Vision Computing An Introduction

Visual Perception Sight is our most impressive sense. It gives us, without conscious effort, detailed information about the shape of the world around us The main focus will be on the processing of the raw information that they provide. The basic approach : understand how sensory stimuli are created by the world, and then ask what must the world have been like to produce this particular stimulus?

Human, Computer and Machine Vision the key elements –automatic extraction, manipulation, analysis and classification of images or image sequences to solve real-world problems requires an appreciation of all the issues involved. computer and machine vision

What is “Image Processing and Computer Vision”? Image Processing manipulate image data generate another image Computer Vision process image data generate symbolic data

Image Resolution How many pixels –spatial resolution How many shades of grey/colours –amplitude resolution How many frames per second –temporal resolution

Spatial Resolution n, n/2, n/4, n/8, n/16 and n/32 pixels on a side.

Shades of Grey 8, 4, 2 and 1 bit images.

Temporal Resolution –how much does an object move between frames? –Can motion be understood unambiguously? Nyquist’s Theorem –A periodic signal can be reconstructed if the sampling interval is half the period –An object can be detected if two samples span its smallest dimension

Colour Representation Newton –white light composed of seven colours red, orange, yellow, green, blue, indigo, violet three primaries could approximate many colours red, green, blue C= rR+gG+bB

Other Colour Models YMCK IHS YCrCb etc

Camera Calibration Link image co-ordinates and world co- ordinates Extrinsic parameters –location and orientation of camera with respect to a co-ordinate frame Intrinsic parameters –relate pixel co-ordinates with camera reference frame co-ordinates

Pinhole Camera Image Object Optical centre Image and centre, object and centre are similar triangles. f Z

How Do We Recover 3-D Information? There are number of cues available in the visual stimulus –Motion –Binocular stereopsis –Texture –Shading –Contour Each of these cues relies on background assumptions about physical scenes in order to provide unambiguous interpretation.

Assumption about the scene how to ‘invert’ the process of image formation - assign physical interpretations to the optical features found in the image, in spite of the ambiguities. –About the physical world (low level/early) –About what the machine is looking at (high level/late) –Mapping from low level to high level –An assumption is worth making by a visual system is often that the human visual system makes it.

Image Data Processing Simulate human image perception pre-processing: –Noise removal, contrast enhancement etc. Low level – find useful info from raw images –Colour, edges, shape, texture detection High level – find objects and meanings from the useful info –Objects –Spatial relationship –Meanings The higher level processing, the more domain knowledge needed

System Overview Feature Extraction Labels or other forms of description Pre-processing, enhancement Object Recognition Image Recognition Captured data Knowledge representation

Image Processing

Image Analysis

Image Classification Image classification examples Example reference : os/classify/

What’s Next More examples on approaching “seeing things” in daily life Examples of approaching these problems in programmes Getting familiar with methodologies for providing intelligence in systems