Developing Agent-Based Model for Colorization

 التلوين هو اضافة الالوان الى صور الابيض والاسود لتصبح صور ملونة. التلوين يجذب الباحثين لكونه يخدم كثير من التطبيقات مثل صفحات الويب ومعالجة الصور الطبية. ان حجم البينات المتوقع يفرض ان تكون العملية الية. في هذا البحث، تم تطوير طريقة مستوحاة من الانظمة الطبيعية لنقل الالوان من الصور الملونة الى الصور الرمادية. يمكن تنفيذ الخوارزمية المقترحة بسهولة في انظمة الحوسبة المتوازية او الموزعة. كما يمكن تطبيق النموذج على انواع متنوعة من الصور مع الحفاظ على خصائص الصور كالاضاءة والنسج. اظهرت الصور الناتجة امكانية تطبيق الاسلوب في مجالات متنوعة. تم اجراء محاكاة لطريقة الحل باستخدام بيئة النت لوكوColorization is adding colors to black and white images. Colorization attracts the interest of researchers as it serves wide are of applications such as web technology and medical images processing. The expected large dimensionality of source datasets, imposes the automation is mandatory. In this paper, a natural inspired solution is developed for automatic color transferring from colored to grayscale images. The proposed algorithm can be easily implemented in parallel and distributed environment. We show that our technique can be applied on broad image types, with preserving image features such as texture and luminance. The resulting images make our technique applicable in verity domains. The algorithm is simulated and result is presented using NetLogo tool

Vers plus d'expressivité dans les représentations graphiques du territoire

International audienceThis paper addresses the issue of expressivity conveyed by the graphical representations of territory. In order to improve the relevancy between graphical representations and effective uses of those representations, expressivity should be enhanced in map and geovisualisation design. Based on knowledge and methods coming from expressive rendering, a sub-domain of computer graphics, we proposed specific rendering techniques for map and geovisualisation design, from the specification of styles. Two stylisation projects, in map design (MapStyle) and in 3D geovisualisation design (Plu++), provide a high diversity of graphical representations of the territory, aiming at making them relevant for many uses. Exploring the space of possible graphical representations is also a way to approach the issue of perception and cognition of complex phenomena on the territory.Cet article questionne la notion d'expressivité dans les représentations graphiques du territoire. Afin d'améliorer l'adéquation entre représentations graphiques du territoire et usages de ces représentations pour mieux comprendre le territoire, une approche consiste à rendre les représentations plus expressives. S'inspirer des techniques de rendu expressif, un domaine de l'informatique graphique, a permis de proposer des techniques de rendu spécifiques pour la cartographie et la géovisualisation, à partir de la spécification de styles. Deux projets de stylisation en cartographie (MapStyle) et en géovisualisation 3D (Plu++) ont permis de construire différentes représentations graphiques visant à répondre à des usages variés des représentations graphiques du territoire. Pouvoir explorer l'espace des possibles dans les représentations graphiques est ainsi une façon d'approcher le problème de la perception et de la compréhension de phénomènes complexes sur le territoire

Development of cartographic styling tool to support geospatial data interoperability

Cartographic styling is a technique used to present geographic data layers in various ways, and controls the appearance of geospatial data. Current practices used to maintain and store cartographic styling are through stylesheet formats, such as Styled Layer Descriptor, Esri layer file, and QGIS Style. However, the use of these formats in current geospatial applications is limited, especially in cross-platform applications. Therefore, a geospatial data format called GeoPackage has been used in this study to provide a new technique of maintaining cartographic styling, apart from the current practices. GeoPackage is an emerging geospatial data format introduced by Open Geospatial Consortium (OGC), with features including open-standard, independent, portable, robust, and cross-platform applications. In this study, a styling extension for GeoPackage was designed and developed to support the storage of styling data. The development of styling extension involves creation of data tables that is styling data model into the existing GeoPackage data model. The main function of the styling data model is to store styling records for geographic data layers within the GeoPackage. The capabilities of the new data model were tested in cross-platform applications including Windows, Linux, and Mac operating system. The testing was limited to vector data types such as point, line, and polygon, which represent geographic data layers. Results show that GeoPackage with the built-in styling extension is capable to store styling data, which can be loaded to cross-platform applications without the need for format conversion. In addition, the extension stores styling records together with the geographical data layers in a single file format (i.e. *.gpkg), in contrast to the use of other stylesheets, which store styling records in separate file format. This is possible because GeoPackage is a cross-platform geospatial data format that supports interoperability and thus, only requires single file format. Finally, this study successfully explores the capability of GeoPackage data format in maintaining and storing cartographic styling

Mobile Robotic Painting of Texture

Robotic painting is well-established in controlled factory environments, but there is now potential for mobile robots to do functional painting tasks around the everyday world. An obvious first target for such robots is painting a uniform single color. A step further is the painting of textured images. Texture involves a varying appearance, and requires that paint is delivered accurately onto the physical surface to produce the desired effect. Robotic painting of texture is relevant for architecture and in themed environments. A key challenge for robotic painting of texture is to take a desired image as input, and to generate the paint commands to as closely as possible create the desired appearance, according to the robotic capabilities. This paper describes a deep learning approach to take an input ink map of a desired texture, and infer robotic paint commands to produce that texture. We analyze the trade-offs between quality of reconstructed appearance and ease of execution. Our method is general for different kinds of robotic paint delivery systems, but the emphasis here is on spray painting. More generally, the framework can be viewed as an approach for solving a specific class of inverse imaging problems

Mixing Modalities of 3D Sketching and Speech for Interactive Model Retrieval in Virtual Reality

Sketch and speech are intuitive interaction methods that convey complementary information and have been independently used for 3D model retrieval in virtual environments. While sketch has been shown to be an effective retrieval method, not all collections are easily navigable using this modality alone. We design a new challenging database for sketch comprised of 3D chairs where each of the components (arms, legs, seat, back) are independently colored. To overcome this, we implement a multimodal interface for querying 3D model databases within a virtual environment. We base the sketch on the state-of-the-art for 3D Sketch Retrieval, and use a Wizard-of-Oz style experiment to process the voice input. In this way, we avoid the complexities of natural language processing which frequently requires fine-tuning to be robust. We conduct two user studies and show that hybrid search strategies emerge from the combination of interactions, fostering the advantages provided by both modalities

Recognizing Elementary Elements in Chemical Diagram Sketches

Organic Chemistry is a challenging subject that requires dedicated practice to learn the meticulous rules composing the subject, otherwise a student risks failure. Current software to teach chemical structures contains drag-and-drop components and fails to provide students with true understanding of Organic Chemistry concepts. My solution is to integrate a sketch recognition interface that can learn to recognize components of various, user-sketched chemical structures with a back-propagation neural network that can be trained to translate the components of the chemical structure to determine correctness. The accuracy of the program will be rigorously tested to determine correctness in interpreting chemical structures