An Introduction to Computer Vision George J. Grevera, Ph.D.

 The science of analyzing images and videos in order to recognize or just model 3D objects, persons, and environments. Computer Vision

 How does the Sony AIBO dog find its way “home” (to its charging stations)? Computer Vision

 How does the yellow, virtual first-down line work? Computer Vision

 How do cameras perform (digital) image stabilization?  In this class, we study the underlying principles and produce working examples. Computer Vision

Visualization includes…  Computer graphics  Computer / Machine vision  Image understanding  Database and communications  Computer games  Medical imaging  Image processing  Pattern recognition

Computer graphics/games vs. computer vision  Computer graphics/games creates a 2D image from a 3D world/model.  3D to 2D

Computer graphics/games vs. computer vision  Computer vision estimates a 3D world/model from a 2D image.  2D to 3D

Examples gray (b&w) and color

Ansel Adams: El Capitan

Bill Brandt: Lambeth Walk

Lewis Hine

George Grevera: Horse Fishing

#1 Major problems in Computer Vision: Segmentation

Segmentation  Recognition: Is a t-shirt present?  Delineation: Can you accurately outline the t-shirt (what size is it)?

Segmentation tasks: 1. Recognition  Human is typically better.  more qualitative 2. Delineation  Computer is typically better.  more quantitative

Model building

Models from CT (Computed Tomography) head data

3D visualization of CT head data

MRI Diffusion Tensor Imaging

#2 Major problem in Computer Vision: Registration

Registration  A.K.A.:  alignment  warping  mosaicing  morphing  fusion

Simple MRI Example (rigid)

Deformable

 Thirion’s “Demons” algorithm applied to pre- and post-contrast MRI of the breast.  excellent results Deformable registration example pre post (no reg) post (after Thirion’s Demons registration) diff diff (after reg)

 Thirion’s “Demons” algorithm applied to PET chest emission and transmission images.  poor results PET transmission image PET emission image PET emission warped to match transmission Deformable registration example

NON-MEDICAL VISUALIZATION

Red eye reduction

What is a distance transform?  Input:a binary image  Output:a grey image  for all points...  assign the minimum distance from that particular point to the nearest point on the border of an object

Applications of distance transforms:  skeletonization/medial axis transform  interpolation  registration  efficient ray tracing  classification of plant cells  measuring cell walls  characterize spinal cord atrophy

Experimental Results binary input image distance transform result

Application areas: Object recognition Tracking Registration Fusion Intelligence, industrial and medical projects FBI Automatic Fingerprint Identification System FOCUS: Monitor change in satellite images FBI Facial Reconstruction Software: Target Junior Image Understanding

Textbook  L.G. Shapiro, G.C. Stockman, Computer Vision, Prentice-Hall, 2001.

Topics  Imaging and image representation  Sensors  Problems (including noise)  Image file formats  Color representation and shading  Binary image analysis  Connected components  Morphology  Region properties

Topics  Pattern recognition concepts  Classifiers and classification  Filtering (enhancing) images  Segmentation  Registration (matching)

Topics  Registration  Texture representation and segmentation  Motion from sequences of 2D images

Homework #1  Read chapter 1.  Hand in 1.1, 1.2, and 1.3.

Survey questions… 1. Do you have access to a digital camera?

2. Write a function that, given a 2D array, returns a 1D array where each entry is the sum of the corresponding row in the 2D array. (So result[0] contains the sum of values in m for row 0, result[1] contains the sum of values in m for row 1, etc.) Java: int[] sumOfRows ( int m[][], int rows, int cols ) { …}