Geometric verification with RANSAC has become a crucial step for many local feature based matching applications. Therefore, the details of its implementation are directly relevant for an application’s run-time and the quality of the estimated results. In this paper, we propose a RANSAC extension that is several orders of magnitude faster than standard RANSAC and as fast as and more robust to de...

In computer vision, and particularly in 3D reconstruction from images, it is customary to be faced with regression problems contaminated by outlying data. The standard efficient method deal them the Random Sample Consensus (RANSAC) algorithm. procedure simple versatile, drawing random minimal samples data estimate parameterized candidate models ranking based on amount of compatible Such evaluat...

RANSAC algorithm is a robust method for model estimation. It is widely used in the extraction of geometry primitives and 3D model reconstruction. However, there has been relatively little comprehensive evaluation in RANSAC-based approach for plane extraction. In order to provide a reference for improving the quality on RANSAC-based approach for roof facets extraction or segmentation, this paper...

Co-registered intensity and range imaging is one example of complementary multi-modality that has been extensively explored for 3D reconstruction, yet not fully researched for robust estimation. The classic RANdom SAmple Consensus and its variants such as PROSAC, Preemptive RANSAC and SCRAMSAC, to name a few, have been the most commonly applied approaches to visionbased homography estimation, w...

Random Sample Consensus (RANSAC) has become one of the most successful techniques for robust estimation from a data set that may contain outliers. It works by constructing model hypotheses from random minimal data subsets and evaluating their validity from the support of the whole data. In this paper we present a novel combination of RANSAC plus Extended Kalman Filter (EKF) that uses the availa...

Feature matching is very important in image stitching. RANSAC algorithm a representative for feature matching. However, still has many shortcomings such as large number of iterations, computational complexity and cannot completely eliminate mismatches. To address above problem, this paper, we propose novel method termed constant elasticity substitution function based (CES-RANSAC) Specifically, ...

Image registration is a vital research topic in image processing. In this paper, we present a novel method to register color image and depth information captured by Kinect for Xbox 360. A contour-based approach is employed to find the correspondence points and estimate the transformation parameters model between color image and depth image. In our paper, the boundary contours are detected by Ca...

A novel algorithm is proposed for registration of two partially overlapping range images that are arbitrarily oriented. The algorithm is inspired by the Shape Index (SI) description of 3D geometry and the Scale Invariant Feature Transform (SIFT) algorithm for detecting and matching features between intensity images. For a range image pair to be registered, we first construct an artificial inten...

Existing sequential feature-based registration algorithms involving search typically either select features randomly (eg. the RANSAC[8] approach) or assume a predefined, intuitive ordering for the features (eg. based on size or resolution). This paper presents a formal framework for computing an ordering for features which maximizes search efficiency. Features are ranked according to matching a...