tailieunhanh - Lecture Digital image processing - Lecture 22: Image registration

This chapter presents the following content: Image registration, different mismatch or measures, cross correlation between tow images, applications of image registration, estimation of degradation model, restoration techniques. | Digital Image Processing CSC331 Image registration 1 Summery of previous lecture Estimation of Degradation Model By observation By experimentation Mathematical model Restoration techniques Inverse filtering Minimum Mean Square error (Wiener) Constrained Least square Filter Restoration in presence of periodic noise 2 Todays lecture Image registration Different mismatch or measures Cross correlation between tow images Applications of image registration 3 4 Image registration 5 image registration 6 Registration techniques Template matching 7 Different match or similarity measures 8 9 Cauchy Schwarz inequality 10 11 Normalized cross correlation 12 13 Working example 14 Cross correlation The similarity measure 15 cross correlation measure directly cannot be used as a similarity measure as a false match 16 normalized cross correlation value 17 All normalized cross correlation value 18 19 20 21 Application 22 Placing the template 23 24 25 26 27 28 29 30 Registration as a image restoration 31 32 Registration as a image restoration 33 Applications 34 35 Image mosaicing 36 Image mosaicing 37 Summery of the lecture Image registration Different mismatch or measures Cross correlation between tow images Applications of image registration 38 References Prof .P. K. Biswas Department of Electronics and Electrical Communication Engineering Indian Institute of Technology, Kharagpur Gonzalez R. C. & Woods . (2008). Digital Image Processing. Prentice Hall. Forsyth, D. A. & Ponce, J. (2011).Computer Vision: A Modern Approach. Pearson Education. . | Digital Image Processing CSC331 Image registration 1 Summery of previous lecture Estimation of Degradation Model By observation By experimentation Mathematical model Restoration techniques Inverse filtering Minimum Mean Square error (Wiener) Constrained Least square Filter Restoration in presence of periodic noise 2 Todays lecture Image registration Different mismatch or measures Cross correlation between tow images Applications of image registration 3 4 Image registration 5 image registration 6 Registration techniques Template matching 7 Different match or similarity measures 8 9 Cauchy Schwarz inequality 10 11 Normalized cross correlation 12 13 Working example 14 Cross correlation The similarity measure 15 cross correlation measure directly cannot be used as a similarity measure as a false match 16 normalized cross correlation value 17 All normalized cross correlation value 18 19 20 21 Application 22 Placing the template 23 24 25 26 27 28 29 30 Registration as a image restoration 31 32 Registration as a image restoration 33 Applications 34 35 Image mosaicing 36 Image mosaicing 37 Summery of the lecture Image registration Different mismatch or measures Cross correlation between tow images Applications of image registration 38 References Prof .P. K. Biswas Department of Electronics and Electrical Communication Engineering Indian Institute of Technology, Kharagpur Gonzalez R. C. & Woods . (2008). Digital Image Processing. Prentice Hall. Forsyth, D. A. & Ponce, J. (2011).Computer Vision: A Modern Approach. Pearson Education. 39