by Robert Sablatnig
Abstract:
The study of visual object recognition is often motivated by the problem of recognizing 3-d objects given that we receive 2-d patterns of light on our retinae. Recent findings from human psychophysics, neurophysiology and computational vision provide converging evi-dence for a view-based recognition framework in which objects and scenes are represented as collections of viewpoint-specific local features rather than 2-d templates or 3-d mod-els. Hence the recent decade saw a gradual shift away from the 3-d object reconstruction approach pioneered by Marr toward view-based approaches. This report summarizes our contributions to this problem where we focus on the shape as recognition feature and apply these findings in the area of Machine Vision. The first part presents an overview of the framework, motivates the view-based recognition strategy, and introduces the hierachical matching concept. Next, a short summary of a collection of six representative publications of our work carried out in this field, and a discussion of how this fits into the framework is given. The second part consists of the six papers themselves, where we start with a paper on the general framework which is followed by three different applications of the framework in Visual Inspection, Archaeology and Art History. The remaining two papers describe re-cent work performed in 3-d vision as part of the object-based recognition concept. The first paper is on the registration of range data, in which we propose a novel technique for range image registration. The collection ends with a work on combining different 3-d acquisition techniques within the hierarchical framework.
Reference:
Shape Based Machine Vision (Robert Sablatnig), Technical report, PRIP, TU Wien, 2003.
Bibtex Entry:
@TechReport{TR080,
author = "Robert Sablatnig",
title = "Shape {B}ased {M}achine {V}ision",
institution = "PRIP, TU Wien",
number = "PRIP-TR-080",
year = "2003",
url = "https://www.prip.tuwien.ac.at/pripfiles/trs/tr80.pdf",
abstract = "The study of visual object recognition is often
motivated by the problem of recognizing 3-d objects
given that we receive 2-d patterns of light on our
retinae. Recent findings from human psychophysics,
neurophysiology and computational vision provide
converging evi-dence for a view-based recognition
framework in which objects and scenes are
represented as collections of viewpoint-specific
local features rather than 2-d templates or 3-d
mod-els. Hence the recent decade saw a gradual shift
away from the 3-d object reconstruction approach
pioneered by Marr toward view-based approaches. This
report summarizes our contributions to this problem
where we focus on the shape as recognition feature
and apply these findings in the area of Machine
Vision. The first part presents an overview of the
framework, motivates the view-based recognition
strategy, and introduces the hierachical matching
concept. Next, a short summary of a collection of
six representative publications of our work carried
out in this field, and a discussion of how this fits
into the framework is given. The second part
consists of the six papers themselves, where we
start with a paper on the general framework which is
followed by three different applications of the
framework in Visual Inspection, Archaeology and Art
History. The remaining two papers describe re-cent
work performed in 3-d vision as part of the
object-based recognition concept. The first paper is
on the registration of range data, in which we
propose a novel technique for range image
registration. The collection ends with a work on
combining different 3-d acquisition techniques
within the hierarchical framework. ",
}