'Output image' diaporamas de présentation

Chapter 3: Filtering & Enhancing Images

Chapter 3: Filtering & Enhancing Images. This chapter is about image processing , since the methods take an input image and create another image as output. Other appropriate terms often used are filtering, enhancement , or conditioning .

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Introduction

Introduction. CSc I6716 Spring 2012. Part I Feature Extraction (1). Image Enhancement. Zhigang Zhu, City College of New York zhu@cs.ccny.cuny.edu. What are Image Features?. Local, meaningful, detectable parts of the image. More Color Woes. Squares with dots in them are the same color.

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Introduction

Introduction. CSc I6716 Fall 2009. Part I Feature Extraction (1). Image Enhancement. Zhigang Zhu, City College of New York zhu@cs.ccny.cuny.edu. What are Image Features?. Local, meaningful, detectable parts of the image. More Color Woes. Squares with dots in them are the same color.

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CIS Testing Technology

CIS Testing Technology. ???? Jemmy Mobil Phone:0917866007 E-mail:jemmy_chen@jetek.com.tw. Basic Introduction CIS Function CIS Test Item CIS Test System Production Issue. Contents. -What is CIS -CIS Application -CIS Market. Basic Introduction. Digital camera. Digital camera. CCD.

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Lecture 18 Write Your Own ITK Filters, Part2

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Introduction to Computer Vision

Introduction to Computer Vision. Lecture 4 Dr. Roger S. Gaborski. Quiz. Intensity image is simply a matrix of numbers. We can summary this information by only retaining the distribution if gray level values:. Partial image info:. 117 83 59 59 68 77 84

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Introduction to Computer Vision

Introduction to Computer Vision. Lecture 4 Dr. Roger S. Gaborski. Quiz. Intensity image is simply a matrix of numbers. We can summary this information by only retaining the distribution if gray level values:. PARTIAL IMAGE INFO:. 117 83 59 59 68 77 84

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ELE 488 Fall 2006 Image Processing and Transmission (10-12-06)

10/12/06. ELE 488 Fall 2006 Image Processing and Transmission (10-12-06). Re-sampling and Re-sizing 1D ? 2D, sinc interpolation nearest neighbor, bilinear, bicubic, . . . Geometric Transformation translation rotation scaling Affine.

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Image Processing

Image Processing. Ch4: Filtering in frequency domain Prepared by: Tahani Khatib AOU. Part 2 . Filtering in frequency domain. Frequency Domain Filtering & Spatial Domain Filtering. Similar jobs can be done in the spatial and frequency domains

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Hongtao Du

Hongtao Du. AICIP Research Mar 9, 2006. Motivation. Image processing Complicated algorithms Large data sets Pipelined and parallel computing Speedup? Not Necessary! Solution: Efficiently Allocate process Distribute data. Outline. Pipelined and parallel computing overview

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Accurate Projection of Small-Scale Raster Datasets

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Block Truncation Coding Using Pattern Fitting

Block Truncation Coding Using Pattern Fitting. Source: Pattern Recognition, vol.37, 2004, pp.2131-2139 Authors: Bibhas Chandra Dhara, Bhabatosh Chanda Speaker: Shu-Fen Chiou( ??? ) Date: 2004/09/08. Outline. Introduction BTC (Block Truncation Coding) Purposed method

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Weather Mining

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Homework 7

Homework 7. Face Detection. Advisor ???? Presenter ????. Outline. Face detection Skin color Symmetry point Lips color. Flow Chart. Symmetry point. Skin color. Face detection. Input image. Output image. Lips color. Symmetry Point. 32 pixel. Y value. 8 pixel. Y direct sum.

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8. Geometric Operations

8. Geometric Operations. Geometric operations change image geometry by moving pixels around in a carefully constrained way. We might do this to remove distortions inherent in the imaging process, or to introduce a deliberate distortion that matches one image with another.

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Image inpainting final project

Image inpainting final project. Speaker: Hui -Chun Su Advisor: Guo-Shiang Lin. Outline. Introduction Method discussion. Introduction. Image inpainting The gold (goal ) of our work is inpainting the damaged region. Image : I. Inpainting Domain : D. Outline. Introduction

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Automated Rule of Thirds Compliancy

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Image Rotation

Image Rotation. Daniel E. Michek. Image Rotation Details Raster Scan Output. Choice to rotate input image or output image Hotelling transform w/ basis vectors cos(t) and sin(t) Rotate input image (raster scan order data output) Sx = Dx*cos(t) + Dy*sin(t), Sy = Dy*cos(t) - Dx*sin(t)

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