PPT-TP12 - Local features: detection and description

Computer Vision FCUP 201819 Miguel Coimbra Slides by Prof Kristen Grauman Today Local invariant features Detection of interest points Harris corner detection Scale invariant blob detection . ABQ Leak Locator brings years of systems engineering and in-depth technical problem solving methodology to the table to apply toward benefiting its clients and customers. we have evolved the process and methodology of leak detection and location into a science and can quickly and accurately locate leaks in homes, office buildings, swimming pools and space, as well as under streets and sidewalks, driveways, asphalt parking lots and even golf courses. 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 CSE 576. Face detection. State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Face detection. Where are the faces? . State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Consumer application: Apple . iPhoto. http://www.apple.com/ilife/iphoto/. State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Consumer application: Apple . iPhoto. http://www.apple.com/ilife/iphoto/. Can you detect an abrupt change in this picture?. Ludmila. I . Kuncheva. School of Computer Science. Bangor University. Answer – at the end. Plan. Zeno says there is no such thing as change.... If change exists, is it a good thing?. Problem motivation. Machine Learning. Anomaly detection example. Aircraft engine features:. . = heat generated. = vibration intensity. …. (vibration). (heat). Dataset:. New engine:. Density estimation. Oscar . Danielsson. (osda02@csc.kth.se). Stefan . Carlsson. (. stefanc@csc.kth.se. ). Josephine Sullivan (. sullivan@csc.kth.se. ). DICTA08. The Problem. Object categories are often modeled by collections (bag-of-features) or constellations (pictorial structures) of local features . Monday March . 7. Prof. Kristen . Grauman. UT-Austin. Midterm Wed.. Covers material up until 3/1. Solutions to practice exam handed out today. Bring a 8.5”x11” sheet of notes if you want. Review the outlines and notes on course website, accompanying reading in textbook. Statistical Features for Image Splicing Detection. Xudong. Zhao, . Shilin. Wang, . Shenghong. Li and . Jianhua. Li. Shanghai Jiao Tong University, Shanghai P. R. China. Introduction. Digital Image Forensics:. (Courtesy . Boris . Babenko. ). slides adapted from Svetlana . Lazebnik. Face detection and recognition. Detection. Recognition. “Sally”. Consumer application: Apple . iPhoto. http://www.apple.com/ilife/iphoto/. Devi Parikh. Slide credit: Kristen . Grauman. 1. Disclaimer: Most slides have been borrowed from Kristen . Grauman. , who may have borrowed some of them from others. Any time a slide did not already have a credit on it, I have credited it to Kristen. So there is a chance some of these credits are inaccurate.. 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 .