Accelerating pattern matching or how much can you slide?

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4844 LNCS, Issue PART 2, 2007, Pages 435-446

  Pele, Ofir ^a   Werman, Michael ^a  

^a HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER VISION; IMAGE ANALYSIS; PIXELS;

IMAGE SMOOTHNESS;

PATTERN MATCHING;

EID: 38149077444     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-76390-1_43     Document Type: Conference Paper

References (6)

1. Pele, O., Werman, M.: Robust real time pattern matching using bayesian sequential hypothesis testing. Technical Report 973, Department of Computer Science, The Hebrew University of Jerusalem (2007), http ://www.cs.huj i.ac.il/~ofirpele/hs/TR.html
2. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Analysis and Machine Intelligence 27(10), 1615-1630 (2005)
3. Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA (2001)
4. Zabih, R., Woodfill, J.: Non-parametric local transforms for computing visual correspondence. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 800, pp. 151-158. Springer, Heidelberg (1994)
5. Lepetit, V., Fua, P.: Keypoint recognition using randomized trees. IEEE Trans. Pattern Analysis and Machine Intelligence 28(9), 1465-1479 (2006)
6. Gil, J., Werman, M.: Computing 2-d min, median, and max filters. IEEE Trans. Pattern Analysis and Machine Intelligence 15(5), 504-507 (1993)