The Random Sample Consensus (RANSAC) algorithm is a popular tool for robust estimation problems in computer vision, primarily due to its ability to tolerate a tremendous fraction of outliers. There have been a number of recent efforts that aim to increase the efficiency of the standard RANSAC algorithm. Relatively fewer efforts, however, have been directed towards formulating RANSAC in a manner...

a user-friendly and inexpensive ionic liquid-based single-drop microextraction (il-sdme) method was developed to preconcentrate trace amounts of aminophenol isomers (aps) from human urine, hair dye and water samples prior to analysis by high performance liquid chromatography-ultraviolet spectrophotometry detection (hplc-uv). under optimum conditions (i.e., 3.5 µl of 1-hexyl-3-methylimidazolium ...

We developed a classification approach to multiple quantitative trait loci (QTL) mapping built upon a Bayesian framework that incorporates the important prior information that most genotypic markers are not cotransmitted with a QTL or their QTL effects are negligible. The genetic effect of each marker is modeled using a three-component mixture prior with a class for markers having negligible ef...

We present PPnP, an algorithm capable of estimating a robust camera pose in real-time, even if being provided with large sets of correspondences containing high ratios of outliers. For these situations, standard pose estimation algorithms using RANSAC are often unable to provide a solution or at least not in the required time frame. PPnP is provided with a probability distribution function whic...

When in a pipeline a robust global motion estimation is needed, RANSAC algorithm is the usual choice. Unfortunately, since RANSAC is an iterative method based on random analysis, it is not suitable for real-time processing. This paper presents an outlier removal algorithm, which reaches a robust estimation (at least equal to RANSAC) with really low power consumption and can be employed for embe...