Computer Vision Detecting the existence, pose and position of known

objects within an image

Michael Horne, Philip Sterne (Supervisor)

Background

• Computer vision is a diverse field– Many schools of thought

• Many different ways of achieving– Monocular– Stereoscopic (quite popular)

Problem Statment

• Recognize objects within a single image– Cutting down the total information in

the images– Extracting and building up useful subset– Using that information to recognize

objects• Model Based system

– System already knows of objects

System structure

• Image capture and segmentation– Pre-process image– Extract useful information

• Matching– Find links between image and objects

known to the system• Pose Solution

– Use the matches to estimate the position and orientation of the object

Image Segmentation

• Feature extraction– Images have lots of information– What features are of interest?– Edges are definitive attributes of

objects– Corners can easily be matched against

Image Segmentation

• Gaussian filter is applied to reduce noise– Noise adds complexity, but no useful

information.• Canny Edge detector is applied to

extract edges

Image Segmentation

• Arbitrary shaped edges are converted into straight line segments.

Similar Geometries

• Need image data to be represented in a similar way to the object model

• Objects are stored as wireframes

• Image data converted to a wireframe like form.

Matching

• To estimate a pose we find correspondences with corners

• Classification problem– Which object to match?– Or what data to match to which objects?

(Multiple object case)• Complex problem• Random Sample Consensus

approach

Matching

• Take only the minimum amount of image data needed to estimate a solution

• But do it at random• Then test the validity of the

estimation

Pose Estimation

• Now using the correspondences• Algorithm based on POSIT

– A quick method– In tune with the RANSAC approach

• POSIT– Scaled orthographic projection model

Pose Estimation

• POSIT – Minimum correspondences is four– Will solve regardless correctness

• Validation is necessary

Pose Estimation

• Checking need to be rapid• Various levels of verification

– Object must not be skewed in making the four points of the object to the image points.

– Distance to estimated position must be minimal

• Estimations that pass undergo further checking

Pose Estimation

• Next step is to project object over image– Number of matches is expanded.– All forward facing vertices are checked

for a corresponding image point• Also the geometries are verified

Pose Estimation

• Goodness ratio = corner ratio * edge ratio– General means of judging the fitted

model

– The higher the ratio, the better the fit• Model fitting with the best ratio is

chosen

Final

• End result

Multiple objects

• After each model is fitted the points used are marked as used– Independence of objects

• The matching process restarts

Results

• Tests were setup for a set of four different objects

• Varying degrees of symmetry

• Images of various poses were captured

• Varying difficulty, degenerate poses to definitive

• 85% success in estimating pose of single objects

Other examples

1 2 3

Multiple Objects

• Multiple objects solved