PPT-Object Detection with Superquadrics

Presenter Ilya Afanasyev Facoltà di Ingegneria Trento 2010 14122010 1 20 Superquadrics SQ A superquadric surface can be defined by the 3D vector are parameters of shape squareness. 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. Binarized Normed Gradients for Objectness Estimation at 300fps. Ming-Ming Cheng. 1. Ziming Zhang. 2. Wen-Yan Li. 1. Philip H. S. Torr. 1. 1. Torr . Vision Group, Oxford . University . Mariolino. De . Cecco. , . Nicolo. . Biasi. , . Ilya. Afanasyev. . . . Facoltà di Ingegneria. Trento, . 2012. 21/11/2011. 1. /20. Content. 2. /20. Kinect installation.. Kinect calibration.. Object Persistence Object Oriented Programming Object Serialization Object Oriented Programming 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]. Compositional bias of salient object detection benchmarking. Xiaodi. . Hou. K-Lab, Computation and Neural Systems. California Institute of Technology. for the Crash Course on Visual Saliency Modeling:. P. . Felzenszwalb. Object detection with deformable part-based models. Challenge: Generic object detection. Histograms of oriented gradients (HOG). Partition image into blocks and compute histogram of gradient orientations in each block. Erik E. Stone and Marjorie . Skubic. IEEE JOURNAL of biomedical and health informatics, VOL. 19, NO. 1, . january. 2015. 1. Outline. Introduction. System Overview. Fall Detection Methodology. Evaluation. Ross Girshick. Microsoft Research. Guest lecture for UW CSE 455. Nov. 24, 2014. Outline. Object detection. the task, evaluation, datasets. Convolutional Neural Networks (CNNs). overview and history. Region-based Convolutional Networks (R-CNNs). Bangpeng Yao and Li Fei-Fei. Computer Science Department, Stanford University. {bangpeng,feifeili}@cs.stanford.edu. 1. Robots interact with objects. Automatic sports commentary. “Kobe is dunking the ball.”. AdaScale: Towards Real-time Video Object Detection using Adaptive Scaling Ting-Wu (Rudy) Chin* Ruizhuo Ding* Diana Marculescu ECE Dept., Carnegie Mellon University SysML 2019 Autonomous Cars “Anomaly Detection: A Tutorial”. Arindam. . Banerjee. , . Varun. . Chandola. , . Vipin. Kumar, Jaideep . Srivastava. , . University of Minnesota. Aleksandar. . Lazarevic. , . United Technology Research Center. Limit of Detection (LOD). The detection limit is the concentration that is obtained when the measured signal differs significantly from the background.. Calculated by this equation for the ARCOS.. C. Yonggang Cui. 1. , Zoe N. Gastelum. 2. , Ray Ren. 1. , Michael R. Smith. 2. , . Yuewei. Lin. 1. , Maikael A. Thomas. 2. , . Shinjae. Yoo. 1. , Warren Stern. 1. 1 . Brookhaven National Laboratory, Upton, USA.