PPT-Adaptive Averaging of Non-Identical Image Series

in the Wavelet Space 3721 marschnercbsmpgde Henrik Marschner André Pampel amp Harald E Möller Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany A. The Fourier Transform. Development of Fourier Analysis. In 1748 Leonhard Euler used linear combinations of “normal modes” to describe the motion of a vibrating string. If the configuration at some point in time is a linear combination of normal modes, so is the configuration at any subsequent time. They replace the value of an image pixel with a combination of its neighbors. Basic operations in images. Shift Invariant. Linear. Thanks to David Jacobs for the use of some slides. Consider 1D images. Lecture 8: Wavelets and Data Compression. 2. Topics. Fourier Series. Wavelets. FBI Fingerprint Compression. A Wavelet-Based Data Compression Scheme. An Image Compression Example. Averaging and Differencing. r. s. t. u. v. w. x. y. z. Origin. x. y. Image f (x, y). e. processed. = . v. *e + . . r. *a + . s. *b + . t. *c + . . u. *d + . w. *f + . . x. *g + . y. *h + . z. *. i. Filter. Simple 3*3. Neighbourhood. Interatomic Forces. Micro - Macro. The Atomic “hypothesis” . (p. 139). All matter consists of atoms, whose typical radius is about 1×10. -10. m. Atoms attract each other when they are close to each other but not too close.. Interatomic Forces. Micro - Macro. The Atomic “hypothesis” . (p. 139). All matter consists of atoms, whose typical radius is about 1×10. -10. m. Atoms attract each other when they are close to each other but not too close.. . Fall 2015. Presented by ESC 13. TxEIS. Student Consultants, . Denise McCoy, Heather Klotz, . & . Michael . Naber. 1. District’s GPA Policy. The district’s GPA policy should be outlined in the Campus Student Handbook or a similar publication. . Vinay Raj Hampapur. Wendy Ni. Stanford University. March 8, 2011. Outline. Motivation. Description of our method. Results and comparisons. Achievements. Future work. Acknowledgement. References. 2. EE398A: Direction-Adaptive KLT for Image Compression. They replace the value of an image pixel with a combination of its neighbors. Basic operations in images. Shift Invariant. Linear. Thanks to David Jacobs for the use of some slides. Consider 1D images. . Fall 2015. Presented by ESC 13. TxEIS. Student Consultants, . Denise McCoy, Heather Klotz, . & . Michael . Naber. 1. District’s GPA Policy. The district’s GPA policy should be outlined in the Campus Student Handbook or a similar publication. . They replace the value of an image pixel with a combination of its neighbors. Basic operations in images. Shift Invariant. Linear. Thanks to David Jacobs for the use of some slides. Consider 1D images. The relevant features for the examination task are enhanced. The irrelevant features for the examination task are removed/reduced. Here the input and output image are both digital image in color or gray scale.. Saliency and KAZE Features Authors: Siddharth Srivastava, Prerana Mukherjee, Dr. Brejesh Lall SPCOM 2016 Department of Electrical Engineering Indian Institute of Technology, Delhi Overview • Intro adjusts its coefficients . to adapt input signal . via an adaptive algorithm. .. Applications:. Signal enhancement. Active noise control. Noise cancellation. Telephone echo cancellation. 1. Text: Digital Signal Processing by Li Tan, Chapter 10.