A Multiobjective Approach to Homography Estimation

Abstract

In several machine vision problems, a relevant issue is the estimation of homographies between two different perspectives that hold an extensive set of abnormal data. A method to find such estimation is the random sampling consensus (RANSAC); in this, the goal is to maximize the number of matching points given a permissible error (Pe), according to a candidate model. However, those objectives are in conflict: a low Pe value increases the accuracy of the model but degrades its generalization ability that refers to the number of matching points that tolerate noisy data, whereas a high Pe value improves the noise tolerance of the model but adversely drives the process to false detections. This work considers the estimation process as a multiobjective optimization problem that seeks to maximize the number of matching points whereas Pe is simultaneously minimized. In order to solve the multiobjective formulation, two different evolutionary algorithms have been explored: the Nondominated Sorting Genetic Algorithm II (NSGA-II) and the Nondominated Sorting Differential Evolution (NSDE). Results considering acknowledged quality measures among original and transformed images over a well-known image benchmark show superior performance of the proposal than Random Sample Consensus algorithm.

Homography estimation from a single-point correspondence using template matching and particle swarm optimization

“…Over the years, several successful variants of the RANSAC algorithm for homography estimation have been suggested [9][10][11]. Alternatively, computational approaches such as neural networks and evolutionary computation have also been explored for homography estimation obtaining fruitful results [12][13][14]. Nevertheless, all these methods still require an accurate specification of several point correspondences and a minimum of outliers.…”

Homography Ranking Based on Multiple Groups of Point Correspondences

“…However, these correspondences are often noisy and they can introduce errors in the homography estimation. Although four keypoints are satisfactory, often a greater number of keypoints is used, allowing us to use optimization to minimize a suitable cost function [ 16 , 17 ]. Then, outlier removal becomes an important step, and algorithms such as RANSAC [ 18 ] are usually employed [ 16 ].…”

Full Explicit Consistency Constraints in Uncalibrated Multiple Homography Estimation

“…A recent addition to this list is the problem of color transfer [14,17]. Various methods for estimating a single homography are available [19] and new techniques emerge on a regular basis [3,18,27,31,41].…”