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With COVID-19, the interest for digital interactions has raised, putting in turn real-time (or low-latency) codecs into a new light. Most of the codec research has been traditionally focusing on coding efficiency, while very little literature exist on real-time codecs. It is shown how the speed at which content is made available impacts both latency and throughput. The authors introduce a new test set up, integrating a paced reader, which allows to run codec in the same condition as real-time media capture. Quality measurements using VMAF, as well as multiple speed measurements are made on encoding of HD and full HD video sequences, both at 25 fps and 50 fps to compare the respective performances of several implementations of the H.264, H.265, VP8, VP9 and AV1 codecs., 8 pages, 4 figures, 6 tables

Finance institutions have always needed enhanced security to protect their assets. Today that want to enjoy access to the cloud as well as new communication technologies like WebRTC. In parallel, the IETF created a new working group called Privacy Enhanced RTP Conferencing (PERC) that propose solutions to allow usage of media servers in the public cloud without compromising the security. This work is about implementing PERC's double encryption specifications in conjunction with WebRTC 1.0 sender simulcast. We are showing that the implementation of a few additional specifications, and an enhancement of the proposed Headers Extension mechanism allows for a viable double encryption for WebRTC 1.0 sender simulcast.

Digital pathology represents one of the major evolutions in modern medicine. Pathological examinations constitute the gold standard in many medical protocols, and also play a critical and legal role in the diagnosis process. In the conventional cancer diagnosis, pathologists analyze biopsies to make diagnostic and prognostic assessments, mainly based on the cell morphology and architecture distribution. Recently, computerized methods have been rapidly evolving in the area of digital pathology, with growing applications related to nuclei detection, segmentation, and classification. In cancer research, these approaches have played, and will continue to play a key (often bottleneck) role in minimizing human intervention, consolidating pertinent second opinions, and providing traceable clinical information. Pathological studies have been conducted for numerous cancer detection and grading applications, including brain, breast, cervix, lung, and prostate cancer grading. Our study presents, discusses, and extracts the major trends from an exhaustive overview of various nuclei detection, segmentation, feature computation, and classification techniques used in histopathology imagery, specifically in hematoxylin-eosin and immunohistochemical staining protocols. This study also enables us to measure the challenges that remain, in order to reach robust analysis of whole slide images, essential high content imaging with diagnostic biomarkers and prognosis support in digital pathology.

Along with mitotic count, nuclear pleomorphism or nuclear atypia is an important criterion for the grading of breast cancer in histopathology. Though some works have been done in mitosis detection (ICPR 2012, 1 MICCAI 2013, 2 and ICPR 2014), not much work has been dedicated to automated nuclear atypia grading, especially the most difficult task of detection of grade 3 nuclei. We propose the use of Convolutional Neural Networks for the automated detection of cell nuclei, using images from the three grades of breast cancer for training. The images were obtained from ICPR contests. Additional manual annotation was performed to classify pixels into five classes: stroma, nuclei, lymphocytes, mitosis and fat. At total of 3,000 thumbnail images of 101 × 101 pixels were used for training. By dividing this training set in an 80/20 ratio we could obtain good training results (around 90%). We tested our CNN on images of the three grades which were not in the training set. High grades nuclei were correctly classified. We then thresholded the classification map and performed basic analysis to keep only rounded objects. Our results show that mostly all atypical nuclei were correctly detected.

This communication describes the study of an ecological system using remote-sensing data and image-analysis tools derived from possibility theory. Possibility theory enables the construction of membership functions using a multisource fusion algorithm. The sources of information are the sampled training stations. The authors test to see if the possibilistic algorithm is able to provide results with an accuracy at least equal to that provided by traditional probabilistic-classification algorithms. Then, for each pixel, they analyze the hierarchy of membership degrees output by the fusion to study the spatial structure of an ecosystem composed of objects that lack precise boundaries. They characterize patches or gradients, boundary rates, and transition states. As an example, a scheme of analysis for underwater reefscapes at Moorea Island, French Polynesia, is proposed. The nonparametric multisource fusion method has an accuracy of 82% (overall normalized-percentage agreement), while a probabilistic maximum-likelihood classifier has an accuracy of 73%. The analysis of the hierarchy of membership degrees indicates that almost 25% of Moorea Island lagoon is heterogeneous, composed of real boundaries, transition states, and fragmented zones.

Ecotones zones lie between homogeneous ecological systems. They are characterised on images by heterogeneous pixels in a specific neighbourhood. Fuzzy classifiers output membership degrees that better represent the heterogeneity within a pixel and can be further processed within the context of a local neighbourhood. This Letter formalises these notations. Two coral reef systems examples are presented. They illustrate the use of possibility measurement to characterise ecotones, and the use of information on well-known ecotones to increase the accuracy of image classification.

Accurate counting of mitosis in breast cancer histopathology plays a critical role in the grading process. Manual counting of mitosis is tedious and subject to considerable inter- and intra-reader variations. This work aims at improving the accuracy of mitosis detection by selecting the color channels that better capture the statistical and morphological features having mitosis discrimination from other objects. The proposed framework includes comprehensive analysis of first and second order statistical features together with morphological features in selected color channels and a study on balancing the skewed dataset using SMOTE method for increasing the predictive accuracy of mitosis classification. The proposed framework has been evaluated on MITOS data set during an ICPR 2012 contest and ranked second from 17 finalists. The proposed framework achieved 74% detection rate, 70% precision and 72% F-Measure. In future work, we plan to apply our mitosis detection tool to images produced by different types of slide scanners, including multi-spectral and multi-focal microscopy.

An expert system approach for image classification according to expert knowledge about best sites for vegetation classes is described. Uncertainty management is solved by a certainty factor approach. The numerical and symbolic data fusion is viewed as an updating process. The fusion approach is then described. A neural classifier applied to image data is the first source. A set of fuzzy neural networks representing expert knowledge constitutes the second source. A conjunctive combination based on evidence theory is applied. Finally, a possibility theory-based pooling aggregation rule is presented. These three approaches are applied to a vegetation classification problem.

Morphology of cell nuclei is a central aspect in many histopathological studies, in particular in breast cancer grading. Therefore, the automatic detection and extraction of cell nuclei from microscopic images obtained from cancer tissue slides is one of the most important problems in digital histopathology. We propose to tackle the problem using a model based on marked point processes (MPP), a methodology for extraction of multiple objects from images. The advantage of MPP based models is their ability to take into account the geometry of objects; and the information about their spatial repartition in the image. Previously, the MPP models have been applied for the extraction of objects of simple geometrical shapes. For histological grading, a morphological criterion known as nuclear pleomorphism corresponding to fine morphological differences between the nuclei is assessed by pathologists. Therefore, the accurate delineation of nuclei became an issue of even greater importance than optimal nuclei detection. Recently, the MPP framework has been defined on the space of arbitrarily-shaped objects allowing more accurate extraction of complex-shaped objects. The nuclei often appear joint or even overlap in histopathological images. The model still allows to extract them as individual joint or overlapping objects without discarding the overlapping parts and therefore without significant loss in delineation precision. We aim to compare the MPP model with two state-of-the-art methods selected from a comprehensive review of the available methods. The experiments are performed using a database of H&E stained breast cancer images covering a wide range of histological grades.

Histopathological examination is a powerful standard for the prognosis of critical diseases. But, despite significant advances in high-speed and high-resolution scanning devices or in virtual exploration capabilities, the clinical analysis of whole slide images (WSI) largely remains the work of human experts. We propose an innovative platform in which multi-scale computer vision algorithms perform fast analysis of a histopathological WSI. It relies on application-driven for high-resolution and generic for low-resolution image analysis algorithms embedded in a multi-scale framework to rapidly identify the high power fields of interest used by the pathologist to assess a global grading. GPU technologies as well speed up the global time-efficiency of the system. Sparse coding and dynamic sampling constitute the keystone of our approach. These methods are implemented within a computer-aided breast biopsy analysis application based on histopathology images and designed in collaboration with a pathology department. The current ground truth slides correspond to about 36,000 high magnification (40×) high power fields. The processing time to achieve automatic WSI analysis is on a par with the pathologist's performance (about ten minutes a WSI), which constitutes by itself a major contribution of the proposed methodology.

Bio-inspired computer vision is an emerging field. It aims to reproduce the capabilities of biological vision systems, eventually to simulate the visual functions for various purposes. In this paper, we propose a bio-inspired computer visual system using Graphical Processing Unit (GPU), and its application on breast cancer prognosis. The system extracts visual features from an input image using a mechanism which is similar to human visual system. Then classify the visual features using a machine learning algorithm. As a result, the elements of the input image, which might be related to particular knowledge concepts, can be identified.

Histopathological examination is a powerful method for prognosis of major diseases such as breast cancer. Analysis of medical images largely remains the work of human experts. Current virtual microscope systems are mainly an emulation of real microscopes with annotation and some image analysis capabilities. However, the lack of effective knowledge management prevents such systems from being computer-aided prognosis platforms. The cognitive virtual microscopic framework, through an extended modeling and use of medical knowledge, has the capacity to analyse histopathological images and to perform grading of breast cancer, providing pathologists with a robust and traceable second opinion.

Presents a multi-sources information-fusion method for satellite image classification. The main characteristics of this method are the use of possibility theory to handle the uncertainty of pixel classification, and the ability to mix numeric sources (the satellite image spectral bands) and symbolic sources (expert knowledge about geographical localisation of classes and out-image data for example). First the authors present the basic concepts of possibility theory and the fusion method used. Then they present how they have computed possibility measures for the numeric sources on the one hand, and for the symbolic sources on the other hand. Finally they introduce the fusion of the numeric and symbolic sources. >

In this paper, we present a multisource information-fusion method for satellite image classification. The main characteristics of this method are the use of possibility theory to handle the uncertainty connected with pixel classification, and the ability to mix numeric sources (the satellite image spectral bands) and symbolic sources (expert knowledge about best localisation of classes and out-image data for example). Moreover, this information fusion method is low time consuming and with a linear complexity. First we introduce briefly the possibility theory and the conjunctive fusion method used here. Then we apply this fusion method to a satellite image classification problem. The classes are defined by their spectral response on the one hand, and by the description of their best geographical context on the other hand. We compute the possibility distribution for the numeric sources on the one hand, and for the symbolic sources on the other hand. Finally the fusion handles the possibility measures coming from the numeric sources and from the symbolic sources. >

This paper presents the application of an adaptive information fusion operator developed in the framework of possibility theory for the supervised classification of multisource remote sensing images. This operator is low CPU time consuming and carries out classification rates comparable to maximum likelihood.

Context: According to Nottingham grading system, mitosis count in breast cancer histopathology is one of three components required for cancer grading and prognosis. Manual counting of mitosis is tedious and subject to considerable inter- and intra-reader variations. Aims: The aim is to investigate the various texture features and Hierarchical Model and X (HMAX) biologically inspired approach for mitosis detection using machine-learning techniques. Materials and Methods: We propose an approach that assists pathologists in automated mitosis detection and counting. The proposed method, which is based on the most favorable texture features combination, examines the separability between different channels of color space. Blue-ratio channel provides more discriminative information for mitosis detection in histopathological images. Co-occurrence features, run-length features, and Scale-invariant feature transform (SIFT) features were extracted and used in the classification of mitosis. Finally, a classification is performed to put the candidate patch either in the mitosis class or in the non-mitosis class. Three different classifiers have been evaluated: Decision tree, linear kernel Support Vector Machine (SVM), and non-linear kernel SVM. We also evaluate the performance of the proposed framework using the modified biologically inspired model of HMAX and compare the results with other feature extraction methods such as dense SIFT. Results: The proposed method has been tested on Mitosis detection in breast cancer histological images (MITOS) dataset provided for an International Conference on Pattern Recognition (ICPR) 2012 contest. The proposed framework achieved 76% recall, 75% precision and 76% F-measure. Conclusions: Different frameworks for classification have been evaluated for mitosis detection. In future work, instead of regions, we intend to compute features on the results of mitosis contour segmentation and use them to improve detection and classification rate.

We present in this paper the use of two auto-adaptive information-fusion methods for a satellite image classification problem. These methods come from possibility theory. Several information-fusion methods are available for different kinds of problems. Auto-adaptive fusion allows to have a fusion which modifies its behavior according to information to be merged. It has a conjunctive behavior when sources agree, and it turns to disjunctive behavior when conflict between sources turns greater. In our image processing application, we have used conjunctive fusions so far because sources usually agree on the choice of a class for a pixel. But when we increase the number of sources, we increase by the same time the difficulty to find a common choice from all sources about a pixel. So a disjunctive fusion would be much appropriate for this pixel. An auto-adaptive fusion is able to apply a conjunctive fusion for pixels without conflict, and is able to turn to a disjunctive fusion as conflict between sources increases. This makes a better classification than a simple conjunctive fusion.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In this paper, we present a multi-sources information-fusion method for satellite image classification. The main characteristics of this method are the use of possibility theory to handle the uncertainty connected with pixel classification, and the ability to mix numeric sources (the satellite image spectral bands) and symbolic sources (expert knowledge about best localisation of classes and out-image data for example). Moreover, this information fusion method is low time consuming and with a linear complexity. First we introduce briefly the possibility theory and the conjunctive fusion method used here. Then we apply this fusion method to a satellite image classification problem. The classes are defined by their spectral response on the one hand, and by the description of their best geographical context on the other hand. We compute the possibility distribution for the numeric sources on the one hand, and for the symbolic sources on the other hand. Finally the fusion handles the possibility measures coming from the numeric sources and from the symbolic sources.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Within the last decade, histopathology became widely accepted as a powerful exam for diagnosis and prognosis in mainstream diseases such as breast cancer. Currently, analysis of medical images in histopathology largely remains the work of human experts. For pathologists, this consists of hundreds of slides examined daily. Such a tedious manual work is often inconsistent and subjective. The recent cognitive microscope – MICO - ANR TecSan project aims at radically modifying the medical practices by proposing a new cognitive medical imaging environment able to improve reliability of decision-making and prognosis assistance in histopathology. Our goal is to design a generic, open-ended, semantic digital histology platform including a cognitive dimension. MICO combines visual perception, pervasive exploration of whole slide images, context (including uncertainties) modeling, cognitive vision and quality of experience to reinforce a visual diagnosis assistance following an approach centered on the user behavior. http://ipal.i2r.a-star.edu.sg/project_MICO.htm

Digital pathology represents one of the major and challenging evolutions in modernmedicine. Pathological exams constitute not only the gold standard in most of medicalprotocols, but also play a critical and legal role in the diagnosis process. Diagnosing adisease after manually analyzing numerous biopsy slides represents a labor-intensive workfor pathologists. Thanks to the recent advances in digital histopathology, the recognitionof histological tissue patterns in a high-content Whole Slide Image (WSI) has the potentialto provide valuable assistance to the pathologist in his daily practice. Histopathologicalclassification and grading of biopsy samples provide valuable prognostic information thatcould be used for diagnosis and treatment support. Nottingham grading system is thestandard for breast cancer grading. It combines three criteria, namely tubule formation(also referenced as glandular architecture), nuclear atypia and mitosis count. Manualdetection and counting of mitosis is tedious and subject to considerable inter- and intrareadervariations. The main goal of this dissertation is the development of a framework ableto provide detection of mitosis on different types of scanners and multispectral microscope.The main contributions of this work are eight fold. First, we present a comprehensivereview on state-of-the-art methodologies in nuclei detection, segmentation and classificationrestricted to two widely available types of image modalities: H&E (HematoxylinEosin) and IHC (Immunohistochemical). Second, we analyse the statistical and morphologicalinformation concerning mitotic cells on different color channels of various colormodels that improve the mitosis detection in color datasets (Aperio and Hamamatsu scanners).Third, we study oversampling methods to increase the number of instances of theminority class (mitosis) by interpolating between several minority class examples that lietogether, which make classification more robust. Fourth, we propose three different methodsfor spectral bands selection including relative spectral absorption of different tissuecomponents, spectral absorption of H&E stains and mRMR (minimum Redundancy MaximumRelevance) technique. Fifth, we compute multispectral spatial features containingpixel, texture and morphological information on selected spectral bands, which leveragediscriminant information for mitosis classification on multispectral dataset. Sixth, we performa comprehensive study on region and patch based features for mitosis classification.Seven, we perform an extensive investigation of classifiers and inference of the best one formitosis classification. Eight, we propose an efficient and generic strategy to explore largeimages like WSI by combining computational geometry tools with a local signal measureof relevance in a dynamic sampling framework.The evaluation of these frameworks is done in MICO (COgnitive MIcroscopy, ANRTecSan project) platform prototyping initiative. We thus tested our proposed frameworks on MITOS international contest dataset initiated by this project. For the color framework,we manage to rank second during the contest. Furthermore, our multispectral frameworkoutperforms significantly the top methods presented during the contest. Finally, ourframeworks allow us reaching the same level of accuracy in mitosis detection on brightlightas multispectral datasets, a promising result on the way to clinical evaluation and routine.; La gradation de lames de biopsie fournit des informations pronostiques essentielles pour le diagnostic et le traitement. La détection et le comptage manuel des mitoses est un travail fastidieux, sujet à des variations inter-et intra- observateur considérables. L'objectif principal de cette thèse de doctorat est le développement d'un système capable de fournir une détection des mitoses sur des images provenant de différents types de scanners rapides automatiques, ainsi que d'un microscope multispectral. L'évaluation des différents systèmes proposés est effectuée dans le cadre du projet MICO (MIcroscopie COgnitive, projet ANR TecSan piloté par notre équipe). Dans ce contexte, les systèmes proposés ont été testés sur les données du benchmark MITOS. En ce qui concerne les images couleur, notre système s'est ainsi classé en deuxième position de ce concours international, selon la valeur du critère F-mesure. Par ailleurs, notre système de détection de mitoses sur images multispectrales surpasse largement les meilleurs résultats obtenus durant le concours.