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...

Random Sample Consensus (RANSAC) is the most widely used robust regression algorithm in computer vision. However, RANSAC has a few drawbacks which make it difficult to use for practical applications. Some of these problems have been addressed through improved sampling algorithms or better cost functions, but an important difficulty still remains. The algorithm is not user independent, and requi...

Fisheye cameras are widely used in visual localization due to the advantage of wide field view. However, severe distortion fisheye images lead feature matching difficulties. This paper proposes an IMU-assisted image method called spherically optimized random sample consensus (So-RANSAC). We converted putative correspondences into spherical coordinates and then inertial measurement unit (IMU) pr...

Random Sample Consensus (RANSAC) [3] has been popular in regression problem with samples contaminated with outliers. M-estimator, Hough transform, and others had been utilized before RANSAC. However, RANSAC does not use complex optimization as like M-estimator. It does not need huge amounts of memory as like Hough transform to keep parameter space. RANSAC is simple iteration of two steps: hypot...

Stochastic diffusion search (SDS) is a multi-agent global optimisation technique based on the behaviour of ants, rooted in the partial evaluation of an objective function and direct communication between agents. Standard SDS, the fundamental algorithm at work in all SDS processes, is presented here. Parameter estimation is the task of suitably fitting a model to given data; some form of paramet...

This paper proposes a matching method for medical image registration, which combined with SURF (Speeded up Robust Features) algorithm and the improved R-RANSAC (the Randomized of Random Sample Consensus) algorithm. Firstly, this algorithm extracts featured points with SURF algorithm from images and matches similar featured points with Euclidean distance. Secondly, the RRANSAC algorithm is used ...

In this paper, we present an algorithm that can correctly recognize the state of a Chinese chess game by processing a photo of the chessboard. Some major steps of the algorithm include chessboard rectification using Hough transformation and homographic transformation, chess piece detection using circular Hough transformation and chess piece recognition using SIFT (Scale-Invariant Feature Transf...

This paper introduces a new featurebased image registration algorithm which registers images by finding rotation and scale invariant features and matches them using an evidence accumulation process based on the Generalized Hough Transform. Once feature correspondence has been established, the transformation parameters are then estimated using Non-linear least squares (NLLS) and the standard RAN...

A number of the most powerful robust estimation algorithms, such as RANSAC,MINPRAN and LMS, have their basis in selecting random minimal sets of data to instantiate hypotheses. However, their performance degrades in higher dimensional spaces due to the exponentially decreasing probability of sampling a set that is composed entirely of inliers. In order to overcome this, rather than picking sets...

This paper presents a method for object matching that uses local graphs called keygraphs instead of simple keypoints. A novel method to compare keygraphs was proposed in order to exploit their local structural information, producing better local matches. This speeds up an object matching pipeline, particularly using RANSAC, because each keygraph match contains enough information to produce a po...