PPT-Pattern matching with Correlation and Time Warping

Cross Correlation A simple yet powerful technique for detecting known patterns Example applications include Detecting preamble sequence in WiFi packets Detecting cell tower ID based on known sequences. Yacov. Hel-Or. The Interdisciplinary Center (IDC), Israel . Visiting Scholar - Google . Hagit. Hel-Or and Eyal David. U. of Haifa, Israel . A given pattern . p. is sought in an image. . The pattern may appear at any location in the image.. E. ngineering for . E. nhanced . P. erformance of . N. etwork . E. lements and . S. ecurity . S. ystems. 1. PIs: Dr. . Anat. . Bremler. -Barr (IDC). Dr. David Hay (HUJI). www.deepness-lab.org. Presented by Victor . Zigdon. 1*. Joint work with: Dr. . Anat. . Bremler. -Barr. 1* . and . Yaron. Koral. 2. The SPC Algorith. m. 1 Computer Science Dept. Interdisciplinary Center, . Herzliya. , Israel. Dongliang. . Zhang, . Xin. Wang, . Yunsong. Huang, Gerard Schuster. KAUST. Outline. . Conclusions. Motivation. Flatten the common image gathers. Numerical Example. Test . on . Marmousi. model and GOM data. Yacov Hel-Or. I.D.C.. Visiting Scholar – Google. joint work with. Hagit Hel-Or and Eyal David. U. of Haifa, Israel . A given pattern . p. is sought in an image. . The pattern may appear at any location in the image.. 2-D Warping and Block Matching. Shinjini. . Kundu. Anand. . Kamat. . Tarcar. EE398A Final Project. 1. EE398A - Compression of Light Fields using 2-D Warping and Block Matching. Outline. Motivation and . Acceleration Data . Pramod. . Vemulapalli. . Outline . 50 % Tutorial and 50 % Research Results . Basics . Literature Survey . Acceleration Data . Preliminary Results . Conclusions . What is A Time-Series Subsequence ?. Yuriy Solodkyy . • . Gabriel Dos Reis . • Bjarne Stroustrup. Texas A&M University. Microsoft. October 27, 2013. GPCE’13, Indianápolis, IN. http://parasol.tamu.edu/mach7/. Partially supported by NSF grants:. Tel Aviv University. March 2016. Last updated: March 28, 2017. Algorithms . in Action. Fast Fourier Transform. 2. String Matching. abracadabra. abraabracadabracadabraabara. abracadabra. Given a . text. M anuscript received Feb. 2, 1996; revised Oct. 21, 1996. Recommended for accep-tance by B. Dom.For information on obtaining reprints of this article, please send e-mail to:transpami@computer.org, and Michael T. Goodrich. University of California, Irvine. The Multi-Pattern Matching Problem. Given a text, T, of length n and a set of k patterns, P. 1. , …, P. k. , each of length at most m, find the first (or each) occurrence of a given pattern in T.. Shashank. . Kadaveru. Introduction. In Motif Finding problem, no particular pattern is given to search for. We infer it from the sample.. In Combinational Pattern Matching, we look for exact or appropriate occurrences of given patterns in a long text. . Ángel. Bautista. 1,2. , Laura Igual. 1,2. ,. Josep. Moya. 3. , . Verónica. Violant. 2. , . Oriol. Pujol. 1,2. and Sergio Escalera. 1,2. Computer Vision Center (CVC). 1. , University of Barcelona (UB). Edward Fernandez, Walid Najjar, Stefano . Lonardi. , Jason Villarreal. UC Riverside, Department of Computer Science and Engineering. Jacquard Computing. Introduction. Multithreaded architectures masks long memory latencies by context switching threads..