Image Processing Example

Aug 18, 2024

660 likes | 667 Views

Explore the sensitivity of the human eye to intensity changes, required gray levels in digital images, and tradeoffs between resolution aspects in this digital image fundamentals experiment.

Share Presentation

Embed Code

tdeck

Download Presentation

Image Processing Example

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

Image Processing Example
Chapter 2: Digital Image Fundamentals
Chapter 2: Digital Image Fundamentals
Chapter 2: Digital Image Fundamentals
Chapter 2: Digital Image Fundamentals
Chapter 2: Digital Image Fundamentals
Brightness discrimination experiment
Chapter 2: Digital Image Fundamentals
Chapter 2: Digital Image Fundamentals
Chapter 2: Digital Image Fundamentals
Chapter 2: Digital Image Fundamentals
Chapter 2: Digital Image Fundamentals
Chapter 2: Digital Image Fundamentals
Chapter 2: Digital Image Fundamentals
Chapter 2: Digital Image Fundamentals
Chapter 2: Digital Image Fundamentals
Chapter 2: Digital Image Fundamentals
Chapter 2: Digital Image Fundamentals
Chapter 2: Digital Image Fundamentals
Chapter 2: Digital Image Fundamentals
Chapter 2: Digital Image Fundamentals
Chapter 2: Digital Image Fundamentals
Chapter 2: Digital Image Fundamentals
Chapter 2: Digital Image Fundamentals
Sampling – Spatial Resolution
Varying # of bits per pixel
Varying # of bits per pixel
How many gray levels are required?
Storage requirements for digital images
Sensitivity of the Eye to intensity changes
Chapter 2: Digital Image Fundamentals
Tradeoff between gray-level resolution and spatial resolution
Interpolation

Aliasing effect for 1-dimensional signal
Aliasing effect (Moiré) for an image
Chapter 2: Digital Image Fundamentals

Sampling example
Anti-alias Filter

Image processing

Image processing. Adaptive filters: Wiener and Lucy Richardson filters. Image model. Original data = Image * PSF of blurring +additive noise * - convolution operator The Importance of the PSF

731 views • 17 slides

Image Processing

Image Processing. Rule 1: Always save your primary data Rule 2: Be able to describe quantitatively what you have done. 3 types of operations. Point operations: operate on a pixel-by-pixel basis Neighborhood operations: operate on a small group of adjacent pizels

443 views • 22 slides

Image processing

Image processing. Fourier Transform. Discrete Fourier Transform. Generate signals. t = 0:0.001:0.6; x = sin(2*pi*50*t)+sin(2*pi*120*t); figure, plot(x(1:50)) title('original Signal’) xlabel('time (seconds)') Add a random noise y = x + 2*randn(size(t)); plot(y(1:50))

432 views • 10 slides

Image Processing

Image Processing. المعالجة الصورية. Syllabus .

475 views • 25 slides

Image Processing

Image Processing. Chapter 14. Landsat 7 image of the retreating Malaspina Glacier, Alaska. Image Basics. Earth science is a very visual discipline Graphs Maps Field Photos Satellite images Because of this, all Earth scientists should have: Basic knowledge about graphics file types

475 views • 22 slides

Image Processing

Image Processing. Overview. Images. Pixel Filters. Neighborhood Filters. Dithering. Image as a Function. We can think of an image as a function, f , f: R 2  R f ( x, y ) gives the intensity at position ( x, y )

711 views • 56 slides

Image Processing

Image Processing. Goal Start simple: look at small windows Identify useful image structures (‘Clues’ useful for recognizing objects) Eliminate irrelevant aspects of image appearance (neglect appearance variations that don’t help to explain object identity)

1.94k views • 156 slides

Image Processing

Image Processing. Image Processing. a typical image is simply a 2D array of color or gray values i.e., a texture image processing takes as input an image and outputs characteristics about the image a new image modifies the input image

607 views • 30 slides

Image processing

Image processing. Reading. Jain, Kasturi, Schunck, Machine Vision . McGraw-Hill, 1995. Sections 4.2-4.4, 4.5(intro), 4.5.5, 4.5.6, 5.1-5.4. Image processing. An image processing operation typically defines a new image g in terms of an existing image f.

657 views • 34 slides

Image Processing

Image Processing. Introduction and Overview. Instructor: Juyong Zhang juyong@ustc.edu.cn http://staff.ustc.edu.cn/~juyong. . 1. %. {. . {. Introduction and Overview. This presentation is an overview of some of the ideas and techniques to be covered during the course. Topics.

663 views • 50 slides

Image Processing

Image Processing. Image Restoration Part 1. What is Image Restoration?. Image restoration attempts to restore images that have been degraded by using a prior knowledge of the degradation phenomenon. Identify the degradation process and attempt to reverse it

695 views • 20 slides

Image Processing

Image Processing. Instructor : Prof. Sei-Wang Chen, PhD Office : Applied Science Building, Room 101 Contaction: Tel : 77346661 E-mail : schen@csie.ntnu.edu.tw Class Hr. : Thursday 2:10 - 5:00pm

329 views • 14 slides

Image Processing

Image Processing. Image processing. Once we have an image in a digital form we can process it in the computer . We apply a “filter” and recalculate the pixel values of the processed image. We will look at the application of filters to sharpen and soften (make less sharp) the image.

521 views • 23 slides

Image Processing

Image Processing. What is Image Processing ?. Image Processing is any form of signal processing for which our input is an image, such as photographs or frames of video and our output can be either an image or a set of characteristics or parameters related to the image.

1.66k views • 83 slides

Image Processing

Image Processing. Empirical Evaluation of Dissimilarity Measures for Color and Texture. Jan Puzicha , Joachim M. Buhmann , Yossi Rubner & Carlo Tomasi. David Kauchak cs160 Fall 2009. Administrative. 11/4 class project discussion project proposal draft due

633 views • 51 slides

image processing

In training we will cover the image processing tool kit for transforming picture & features too in MATLAB. Diverse picture directions and pixel count as per their arrangements would be uncovered alongside the Simulink & GUI advancement utilizing MATLAB and different operations, with cases of certifiable applications.

354 views • 10 slides

Image Processing

Image Processing. What is an image?. We can think of an image as a function, f , from R 2 to R: f ( x, y ) gives the intensity at position ( x, y ) Realistically, we expect the image only to be defined over a rectangle, with a finite range: f : [ a , b ] x [ c , d ]  [0,1]

1.24k views • 104 slides

Image Processing

Image Processing. Today’s readings Forsyth & Ponce , chapters 8.1-8.2 http://www.cs.washington.edu/education/courses/490cv/02wi/readings/book-7-revised-a-indx.pdf For Monday Watt, 10.3-10.4 (handout) Cipolla and Gee (handout) supplemental: Forsyth , chapter 9. Images by Pawan Sinha.

389 views • 35 slides

Image Processing

Image Processing. Image Processing – Where does it fit?. Image Processing Product Lines. Traditional Image Processing Presentation Switchers: (Encore, ScreenPro-II, DCS 100/200) Family of products spanning small, medium, and large venues

529 views • 43 slides

Image Processing

Image Processing. By- Ms. Ashwini Deshmukh. Today. Image Enhancement Techniques Spatial Domain Method Histogram Methods Frequency Domain Methods. Spatial Domain Methods.

809 views • 38 slides

Image Processing

Image Processing. Chapter(3) Part 4:Intensity Transformation and spatial filters Prepared by: Hanan Hardan. Ch3, lesson 6: spatial filters. Origin. x. (x, y). Neighbourhood. y. Image f (x, y). Spatial filters. Remember that types of neighborhood:

274 views • 21 slides

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

690 views • 38 slides

More Related