1 / 15

Understanding Digital Images in Computer Vision

Introduction to digital images in computer vision. Explore grayscale and color image sensing systems, resolution, quantization levels, image enhancement, and pixel representation. Learn about sampling, quantization, and the properties of digital images.

zorina
Télécharger la présentation

Understanding Digital Images in Computer Vision

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

  1. 0116136Computer Vision IntroductiontoDigitalImages

  2. Digital Images Digital Image: • in general, image is a function of four variables • For color image, λ takes three different values corresponding to red, green and blue components, • For constant λ (black and white), the image function becomes where t is a time variable for a sequence of frames. • For a constant t, f becomes which is a function of two spatial variables.

  3. Grayscale Image Sensing Systems:

  4. ColorImageImageSensingSystems:

  5. CCD cameras are much more sensitive than the eye

  6. Sampling (Resolution)

  7. Grayscale Quantization Level:

  8. Color Image Quantization Level

  9. Image Enhancement

  10. Digital Image • These values are called “gray levels ”. They are real, non-negative. • Image is of finite size : They are zero outside a finite region, since an optical system has a bounded field of view. • Whenever necessary, we will assume that image functions are analytically well -behaved, e.g. integrable, invertible FT. • After sampling, we have a discrete set of real numbers. (m,n) • After quantization, the resulting quantized gray levels can be regarded as integers f(m,n) • Thus after sampling and quantization, we can assume that a digital image is a rectangular array rectangular array of integer values. • Pixel : An element of a digital image is called a “picture element”. • Binary Image : If there are just two values, e.g. black and white, we usually represent them by 0 and 1.

  11. Except on borders of the array, any point (m,n) has 8 neighbor pixels • Note that diagonal neighbors units away from (m,n) while horizontal and vertical neighbors are only 1 unit away.

More Related