Graph matching (GM) is a fundamental problem in computer science, and it plays a central
role to solve correspondence problems in computer vision. GM problems that incorporate …

We introduce the Suggest-and-Improve framework for general nonconvex quadratically
constrained quadratic programs (QCQPs). Using this framework, we generalize a number of …

This book is an introduction to the fundamental concepts and tools needed for solving
problems of a geometric nature using a computer. It attempts to fill the gap between standard …

Graph matching and MAP inference are essential problems in computer vision and machine
learning. We introduce a novel algorithm that can accommodate both problems and solve …

Graph matching is an essential problem in computer vision that has been successfully
applied to 2D and 3D feature matching and object recognition. Despite its importance, little …

Spectral Matching (SM) is a computationally efficient approach to approximate the solution
of pairwise matching problems that are np-hard. In this paper, we present a probabilistic …

Several algorithms for problems including image segmentation, gradient inpainting and total
variation are based on solving symmetric diagonally dominant (SDD) linear systems. These …

In computer vision, many problems can be formulated as binary quadratic programs (BQPs),
which are in general NP hard. Finding a solution when the problem is of large size to be of …

Abstract Indisputably Normalized Cuts is one of the most popular segmentation algorithms in
pattern recognition and computer vision. It has been applied to a wide range of …

We present a novel feature matching algorithm that systematically utilizes the geometric
properties of image features such as position, scale, and orientation, in addition to the …