Reference books:

– Digital Image Processing, Gonzalez & Woods.

- Digital Image Processing, M. Joshi

- Computer Vision – a modern approach, Forsyth & Ponce

Robot vision

Md. Atiqur Rahman AhadUniversity of Dhaka

Computer / Robot / Machine vision vs.

Human vision

Machine vs. HumanCamera vs. EyeComputer/Processor vs. BrainArtificial intelligence vs. Human brain…

- Very difficult for a machine – as object varies, number of object varies, dimensional issues, view-/illumination-/angle-/perspective-invariance, etc.

• Computer vision– Endowing machines with the means to

“see”• Create an image of a scene and extract

features

– Very difficult problem for machines• Several different scenes can produce identical

images.• Images can be noisy .• Cannot directly ‘invert’ the image to

reconstruct the scene.

Computer Vision (CV)

• CV - creates a model of the real world from images- recovers useful information about a scene from its two

dimensional projections

• Finding out objects in the scene– Looking for “edges” in the image

• Edge: a part of the image across which the image intensity or some other property of the image changes abruptly.

– Attempting to segment the image into regions.

• Region: a part of the image in which the image intensity or some other property of the image changes only gradually.

1. Image processing stage – transform the original image into something that can be helpful for scene analysis

- Interpreting lines edge detection, edge accumulation, end-point identification

- Curves analysis junctions

2. Scene Analysis stage – attempt to create an iconic [build a model] or a feature-based description of the original scene, providing a task-specific information

Robot-player Identify lines, cornersIdentify the ball [ellipse or circle]Identify players – opponents!

Scene Image Description

Application feedback

Imaging device

MACHINE VISION

Illumination

A typical CV-based control system

Machine Vision Stages

Image Acquisition

Image Processing

Image Segmentation

Image Analysis

Pattern Recognition

Analog to digital conversion

Remove noise,improve contrast…

Find regions (objects) in the image

Take measurements of objects/relationships

Match the description withsimilar description of known objects (models)

Model-based vision: Considering various models and fit into it. - Cylindrical, stick model, etc.- e.g., Hierarchical representation through smaller cylinders to

recreate a person

Stereo vision & depth information:- Stereo vision has two or more cameras- Depth info from a single camera is difficult or almost

impossible – though through texture analysis, it might be possible a bit

- Depth calculate the distance of foreground objects – far or closer!

- Stereo vision – key constraint is correspondence problem or registration problem