Stereo

1997

Abstract

This

paper

presents

an

approach

to

the

local

stereo

matching

problem

using

edge

segments

as

features

with

several

attributes.

We

have

verified

that

the

differences

in

attributes

for

the

true

matches

cluster

in

a

cloud

around

a

center.

The

correspondence

is

established

on

the

basis

of

the

minimum

squared

Mahalanobis

distance

between

the

difference

of

the

Ž

.

attributes

for

a

current

pair

of

features

and

the

cluster

center

similarity

constraint

.

We

introduce

a

learning

strategy

based

on

the

Self-Organizing

feature-mapping

method

to

get

the

best

cluster

center.

A

comparative

analysis

among

methods

without

learning

is

illustrated.

q

1998

Elsevier

Science

B.V.

All

rights

reserved.

Keywords:

Self-organizing

feature-mapping;

Stereovision;

Local

matching;

Learning;