PPT-Large-Scale Copy Detection

Xin Luna Dong Divesh Srivastava 1 Outline Motivation Why does copy detection matter Examples of copying not copying Copy detection In documents In software In databases Summary 2. Feature detection with . s. cale selection. We want to extract features with characteristic scale that is . covariant. with the image transformation. Blob detection: Basic idea. To detect blobs, convolve the image with a “blob filter” at multiple scales and look for . By. Chi . Bemieh. . Fule. August 6, 2013. THESIS PRESENTATION . Outline. . of. . today’s. presentation. Justification of the study. Problem . statement. Hypotheses. Conceptual. . framework. Research . Large-scale Single-pass k-Means . Clustering. Large-scale . k. -Means Clustering. Goals. Cluster very large data sets. Facilitate large nearest neighbor search. Allow very large number of clusters. Achieve good quality. Achieving scale covariance. Goal: independently detect corresponding regions in scaled versions of the same image. Need . scale selection. mechanism for finding characteristic region size that is . covariant. Sébastien Biner and Daniel Caya with the contribution of the climate simulation team at Ouranos. The Canadian RCM (CRCM) history. Developped at the University of Québec in Montréal during the 1991-2001 period. The Nature of Business. Year Eleven – Semester one - Unit one. Roles of Business. Production. Employment . Careers. Enterprise. Investment. Production. Production is the process of manufacturing a product or providing a service. Abstract. Cloud computing economically enables customers with limited computational resources to outsource large-scale computations to the cloud. . However, how to protect customers’ confidential data involved in the computations then becomes a major security concern. In this paper, we present a secure outsourcing mechanism for solving large-scale systems of linear equations (LE) in cloud.. Pre-SUSY Summer School. Melbourne, June 29-July 1, 2016 . An . Introduction. to. Particle. Dark . Matter. Santa Cruz Institute for Particle Physics. University of California, Santa Cruz. Quick . summary. LINKEDIN FOR LAWYERS: 2015 Edition. Matt Godson: . Matt is the Co-CEO of Momentum Events & Momentum Names with a particular focus on legal event marketing, attendee acquisition and legal naming strategies. 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 Extreme Events. . Robert Black, Yi Deng, . Katherine Evans*. Yun-Young Lee, Tae-Won Park, . Rebecca . Westby . School . of Earth and Atmospheric Sciences. Georgia Institute of Technology, Atlanta, . Computer Vision, FCUP, . 2018/19. Miguel Coimbra. Slides by Prof. Kristen . Grauman. Today. Local . invariant . features. Detection of interest points. (Harris corner detection). Scale invariant blob detection: . : From the . Very Large . to the . Very Small. Saurabh Bagchi. School of Electrical and Computer Engineering. Department of Computer Science . Purdue University. Presentation available at: . engineering.purdue.edu/dcsl. Institute of High Energy Physics, CAS. Wang Lu (Lu.Wang@ihep.ac.cn). Agenda. Introduction. Challenges and requirements of anomaly detection in large scale storage systems . Definition and category of anomaly.