Saliency-guided Adaptive Seeding for Supervoxel Segmentation

We propose a new saliency-guided method for generating supervoxels in 3D space. Rather than using an evenly distributed spatial seeding procedure, our method uses visual saliency to guide the process of supervoxel generation. This results in densely distributed, small, and precise supervoxels in salient regions which often contain objects, and larger supervoxels in less salient regions that often correspond to background. Our approach largely improves the quality of the resulting supervoxel segmentation in terms of boundary recall and under-segmentation error on publicly available benchmarks.Comment: 6 pages, accepted to IROS201

ПРИМЕНЕНИЕ МЕТОДОВ ОБНАРУЖЕНИЯ ОБЪЕКТОВ К ИЗОБРАЖЕНИЯМ ВЗЛЕТНО-ПОСАДОЧНОЙ ПОЛОСЫ, ПОЛУЧЕННЫМ В УСЛОВИЯХ ПЛОХОЙ ВИДИМОСТИ

When ensuring aviation safety, the outboard environment awareness of the crew in low visibility conditions is especially important. The information about the runway condition and availability of any obstacles is crucial. There are ground-based obstacle detection systems, but currently only large airports are equipped with them. There are Enhanced Vision Systems designed for application on aircraft in low visibility conditions. The main goal of this research is to develop the means of runway obstacle recognition in low visibility conditions, which are to improve the capabilities of Enhanced Vision Systems. The research covers only the methods for static image object detection. The analysis of the runway markings, objects and possible obstacles is performed. Targets for acquisition are defined. The simulation of runway images is performed on full-flight simulator in low visibility conditions. The requirements for features descriptors, recognition and detection methods are defined and methods for research are defined. The paper provides evaluation of method applicability to runway pictures taken in poor visibility conditions above and below the decision height taking into account various characteristics. The covered methods solve the problem of detecting objects of the runway in low visibility conditions for static image. Conclusions about the possibility to use the studied methods in Enhanced Vision Systems are made. Further development of optimization methods is required to perform detection in video sequences in real time. The results of this work are relevant to the tasks of avionics, computer vision and image processing.При обеспечении безопасности движения самолета особенно важна осведомленность экипажа о закабинном пространстве в условиях плохой видимости. Важнейшую роль играет информация о состоянии взлетно-посадочной полосы (ВПП) и о наличии на ней препятствий. Существуют наземные системы обнаружения препятствий, но в настоящее время такими системами оборудованы лишь крупные аэропорты. Альтернативой могут служить системы улучшенного видения, используемые на воздушном судне в условиях плохой видимости. Цель представленного в настоящей статье исследования – разработка средств обнаружения препятствий на ВПП в условиях плохой видимости, которые должны расширить возможности систем улучшенного видения. В рамках исследования рассмотрены методы обнаружения объектов только на статичных изображениях. Проведен анализ разметки, объектов ВПП и возможных типов препятствий. Определены цели для обнаружения. На комплексном авиационном тренажере выполнено моделирование снимков ВПП в условиях плохой видимости. В качестве моделируемой цели для обнаружения выбрано воздушное судно на ВПП, потерявшее способность двигаться. Сформулированы требования к дескрипторам признаков, методам распознавания и обнаружения, выбраны методы для исследования. Проведена оценка применимости методов к изображениям ВПП, полученным в условиях плохой видимости выше и ниже высоты принятия решения с учетом различных характеристик. Исследованные методы решают задачу обнаружения объектов ВПП в условиях плохой видимости для статичного изображения. Сформулированы выводы о возможности применения исследованных методов в системах улучшенного видения. В дальнейшем требуется разработка методов оптимизации для обеспечения обнаружения на видеопоследовательности в режиме реального времени. Результаты представленной работы актуальны в задачах авиаприборостроения, компьютерного видения и обработки изображений

Density Estimates as Representations of Agricultural Fields for Remote Sensing-Based Monitoring of Tillage and Vegetation Cover

We consider the use of remote sensing for large-scale monitoring of agricultural land use, focusing on classification of tillage and vegetation cover for individual field parcels across large spatial areas. From the perspective of remote sensing and modelling, field parcels are challenging as objects of interest due to highly varying shape and size but relatively uniform pixel content and texture. To model such areas we need representations that can be reliably estimated already for small parcels and that are invariant to the size of the parcel. We propose representing the parcels using density estimates of remote imaging pixels and provide a computational pipeline that combines the representation with arbitrary supervised learning algorithms, while allowing easy integration of multiple imaging sources. We demonstrate the method in the task of the automatic monitoring of autumn tillage method and vegetation cover of Finnish crop fields, based on the integrated analysis of intensity of Synthetic Aperture Radar (SAR) polarity bands of the Sentinel-1 satellite and spectral indices calculated from Sentinel-2 multispectral image data. We use a collection of 127,757 field parcels monitored in April 2018 and annotated to six tillage method and vegetation cover classes, reaching 70% classification accuracy for test parcels when using both SAR and multispectral data. Besides this task, the method could also directly be applied for other agricultural monitoring tasks, such as crop yield prediction

Density Estimates as Representations of Agricultural Fields for Remote Sensing-Based Monitoring of Tillage and Vegetation Cover

We consider the use of remote sensing for large-scale monitoring of agricultural land use, focusing on classification of tillage and vegetation cover for individual field parcels across large spatial areas. From the perspective of remote sensing and modelling, field parcels are challenging as objects of interest due to highly varying shape and size but relatively uniform pixel content and texture. To model such areas we need representations that can be reliably estimated already for small parcels and that are invariant to the size of the parcel. We propose representing the parcels using density estimates of remote imaging pixels and provide a computational pipeline that combines the representation with arbitrary supervised learning algorithms, while allowing easy integration of multiple imaging sources. We demonstrate the method in the task of the automatic monitoring of autumn tillage method and vegetation cover of Finnish crop fields, based on the integrated analysis of intensity of Synthetic Aperture Radar (SAR) polarity bands of the Sentinel-1 satellite and spectral indices calculated from Sentinel-2 multispectral image data. We use a collection of 127,757 field parcels monitored in April 2018 and annotated to six tillage method and vegetation cover classes, reaching 70% classification accuracy for test parcels when using both SAR and multispectral data. Besides this task, the method could also directly be applied for other agricultural monitoring tasks, such as crop yield prediction.Peer reviewe