ІНТЕРПРЕТАЦІЯ ХУДОЖНІХ ТЕКСТІВ У ІНТЕРМЕДІАЛЬНОМУ ВИМІРІ ФРАНСІС ПУЛЕНК «ЗАРУЧИНИ ЖАРТОМА»

The purpose of the article is to analyze the peculiarities of the interpretation of artistic texts of the vocal cycle of F. Poulenc on the poem by Louise de Wilmoren "Engagement is a joke" in the intermedial dimension. In addition to the general characteristics of the chamber-vocal cycle of F. Poulenc, the analysis of the poetic text, the peculiarities of its compositional reading, to comprehend the significance of the form of French vocal culture "musical portrait". Outline intermediality as a phenomenon of art in the space of culture. Methodology: comparative, structural methods and narrative analysis to understand the deep psychological processes that occur during the interpretation of "musical portraits". The scientific novelty is the study of the interaction of poetic style and form of "musical portrait" as the basis of internal textual connections of the work of art, and the relationship of the artistic language of different arts, which are coordinated in the process of interpretation into the intermediality space. Conclusions. The musical portrayal is substantiated as a kind of intermedia interpretation of artistic texts. "Musical portrait" as a form of French vocal culture is a full-fledged semiotic space, with a universal textual category of intertextuality, along with the specific characteristics of its sign system, conveys "figurative" information. Correlation of poetic texts, features of intersemiotic translation of figurative structures which carry information by means and at the expense of aesthetic possibilities of other kinds of arts - is an actual theme of the present. Correlation of poetic texts, features of intersemiotic translation of figurative structures which carry information by means and at the expense of aesthetic possibilities of other kinds of arts - is an actual theme of the present. A special type of intratextual relationships is formed in a work of art - intermediality, based on the interaction of artistic codes of different types of art.Мета дослідження: проаналізувати особливості інтерпретації художніх текстів вокального циклу Ф. Пуленка на вірші Луїзи де Вільморен «Заручини жартома» в інтермедіальному вимірі. Крім загальної характеристики камерно-вокального циклу Ф. Пуленка, аналізу поетичного тексту, особливостей його композиторського прочитання, осмислити значущість форми французького вокального культури «музичного портрета». Окреслити інтермедіальність як явище мистецтва у просторі культури. Методологія дослідження полягає в застосовані: компаративного, структурного методів та наративного аналізу для розуміння глибинних психологічних процесів, що виникають під час інтерпретації «музичних портретів». Наукова новизна полягає у дослідженні взаємовпливу поетичного стилю та форми «музичного портрета» як основи внутрішньо текстових зв’язків художнього твору, та взаємозв’язків художньої мови різних видів мистецтв, що узгоджуються в процесі інтерпретації в інтермедіальний простір. Висновки Музичне портретування обґрунтовано як різновид інтермедіальної інтерпретації художніх текстів. «Музичний портрет» як форма французької вокальної культури є повноцінним семіотичним простором, з універсальною текстовою категорією інтертекстуальності, наряду із специфічними характеристиками своєї знакової системи, що передає «образну» інформацію. Кореляція поетичних текстів, особливостей інтерсеміотичного перекладу образних структур які несуть інформацію засобами і за рахунок естетичних можливостей інших видів мистецтв – є актуальною темою сучасності. Утворюється особливий тип внутрітекстових взаємозв'язків у художньому творі – інтермедіальність, яка ґрунтується на взаємодії художніх кодів різних видів мистецтв. Тобто, розуміється як створення цілісного поліхудожнього простору в системі культури, і як особливий тип внутрітекстових взаємин у художньому творі, де взаємодіють різні види мистецтва. Інтермедіальність надає безліч можливостей інтертекстуальніх зв’язків, від вербальних знаків до будь яких художніх. Багаторівневість дозволяє взаємодію з будь-яким компонентом художніх текстів, з яким він вступає в інтертекстуальний зв'язок

Localization and segmentation of optic disc in retinal images using circular Hough transform and grow-cut algorithm

Automated retinal image analysis has been emerging as an important diagnostic tool for early detection of eye-related diseases such as glaucoma and diabetic retinopathy. In this paper, we have presented a robust methodology for optic disc detection and boundary segmentation, which can be seen as the preliminary step in the development of a computer-assisted diagnostic system for glaucoma in retinal images. The proposed method is based on morphological operations, the circular Hough transform and the grow-cut algorithm. The morphological operators are used to enhance the optic disc and remove the retinal vasculature and other pathologies. The optic disc center is approximated using the circular Hough transform, and the grow-cut algorithm is employed to precisely segment the optic disc boundary. The method is quantitatively evaluated on five publicly available retinal image databases DRIVE, DIARETDB1, CHASE_DB1, DRIONS-DB, Messidor and one local Shifa Hospital Database. The method achieves an optic disc detection success rate of 100% for these databases with the exception of 99.09% and 99.25% for the DRIONS-DB, Messidor, and ONHSD databases, respectively. The optic disc boundary detection achieved an average spatial overlap of 78.6%, 85.12%, 83.23%, 85.1%, 87.93%, 80.1%, and 86.1%, respectively, for these databases. This unique method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc

Relation between plaque type, plaque thickness, blood shear stress, and plaque stress in coronary arteries assessed by X-ray Angiography and Intravascular Ultrasound

Purpose: Atheromatic plaque progression is affected, among others phenomena, by biomechanical, biochemical, and physiological factors. In this paper, the authors introduce a novel framework able to provide both morphological (vessel radius, plaque thickness, and type) and biomechanical (wall shear stress and Von Mises stress) indices of coronary arteries. Methods: First, the approach reconstructs the three-dimensional morphology of the vessel from intravascular ultrasound(IVUS) and Angiographic sequences, requiring minimal user interaction. Then, a computational pipeline allows to automatically assess fluid-dynamic and mechanical indices. Ten coronary arteries are analyzed illustrating the capabilities of the tool and confirming previous technical and clinical observations. Results: The relations between the arterial indices obtained by IVUS measurement and simulations have been quantitatively analyzed along the whole surface of the artery, extending the analysis of the coronary arteries shown in previous state of the art studies. Additionally, for the first time in the literature, the framework allows the computation of the membrane stresses using a simplified mechanical model of the arterial wall. Conclusions: Circumferentially (within a given frame), statistical analysis shows an inverse relation between the wall shear stress and the plaque thickness. At the global level (comparing a frame within the entire vessel), it is observed that heavy plaque accumulations are in general calcified and are located in the areas of the vessel having high wall shear stress. Finally, in their experiments the inverse proportionality between fluid and structural stresses is observed

Associative embeddings for large-scale knowledge transfer with self-assessment

We propose a method for knowledge transfer between semantically related classes in ImageNet. By transferring knowledge from the images that have bounding-box annotations to the others, our method is capable of automatically populating ImageNet with many more bounding-boxes and even pixel-level segmentations. The underlying assumption that objects from semantically related classes look alike is formalized in our novel Associative Embedding (AE) representation. AE recovers the latent low-dimensional space of appearance variations among image windows. The dimensions of AE space tend to correspond to aspects of window appearance (e.g. side view, close up, background). We model the overlap of a window with an object using Gaussian Processes (GP) regression, which spreads annotation smoothly through AE space. The probabilistic nature of GP allows our method to perform self-assessment, i.e. assigning a quality estimate to its own output. It enables trading off the amount of returned annotations for their quality. A large scale experiment on 219 classes and 0.5 million images demonstrates that our method outperforms state-of-the-art methods and baselines for both object localization and segmentation. Using self-assessment we can automatically return bounding-box annotations for 30% of all images with high localization accuracy (i.e.~73% average overlap with ground-truth).Comment: A final CVPR version with a correction in (1). IEEE Computer Vision and Pattern Recognition, 201

Object localization in ImageNet by looking out of the window

We propose a method for annotating the location of objects in ImageNet. Traditionally, this is cast as an image window classification problem, where each window is considered independently and scored based on its appearance alone. Instead, we propose a method which scores each candidate window in the context of all other windows in the image, taking into account their similarity in appearance space as well as their spatial relations in the image plane. We devise a fast and exact procedure to optimize our scoring function over all candidate windows in an image, and we learn its parameters using structured output regression. We demonstrate on 92000 images from ImageNet that this significantly improves localization over recent techniques that score windows in isolation.Comment: in BMVC 201

Weakly supervised semantic segmentation with a multi-image model

We propose a novel method for weakly supervised semantic segmentation. Training images are labeled only by the classes they contain, not by their location in the image. On test images instead, the method predicts a class label for every pixel. Our main innovation is a multi-image model (MIM)- a graphical model for recovering the pixel labels of the training images. The model connects superpixels from all training images in a data-driven fashion, based on their appearance similarity. For generalizing to new test images we integrate them into MIM using a learned multiple kernel metric, instead of learning conventional classifiers on the recovered pixel labels. We also introduce an “objectness” potential, that helps separating objects (e.g. car, dog, human) from background classes (e.g. grass, sky, road). In experiments on the MSRC 21 dataset and the LabelMe subset of [18], our technique outperforms previous weakly supervised methods and achieves accuracy comparable with fully supervised methods. 1