cis 601 fall 2003 introduction to computer vision longin jan latecki

CIS 601 Fall 2003

Introduction toComputer Vision

Longin Jan Latecki

Based on the lectures of Rolf Lakaemper and David Young

Computer Vision ?

Computer Vision ?

“Computer vision’s great trick is extracting descriptions of the world

from pictures or sequences of pictures”(Forsyth/Ponce: Computer Vision)

Pictures/Movies:

How to

• Represent• Process / Prepare• Handle• Recognize Objects

Representation

• Digital Images• Color Spaces• Gray Images• Binary Images• Geometrical Properties

Representation

• Digital Images• Color Spaces• Gray Images• Binary Images• Geometrical Properties

How to process / prepare:

• Filters• Edges• Geometric Primitives• Lines, Circles

Introduction to Image Analysis and Processing

Low Level Object Handling:

• Image / Video Compression• Huffman • JPEG• MPEG• …

JPEG - Joint Photographic Experts Group

JPEG is designed with photographs in mind. It is capable of handling all of the colors needed.JPEGs have a lossy way of compressing images. At a low compression value, this is largely not noticeable, but at high compression, an image can become blurry and messy.

BMP - Bitmap Format

uses a pixel map which contains line by line information.

It is a very common format, as it got its start in Windows.

This format can cause an image to be super large.

Image File Formats

GIF - Graphics Interchange Format

GIF is the most popular on the Internet, mainly because of its small file size. It is ideal for small navigational icons and simple diagrams and illustrations where accuracy is required, or graphics with large blocks of a single color. The format is loss-less, meaning it does not get blurry or messy.     The 256 color maximum is sometimes tight, and so it has the option to dither, which means create the needed color by mixing two or more available colors. GIF use a simple technique called LZW compression to reduce the file sizes of images by finding repeated patterns, but this compression never degrades the image quality.GIF can also be animated.

Low Level Object Handling:

• Object representation

Low Level Object Handling:

• Segmentation

The “bottom-up” approach

These operations fit into a processing scheme strongly associated with David

Marr, whose seminal book Vision appeared in 1980.

Marr espoused a principle of least commitment, and proposed a processing

scheme involving a series of representations:

• Grey level array (the image, in effect)

• Raw primal sketch (edges)

• Primal sketch (groupings of edges)

• Two-and-a-half-D sketch (surface depths and orientations, camera centered)

• 3-D model (object-centered shapes and relationships).

In some sense, the 3-D model is taken as the goal of the visual processing. It

can be used for matching against a database of object shapes to achieve object

identification.

But that is not the whole story

A better goal is to produce systems that enable successful interaction with

the environment. Interaction may mean, for example:

•navigating a robot or autonomous vehicle through obstacles, or along a

•road;

•moving a robot arm to manipulate parts for assembly;

•recognizing human gestures and movements for computer control;

•identifying images in a database on the basis of their content.

• For many applications, a top-down, model-based or hypothesis-driven

approach is more successful. In such an approach the system starts from an assumption about what is in front of it, and tests and updates this hypothesis to attempt to match the image data.

• Vision is becoming increasingly dynamic. Change and motion are integral

to the goals and methods, not simply techniques for recognizing shape or

inferring the third dimension. Dynamic vision needs to be predictive and

goal-directed.

• Biological vision remains the most important inspiration for computer

vision. Increasing attention is being paid to the role of foveal vision and

eye movements. And computer modeling continues to shed light on how

biological visual systems work.

Object Recognition:

• Color, Texture, Shape

Object Recognition:

• Applications

• Character recognition• Face Recognition• Shape Recognition (Image

Databases)

3D Distance Histogram

(MATLAB DEMO)

The Interface (JAVA – Applet)

The Sketchpad: Query by Shape

The First Guess: Different Shape - Classes

Selected shape defines query by shape – class

Result

Specification of different shape in shape – class

Result

Let's go for another shape...

...first guess...

...and final result

Query by Shape, Texture and Keyword

Result