Paper Info
Title
Plausible Image Matching: Determining Dense And Smooth Mapping Between Images Without A Priori Knowledge
Abstract
This paper presents a method for automatic determination of dense and smooth mapping between two images without a priori knowledge of either the camera pose or the objects in the images. We designed an algorithm to find the mapping between a pair of arbitrary images, and accomplish automatic image morphing. In order to extract image features which look natural to human, we use a set of linear filters similar to those that are used in early vision. Then the derived vector fields consisting of filter responses are matched with each other through a minimization of the cost function which expresses the similarity of transformed images and mapping smoothness, in a multiresolutional hierarchy. Since the cost function in general is highly nonlinear, we avoid excessive distortion in the estimated mapping by providing a local convexity of mapping in nonlinear optimization. In this paper, a variety of experimental results are discussed for various data sets, including images of rotating objects, static objects, human faces and texture patterns, to demonstrate the performance of the proposed method.
Year
DOI
Venue
2005
10.1142/S0218001405004162
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Keywords
Field
DocType
unconstrained image matching, plausible image matching, rotated Gaussian derivatives, convexity constraint, nonlinear optimization
Data set,Nonlinear programming,A priori and a posteriori,Artificial intelligence,Smoothness,Distortion,Computer vision,Morphing,Linear filter,Pattern recognition,Feature (computer vision),Machine learning,Mathematics
Journal
Volume
Issue
ISSN
19
4
0218-0014
Citations 
PageRank 
References 
1
0.35
19
Authors
3
Name
Order
Citations
PageRank
Shuntaro Yamazaki121015.74
Katsushi Ikeuchi24651881.49
Yoshihisa Shinagawa31900124.80