Random sample consensus (RANSAC) is a successful algorithm in model fitting applications. It is vital to have strong exploration phase when there are an enormous amount of outliers within the dataset. Achieving a proper model is guaranteed by pure exploration strategy of RANSAC. However, finding the optimum result requires exploitation. GASAC is an evolutionary paradigm to add exploitation capa...

In this paper, we present an approach to the reconstruction of signals exhibiting sparsity in a transformation domain, having some heavily disturbed samples. This sparsity-driven signal recovery exploits carefully suited random sampling consensus (RANSAC) methodology for selection subset inlier To aim, two fundamental properties are used: A sample represents linear combination sparse coefficien...

This letter is concerned with the problem of estimating camera orientation from a set 2D/3D line correspondences, which major part Perspective-n-Line (PnL) problem. There are some cases that usually occur in real applications for PnL: input correspondences corrupted by mismatches (a.k.a. outlier correspondences). The RANdom SAmple Consensus (RANSAC) algorithm de facto standard solving outlier-c...

We present a Random Sampling Consensus (RANSAC) based training approach for the problem of speaker state recognition from spontaneous speech. Our system is trained and tested with the INTERSPEECH 2011 Speaker State Challenge corpora that includes the Intoxication and the Sleepiness Subchallenges, where each sub-challenge defines a two-class classification task. We aim to perform a RANSAC-based ...

This paper proposes a robust fully automated method for geometric coregistration, and an accurate statistical based change detection technique for multitemporal high-resolution satellite imagery. The proposed algorithm is based on four main steps: First, multi-spectral scale-invariant feature transform (M-SIFT) is used to extract a set of correspondence points in a pair, or multiple pairs, of i...

Recent advances in 3D reconstruction allows to acquire highly detailed geometry from a set of images. The outcome of vision-based reconstruction methods is often oversampled, noisy, and without any higher-level information. Further processing such as object recognition, physical measurement, urban modeling, or rendering requires more advanced representations such as computer-aided design (CAD) ...

This article proposes an automatic high-precision detection method for structure parameters of catenary cantilever devices (SPCCDs) using 3-D point cloud data. The steps the proposed are: 1) segmenting and recognizing components devices, 2) extracting plane backbone component axis 3) detecting SPCCD. effective segmentation is critical parameter detection. A recognition based on three-dimensiona...

An efficient method for the continuous extraction of subway tunnel cross sections using terrestrial point clouds is proposed. First, the continuous central axis of the tunnel is extracted using a 2D projection of the point cloud and curve fitting using the RANSAC (RANdom SAmple Consensus) algorithm, and the axis is optimized using a global extraction strategy based on segment-wise fitting. The ...

In this paper, a new method for robustly estimating multiple view relations from point correspondences is presented. The approach combines the popular random sampling consensus (RANSAC) algorithm and the evolutionary method harmony search (HS). With this combination, the proposed method adopts a different sampling strategy than RANSAC to generate putative solutions. Under the new mechanism, at ...

In this contribution we describe a method to assess the activity of vehicles based on airborne image sequences taken by an infrared camera. Active vehicles often appear as a configuration of a dark and a bright spot close to each other. The sensor movement is inferred from image sequences. Due to the fast velocity of the platform estimations of vehicle movements require a precise measurement of...