Rendering of Wind Effects in 3D Landscape Scenes

AbstractVisualization of 3D landscape scenes is often used in architectural modeling systems, realistic simulators, computer virtual reality, and other applications. Wind is a common spread natural effect without which any scene would be unrealistic. Three algorithms for tree rendering under changeable wind parameters were developed. They have a minimal computational cost and simulate weak wind; mid-force wind, and storm wind. A 3D landscape scene is formed from a set of trees models that are generated from laser data and templates of L-systems. The user can tune the wind parameters and manipulate a modeling scene by using the designed software tool

Verification of Smoke Detection in Video Sequences Based on Spatio-temporal Local Binary Patterns

AbstractThe early smoke detection in outdoor scenes using video sequences is one of the crucial tasks of modern surveillance systems. Real scenes may include objects that are similar to smoke with dynamic behavior due to low resolution cameras, blurring, or weather conditions. Therefore, verification of smoke detection is a necessary stage in such systems. Verification confirms the true smoke regions, when the regions similar to smoke are already detected in a video sequence. The contributions are two-fold. First, many types of Local Binary Patterns (LBPs) in 2D and 3D variants were investigated during experiments according to changing properties of smoke during fire gain. Second, map of brightness differences, edge map, and Laplacian map were studied in Spatio-Temporal LBP (STLBP) specification. The descriptors are based on histograms, and a classification into three classes such as dense smoke, transparent smoke, and non-smoke was implemented using Kullback-Leibler divergence. The recognition results achieved 96–99% and 86–94% of accuracy for dense smoke in dependence of various types of LPBs and shooting artifacts including noise

Image inpainting based on self-organizing maps by using multi-agent implementation

AbstractThe image inpainting is a well-known task of visual editing. However, the efficiency strongly depends on sizes and textural neighborhood of “missing” area. Various methods of image inpainting exist, among which the Kohonen Self-Organizing Map (SOM) network as a mean of unsupervised learning is widely used. The weaknesses of the Kohonen SOM network such as the necessity for tuning of algorithm parameters and the low computational speed caused the application of multi- agent system with a multi-mapping possibility and a parallel processing by the identical agents. During experiments, it was shown that the preliminary image segmentation and the creation of the SOMs for each type of homogeneous textures provide better results in comparison with the classical SOM application. Also the optimal number of inpainting agents was determined. The quality of inpainting was estimated by several metrics, and good results were obtained in complex images

Верификация разливов нефти на водных поверхностях по аэрофотоснимкам на основе методов глубокого обучения

В статье решается задача верификации разливов нефти на водных поверхностях рек, морей и океанов по оптическим аэрофотоснимкам с использованием методов глубокого обучения. Особенностью данной задачи является наличие визуально похожих на разливы нефти областей на водных поверхностях, вызванных цветением водорослей, веществ, не приносящих экологический ущерб (например, пальмовое масло), бликов при съемке или природных явлений (так называемые «двойники»). Многие исследования в данной области основаны на анализе изображений, полученных от радаров с синтезированной апертурой (Synthetic Aperture Radar (SAR) images), которые не обеспечивают точной классификации и сегментации. Последующая верификация способствует сокращению экологического и материального ущерба, а мониторинг размеров площади нефтяного пятна используется для принятия дальнейших решений по устранению последствий. Предлагается новый подход к верификации оптических снимков как задачи бинарной классификации на основе сиамской сети, когда фрагмент исходного изображения многократно сравнивается с репрезентативными примерами из класса нефтяных пятен на водных поверхностях. Основой сиамской сети служит облегченная сеть VGG16. При превышении порогового значения выходной функции принимается решение о наличии разлива нефти. Для обучения сети был собран и размечен собственный набор данных из открытых интернет-ресурсов. Существенной проблемой является несбалансированность выборки данных по классам, что потребовало применения методов аугментации, основанных не только на геометрических и цветовых манипуляциях, но и на основе генеративной состязательной сети (Generative Adversarial Network, GAN). Эксперименты показали, что точность классификации разливов нефти и «двойников» на тестовой выборке достигает значений 0,91 и 0,834 соответственно. Далее решается дополнительная задача семантической сегментации нефтяного пятна с применением сверточных нейронных сетей (СНС) типа кодировщик-декодировщик. Для сегментации исследовались три архитектуры глубоких сетей, а именно U-Net, SegNet и Poly-YOLOv3. Лучшие результаты показала сеть Poly-YOLOv3, достигнув точности 0,97 при среднем времени обработки снимка 385 с веб-сервисом Google Colab. Также была спроектирована база данных для хранения исходных и верифицированных изображений с проблемными областями

Deep Learning for Visual SLAM: The State-of-the-Art and Future Trends

Visual Simultaneous Localization and Mapping (VSLAM) has been a hot topic of research since the 1990s, first based on traditional computer vision and recognition techniques and later on deep learning models. Although the implementation of VSLAM methods is far from perfect and complete, recent research in deep learning has yielded promising results for applications such as autonomous driving and navigation, service robots, virtual and augmented reality, and pose estimation. The pipeline of traditional VSLAM methods based on classical image processing algorithms consists of six main steps, including initialization (data acquisition), feature extraction, feature matching, pose estimation, map construction, and loop closure. Since 2017, deep learning has changed this approach from individual steps to implementation as a whole. Currently, three ways are developing with varying degrees of integration of deep learning into traditional VSLAM systems: (1) adding auxiliary modules based on deep learning, (2) replacing the original modules of traditional VSLAM with deep learning modules, and (3) replacing the traditional VSLAM system with end-to-end deep neural networks. The first way is the most elaborate and includes multiple algorithms. The other two are in the early stages of development due to complex requirements and criteria. The available datasets with multi-modal data are also of interest. The discussed challenges, advantages, and disadvantages underlie future VSLAM trends, guiding subsequent directions of research

Computer vision in control systems

Volume 1 : This book is focused on the recent advances in computer vision methodologies and technical solutions using conventional and intelligent paradigms. The Contributions include: ·         Morphological Image Analysis for Computer Vision Applications. ·         Methods for Detecting of Structural Changes in Computer Vision Systems. ·         Hierarchical Adaptive KL-based Transform: Algorithms and Applications. ·         Automatic Estimation for Parameters of Image Projective Transforms Based on Object-invariant Cores. ·         A Way of Energy Analysis for Image and Video Sequence Processing. ·         Optimal Measurement of Visual Motion Across Spatial and Temporal Scales. ·         Scene Analysis Using Morphological Mathematics and Fuzzy Logic. ·         Digital Video Stabilization in Static and Dynamic Scenes. ·         Implementation of Hadamard Matrices for Image Processing. ·         A Generalized Criterion of Efficiency for Telecommunication Systems. The book is directed to PhD students, professors, researchers and software developers working in the areas of digital video processing and computer vision technologies.Volume 2 : The research book is focused on the recent advances in computer vision methodologies and innovations in practice. The Contributions include: ·          Human Action Recognition: Contour-Based and Silhouette-based Approaches. ·         The Application of Machine Learning Techniques to Real Time Audience Analysis System. ·         Panorama Construction from Multi-view Cameras in Outdoor Scenes. ·         A New Real-Time Method of Contextual Image Description and Its Application in Robot Navigation and Intelligent Control. ·         Perception of Audio Visual Information for Mobile Robot Motion Control Systems. ·         Adaptive Surveillance Algorithms Based on the Situation Analysis. ·         Enhanced, Synthetic and Combined Vision Technologies for Civil Aviation. ·         Navigation of Autonomous Underwater Vehicles Using Acoustic and Visual Data Processing. ·         Efficient Denoising Algorithms for Intelligent Recognition Systems. ·         Image Segmentation Based on Two-dimensional Markov Chains. The book is directed to the PhD students, professors, researchers and software developers working in the areas of digital video processing and computer vision technologies

Texture analysis in watermarking paradigms

Текст статьи не публикуется в открытом доступе в соответствии с политикой журнала.Digital watermarking algorithms have been developed rapidly as a response on the challenges caused by various internet attacks that are distorted the content of the host image and watermark partially or fully. In this paper, the issues of texture analysis with a goal to detect the most suitable image areas for embedding are discussed. The statistical and model-based methods are investigated as a trade-off between the computational cost and quality of the detected areas, where the embedded bits of a watermark could be the most invisible for a human vision. The criteria for detection of such areas based on the textural, contrast, illumination, and color coherence of the host image and watermark are formulated. The experiments show that the statistical methods based on the gradient oriented Local Binary Patterns (LBP) provide better computational time regarding to fractal estimation of textural image areas