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Introduction to Computer Vision

by: Abel Lueilwitz

Introduction to Computer Vision CSE 185

Abel Lueilwitz
GPA 3.71

Shawn Newsam

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Shawn Newsam
Class Notes
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This 2 page Class Notes was uploaded by Abel Lueilwitz on Thursday October 29, 2015. The Class Notes belongs to CSE 185 at University of California - Merced taught by Shawn Newsam in Fall. Since its upload, it has received 42 views. For similar materials see /class/231722/cse-185-university-of-california-merced in Computer Science and Engineering at University of California - Merced.

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Date Created: 10/29/15
CSE185 Fall 2008 Lecture 15 I a Duzlallmq Processing M m T oday Image transformations and Spam ltering Chap 3 Asslgnments HW 3 due Wed 1029 by midnight Midterm Wed 115 in class Chap 3 Image transformations and Spam lten39ng We will rst deal with case when neighborhood is n a single pixel intensity transformatJo d g 7 Log transformations 7 Histngam processing 53523 r Chap 3 Image transformations and Spam lten39ng Intensitytxansformation Drop xy 3 79 Examples Can utilize different families offunetions for intensity transformations chap 3 Image transformations and spatial filtering Image negative 5 Leler Useful for enhaneing forvi ualization white or gray detail embedded in dark regions of an image espeeially when the black areas are dominant in size chap 3 Image transformations and spatial filtering Log transformations 5 cloglt Maps a nanow range oflow intensity values in the input to a wider range of output levels Expands the values of dark pixels in an image while eompressing the higher level values Useful when range ofpixel values is large example 106 but most values are small 7 Sueh an image would lie mnstly darkiwnuldn39t lie able to see detail in dark areas 1 mused quot chap 3 Image transformations and spatial filtering Piecewise linear transformation funetions Advantages 7 Unlike og inverselog ete canbe arbitrarily nmplex 39i 39t l t u tint Disadvanta e Minimum 7 Require more user input for parameter seleetinn chap 3 Image transformations and spatial filtering 44 chap 3 Image transformations and spatial filtering Piecewise linear transformation funetions Contrast stretching Let ari 39nml and erzhwll where W and tmdenote the rninirnurnan maximum intensity In the image Stretches the levels linearly from their original range to the full range Mel Contrast stretching example rmm84 and 5152


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