熱影像可獲取不同於可見光與近紅外光的溫度資訊，可運用於監測地表火山及斷層帶的溫度或災害防治上。以往於空載或衛載上的熱感測器解析度皆較低，判釋熱影像受到限制；如今，低成本、高機動性的無人飛行載具發展趨於成熟，可搭載熱感測器，並近空垂直拍攝近景熱影像，得到較高空間解析度之熱影像。 然而，熱影像上之地物內容與邊緣較一般可見光影像模糊，若要將熱影像應用於地理空間資訊系統上時，為使熱影像可與其他地面坐標資料結合，勢必需先幾何改正熱影像，並以相同區域之數值地表模型，正射化熱影像，同時三維展示熱影像與地表模型，提供研究者地形與熱分佈資訊；此外，對於火山地帶來說，高程資料也常是研究者判釋分析的重點資訊，此做法可看出區域之溫度分佈。 為正射糾正熱影像，利用共線式執行空中三角測量平差，本研究不僅率定熱像儀，求其內方外元素，更以空中三角測量平差，計算熱影像之外方位元素。此外，因熱影像紀錄地表輻射資訊，與可見光資訊大不相同，故熱影像經共線式空中三角測量平差後，建製之數值地表模型 (Digital Surface Model, DSM)，並非該拍攝地區之真實地表起伏模型，因此本研究利用一既有的DSM，正射糾正空中三角測量後之熱影像，並以誤差向量圖表示正射糾正之成果。 / Usually, thermal images contain abundant temperature information which can often be used to monitor the surface temperature or volcanic disaster prevention. Previously, thermal images acquired by satellite platform have low resolution. Today, low-cost, highly maneuverable unmanned aerial vehicle (UAV) can carry thermal sensors and obtain close-range thermal images with high spatial resolution. Due to the distortion of thermal sensor, geometric correction should be applied to the thermal images. In this study, a UAV-borne thermal sensor has been calibrated, and used for taking thermal images. The exterior orientation elements of the thermal images have been determined by using aerial triangulation. A digital surface model generated by LiDAR was then used to ortho-rectify the thermal images. Gray values of the rectified thermal images were also normalized for generating a thermal mosaic. The resultant rectified thermal mosaic has excellent appearance for showing the temperature distribution and elevation simultaneously.

Packages that are left unattended in public spaces are a security concern and timely detection of these packages is important for prevention of potential threats. Operators should be always alert to detect abandoned items in crowded environments. However, it is very difficult for operators to stay concentrated for extended periods. Therefore, it is important to aid operators with automatic detection of abandoned items. Most of the methods in the literature define abandoned items as items newly added to the scene and stayed stationary for a predefined time. Hence other stationary objects, such as people sitting on a bench are also detected as suspicious objects resulting in a high number of false alarms. These false alarms could be prevented by discriminating suspicious items as living/nonliving objects. In this thesis, visible band and thermal band cameras are used together to analyze the interactions between humans and other objects. Thermal images help classification of objects using their heat signatures. This way, people and the objects they carry or left behind can be detected separately. Especially, it is aimed to detect abandoned items and discriminate living or nonliving objects

This thesis focuses on the development of an open source software capable of automatic face detection in an image captured by a thermal camera, followed by a temperature measuring. This software is supposed to aid in the COVID-19 pandemics. The developed software is independent of used thermal camera. In this thesis, I am using TIM400 thermal camera. The implementation of the face detection was achieved by an OpenCV module. The methods tested were Template Matching, Eigen Faces, and Cascade Classifier. The last-mentioned had the best results, hence was used in the final version of the software. Cascade Classifier is looking for the eyes and their surrounding area in the image, allowing the software to subsequently measure the temperature on the surface of one's forehead. One can therefore be wearing a face mask or a respirator safely. The temperature measuring works in real time and the software is able to capture several people at once. It then keeps a record of the temperature of each measured individual as well as the time of the measurement. The software as a whole is a part of an installation file compatible with the Windows operating system. The functionality of this software was tested – the video recordings are included in this thesis.

Thesis deals with problematic of raising digital image spacial resolution, mainly thermal image. There are mentioned methods of interpolation, panorama and super-resolution. Main topic of this thesis is super-resolution which is detailly described during the thesis. Finally there is a description of algorithm implementation and problems that may occur during the implemetation.

Images of human face are one of the most used biometric features in automatic identification. This article presents an approach which uses face images in thermal (infrared) spectrum for purpose of important face features (eyes position, head rotation) detection and identification.

No description available.

Individual tracking of objects in the video such as people and the luggages they carry is important for surveillance applications as it would enable deduction of higher level information and timely detection of potential threats. However, this is a challenging problem and many studies in the literature track people and the belongings as a single object. In this thesis, we propose using thermal band video data in addition to the visible band video data for tracking people and their belongings separately for indoor applications using their heat signatures. For object tracking step, an adaptive, fully automatic multi object tracking system based on mean-shift tracking method is proposed. Trackers are refreshed using foreground information to overcome possible problems which may occur due to the changes in object&rsquo / s size, shape and to handle occlusion, split and to detect newly emerging objects as well as objects that leave the scene. By using the trajectories of objects, owners of the objects are found and abandoned objects are detected to generate an alarm. Better tracking performance is also achieved compared a single modality as the thermal reflection and halo effect which adversely affect tracking are eliminated by the complementing visible band data.

The range of missions performed by Unmanned Aircraft Systems (UAS) has been steadily growing in the past decades thanks to continued development in several disciplines. The goal of increasing the autonomy of UAS's is widening the range of tasks which can be carried out without, or with minimal, external help. This thesis presents methods for increasing specific aspects of autonomy of UAS's operating both in outdoor and indoor environments where cameras are used as the primary sensors. First, a method for fusing color and thermal images for object detection, geolocation and tracking for UAS's operating primarily outdoors is presented. Specifically, a method for building saliency maps where human body locations are marked as points of interest is described. Such maps can be used in emergency situations to increase the situational awareness of first responders or a robotic system itself. Additionally, the same method is applied to the problem of vehicle tracking. A generated stream of geographical locations of tracked vehicles increases situational awareness by allowing for qualitative reasoning about, for example, vehicles overtaking, entering or leaving crossings. Second, two approaches to the UAS indoor localization problem in the absence of GPS-based positioning are presented. Both use cameras as the main sensors and enable autonomous indoor ight and navigation. The first approach takes advantage of cooperation with a ground robot to provide a UAS with its localization information. The second approach uses marker-based visual pose estimation where all computations are done onboard a small-scale aircraft which additionally increases its autonomy by not relying on external computational power.

No description available.

Heterogeneous data with different characteristics are ubiquitous in the modern digital world. For example, the observations collected from a process may change on its mean or variance. In numerous applications, data are often of mixed types including both discrete and continuous variables. Heterogeneity also commonly arises in data when underlying models vary across different segments. Besides, the underlying pattern of data may change in different dimensions, such as in time and space. The diversity of heterogeneous data structures makes statistical modeling and analysis challenging. Detection of change-points in heterogeneous data has attracted great attention from a variety of application areas, such as quality control in manufacturing, protest event detection in social science, purchase likelihood prediction in business analytics, and organ state change in the biomedical engineering. However, due to the extraordinary diversity of the heterogeneous data structures and complexity of the underlying dynamic patterns, the change-detection and analysis of such data is quite challenging. This dissertation aims to develop novel statistical modeling methodologies to analyze four types of heterogeneous data and to find change-points efficiently. The proposed approaches have been applied to solve real-world problems and can be potentially applied to a broad range of areas. / Ph. D.