. This new texture feature is based on the colour
dierence assessment. To be link to the human perception, the colour dierence is expressed using a perceptual
distance expressed in CIELab and two angles characterizing the chromaticity and darker or lighter direction.
Through this new attribute, we analyze the stability to changes in illumination, viewpoint and spectrum of the
light source in front of dierent texture image databases . Thanks to our construction, we avoid the main limit of
existing texture features requiring an initial colour quantization or a binarization inside the texture construction.
Keeping the small local contrast, we obtain a more accurate texture feature description explaining the obtained
results. Then we carry out the construction of a features vector by occurrence quantization, keeping the initial
ideas of Julesz, Haralick and Ojala, for the classication purposes. The results show best correct classication
percentages in databases that with important spatio-chromatic complexity as ALOT.

https://hal.archives-ouvertes.fr/hal-01228237/document

Colour Contrast Occurrence matrix: a vector and perceptual texture feature

Gliomas are the most common central nervous system tumors. They represent 1.3% of cancers and are the 15th most common cancer for men and women. For the diagnosis of such pathology, doctors commonly use Magnetic Resonance Imaging (MRI) with different sequences. In this work, we propose a global framework using convolutional neural networks to create an intelligent assistant system for neurologists to diagnose the brain gliomas. Within this framework, we study the performance of different neural networks on four MRI modalities. This work allows us to highlight the most specific MRI sequences so that the presence of gliomas in brain tissue can be classified. We also visually analyze extracted features from the different modalities and networks with an aim to improve the interpretability and analysis of the performance obtained. We apply our study on the MRI sequences that are obtained from BraTS datasets.

An Empirical Study of Deep Neural Networks for Glioma Detection from MRI Sequences

JPEG2000 is an emerging standard for still image compression. It is not only intended to provide rate-distortion and subjective image quality performance superior to existing standards, but also to provide features and additional functionalities that current standards can not address sufficiently such as lossless and lossy compression, progressive transmission by pixel accuracy and by resolution, etc. Currently the JPEG2000 standard is set up for use with the sRGB three-component color space.the aim of this research is to determine thanks to psychovisual experiences whether or not the color space selected will significantly improve the image compression. The RGB, XYZ, CIELAB, CIELUV, YIQ, YCrCb and YUV color spaces were examined and compared. In addition, we started a psychovisual evaluation on the effect of color quantification on JPEG2000 image compression. the final results indicate that the YCrCb color space is very suitable for this standard as mentioned in the amendement2.

Christine Fernandez-Maloigne

Papers

Explainable AI (XAI) In Biomedical Signal and Image Processing: Promises and Challenges

Perceptual fuzzy multiscale color edge detection

Colour Contrast Occurrence matrix: a vector and perceptual texture feature

An Empirical Study of Deep Neural Networks for Glioma Detection from MRI Sequences

Psychovisual evaluation of the effect of color spaces and color quantification in JPEG2000 image compression