1 / 9

Chapter 24: Perception

Chapter 24: Perception. April 20, 2004. 24.1 Introduction. Emphasis on vision Feature extraction approach Model-based approach S stimulus W world f, defined by physics and optics S = f(W) computer graphics W = f -1 (S) computer vision. 24.2 Image Formation.

aarmendariz
Download Presentation

Chapter 24: Perception

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. Chapter 24: Perception April 20, 2004

  2. 24.1 Introduction • Emphasis on vision • Feature extraction approach • Model-based approach • S stimulus • W world • f, defined by physics and optics • S = f(W) computer graphics • W = f-1(S) computer vision

  3. 24.2 Image Formation • Pinhole Camera (image without a lense) • Figure 24.1 • point P in scene (X, Y, Z) • point P’ in image plane (x, y, z) • f: distance from pinhole to image plane • -x / f = X / Z (similar triangles) • - y / f = Y / Z (similar triangles)

  4. Lens Systems • A lens enables more light to enter • The pinhole camera equations are still accurate • Figure 24.2

  5. Photometry: Study of Light • Figure 24.3 • Specular reflection: light is reflected from the outer surface of the object • Diffuse reflection: light is absorbed by the object and then re-emitted

  6. Color • The retina has three types of cones with receptivity peaks at 650 nm, 530 nm and 430 nm • Colors can be reproduced by using linear combinations of red (700 nm), green (546 nm) and blue (436 nm)

  7. 24.3 Early Image Processing Operations • Local operations • Lack of Knowledge • Smoothing: predicting the value of a pixel, given the surrounding pixels • A weighted average can be calculated using a Gaussian filter (cancels Gaussian noise)

  8. Edge Detection • Edges occur where there is a significant change in image brightness • Figure 24.4, kinds of edges • Figure 24.5, edges from a photograph • Figure 24.6, calculating edges • Canny edge detection

  9. Image Segmentation • Can look at low level knowledge such as brightness, color, or texture • Can also factor in high-level knowledge.

More Related