PPT-Introduction to Change Detection

Lecture 5 Data access applications Instructor Lila Leatherman theythem November 1718 2021 High Resolution Data Resources FS EDW and Image Services NAIP Imagery Google Earth Engine NAIP Imagery and others. 02nT Faster cycle rates Up to 10Hz Longer range detection Pros brPage 5br Magnetometers Magnetometers Large distant targets mask small local targets Difficult to pick out small target due to background noise No sense of direction of target on single Mahmoud. . Abdallah. Daniel . Eiland. The detection of traffic signals within a moving video is problematic due to issues caused by:. Low-light, Day and Night situations. Inter/Intra-frame motion. Similar light sources (such as tail lights). Introduction and Use Cases. Derick . Winkworth. , Ed Henry and David Meyer. Agenda. Introduction and a Bit of History. So What Are Anomalies?. Anomaly Detection Schemes. Use Cases. Current Events. Q&A. Main Advantages. H . 2. 1. Fiber Optics Technology. . -Covert design. Caused no physical alteration to present building outlook. -Full Fiber Structure thus immune to lightning strike and EMI. Ross . Girshick. , Jeff Donahue, Trevor Darrell, . Jitandra. Malik (UC Berkeley). Presenter: . Hossein. . Azizpour. Abstract. Can CNN improve . s.o.a. . object detection results?. Yes, it helps by learning rich representations which can then be combined with computer vision techniques.. applications. The 10th IEEE Conference on Industrial Electronics and Applications (ICIEA 2015. ), Auckland , 15-17 June 2015. Kai Ki Lee. 1. ,Ying Kin Yu. 2. and Kin Hong . Wong. 2+. 1. Dept. of Information Engineering, The Chinese University of Hong Kong (CUHK). 2. /86. Contents. Statistical . methods. parametric. non-parametric (clustering). Systems with learning. 3. /86. Anomaly detection. Establishes . profiles of normal . user/network behaviour . Compares . Abstract. Link error and malicious packet dropping are two sources for packet losses in multi-hop wireless ad hoc network. In this paper, while observing a sequence of packet losses in the network, we are interested in determining whether the losses are caused by link errors only, or by the combined effect of link errors and malicious drop. . Problem motivation. Machine Learning. Anomaly detection example. Aircraft engine features:. . = heat generated. = vibration intensity. …. (vibration). (heat). Dataset:. New engine:. Density estimation. Elwha . River Restoration Project. . About the Project. Aerial photography of damns before removal. Time Lapse of . Glines. Damn Removal. Blasting of . Glines. Damn. ROI of Dark Regions. Tool Used. Colors represent nature of change (. e.g. ., more or less greenness, . etc.. ). USGS Image. 2011 Minimum. 2011 Sea ice extent. NASA Video. Change Detection in Remote Sensing. Using multi-temporal satellite imagery to measure change. in Tensors . with Quality Guarantees. Kijung Shin. , Bryan . Hooi. , Christos . Faloutsos. Carnegie Mellon University . Motivation: Review Fraud. M-Zoom:. Fast Dense-Block Detection in Tensors with Quality Guarantees . State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Face detection. Where are the faces? . Face Detection. What kind of features?. Xindian. Long. 2018.09. Outline. Introduction. Object Detection Concept and the YOLO Algorithm. Object Detection Example (CAS Action). Facial Keypoint Detection Example (. DLPy. ). Why SAS Deep Learning .