PPT-Rich feature Hierarchies for Accurate object detection and

Ross Girshick Jeff Donahue Trevor Darrell Jitandra Malik UC Berkeley Presenter Hossein Azizpour Abstract Can CNN improve soa object detection results Yes it helps by learning rich representations which can then be combined with computer vision techniques. Binarized. Normed Gradients for . Objectness. Estimation at 300fps. CVPR 2014 Oral. Outline. 1. . Introduction. 2.. . Methodology. 2.1 Normed . gradients (NG) and . objectness. 2.2 Learning . objectness. 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?. Oscar . Danielsson. (osda02@kth.se). Stefan . Carlsson. (. stefanc@kth.se. ). Outline. Detect all Instances of an Object Class. The classifier needs to be fast (on average). This is typically accomplished by:. for Object Detection. Forrest Iandola, . Ning. Zhang, Ross . Girshick. , Trevor Darrell, and Kurt . Keutzer. Deformable Parts Model (DPM): state of the art algorithm for object detection [1]. Several attempts to accelerate multi-category DPM detection, such as [2] [3]. Image Processing. Pier Luigi Mazzeo. pierluigi.mazzeo@cnr.it. Image Rotation &. Object . Detection . Find. Image . Rotation. and Scale Using . Automated. . Feature. . Matching. and RANSAC. Step. Effectiveness and Limitations. Yuan Zhou. Computer Science Department. Carnegie Mellon University. 1. Combinatorial Optimization. Goal:. optimize an objective function of . n. 0-1 variables. Subject to: . 1. Content. What is . OpenCV. ?. What is face detection and . haar. cascade classifiers?. How to make face detection in Java using . OpenCV. Live Demo. Problems in face detection process. How to improve face detection. . SYFTET. Göteborgs universitet ska skapa en modern, lättanvänd och . effektiv webbmiljö med fokus på användarnas förväntningar.. 1. ETT UNIVERSITET – EN GEMENSAM WEBB. Innehåll som är intressant för de prioriterade målgrupperna samlas på ett ställe till exempel:. 2020 Census LUCA L OCAL U PDATE OF C ENSUS A DDRESSES What is LUCA? LUCA is the ONLY opportuni ty offered to local governments to review and comment on the U.S. Census Bureau's residentia 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?. transcriptome. sequencing data. Jorge Duitama. 1. , . Pramod. Srivastava. 2. ,. . and Ion Mandoiu. 1. 1. University of Connecticut. Department of Computer Sciences & Engineering. 2. University of Connecticut Health Center. The correspondence problem. A general pipeline for correspondence. If sparse correspondences are enough, . choose points for which we will search for correspondences (feature points). For each point (or every pixel if dense correspondence), describe point using a . Ming-Ming Cheng. 1. Ziming Zhang. 2. Wen-Yan Li. 1. Philip H. S. Torr. 1. 1. Torr . Vision Group, Oxford . University . 2. Boston . University. 1. Motivation: Generic . object detection. INTRODUCTION. The formation and maintenance of linear dominance hierarchies is characterized by a gradual polarization (increased steepness) of dominance ranks over time. Agonistic interactions are usually correlated to daily activity rhythms and both are controlled by light-entrained endogenous pacemakers (i.e., circadian clocks). Circadian clocks can be .