An improved RANSAC algorithm for extracting roof planes from airborne lidar data

Most visual navigation relies on point matching and many of the matching point pairs are faulty. Generally, they are eliminated by the Random Sample Consensus (RANSAC) algorithms. However, RANSAC algorithms are often time-consuming and their parameters are hard to adjust. For such a purpose, a new method is proposed to eliminate the error matching points of every two consecutive images based on motion information. The proposed method is applicable to the cases that the motion information is available and the interest points are all from a horizontal plane. It does not require iteration and thus time-efficient. To demonstrate the effectiveness, the proposed method is tested on synthetic data of both simulation points and real images. The results show that the proposed method has better performance over RANSAC algorithms.

When an operator teleoperates a rescue robot, it is necessary for the operator to understand the relationship between the robot and its environment while indirect-vision driving. A LRF (Laser Range Finder) installed on a rotating stage is often used for scanning the environment in order to give the operator spatial information, while there is a tradeoff between its scanning speed and point density. To solve the problem, we implement plane detection by RANSAC (RANdom SAmple Consensus) algorithm, integration of overlapped planes using tables and drawing them as convex hulls. The experimental results show that the scanning speed could be improved because the result of 3D scanning with a low point density shows a good match for that with a high point density by using the implemented methods.

Orthognathic surgery is applied to restore esthetical facial profile and functional occlusion for patients with dentofacial deformity. Virtual surgical planning (VSP) is indispensable for precise and individualized treatment. Manually designing osteotomy planes is time-consuming and highly experience-dependent. This study aimed to develop and validate an automatic osteotomy plane design method based on deep learning. Methods: A deep learning model, Ortho-OPD (orthognathic osteotomy planes de signer), was proposed, consisting of a segmentation network and the random sample consensus (RANSAC) algorithm. The segmentation network was based on a convolutional neural network (CNN), orthognathic segmenting the craniomaxillofacial (CMF) CT data. Osteotomy planes were then defined by the RANSAC algorithm. Ortho-OPD was trained on 71 samples and tested on 31 cases. The performance was evaluated quantitatively and qualitatively. Results: Ortho-OPD functioned smoothly, and all cases were successfully performed. The 3D boundary-sensitive loss was employed to optimize precision. Evaluation metrics included accuracy and clinical efficiency. The mean dice similarity coefficient (DSC) was 0.920.032 in CMF seg mentation. Ortho-OPD showcased excellent productivity, taking an average of about 9 seconds to complete virtual bimaxillary osteotomy compared to manual work. The angular errors between the predicted planes and ground truth planes, plus the shortest distance from the neural tube or the adjacent apical points to predicted planes, were examined, indicating no significant difference and reliability for preserving vital anatomical structures. Overall, the automatic osteotomy plane design from raw CT data was realized using Ortho-OPD, composed of CNN and RANSAC, providing an efficient and ideal alternative in orthognathic osteotomy planning.

It is important to get reliable measurement data in Electrical impedance tomography (EIT) for getting satisfactory reconstruction result. Because EIT inverse problem is non-linear and ill-posed, incorrect data such as measured data through fault electrode can effect the estimation of resistivity distribution. So, EIT measurement system with methods for checking reliability has been developed. However, most existing methods require extra cost to evaluate reliability of EIT system. This paper presents modified Gauss-Newton (GN) method based on random sample consensus (RANSAC) algorithm for finding fault electrodes and getting good reconstructed image with faulty data for two-phase flow application. Also, suitable residual equation is proposed for determining the threshold to use RANSAC algorithm. Numerical simulations are performed to validate the performance of the proposed method. The results show that the proposed method has a good reconstruction performance compared to the conventional GN method.

Image stitching is one of the branches of computer vision. It combines two or more images for a scene to acquire a high-resolution panoramic image. An invariant local function often uses to stitch two images together. Since the flat plate of a digital radiography (DR) system does not cover all parts of the body, the whole bone structure image cannot seize in a single scan. To solved this problem, image stitching is broadly utilized by medical systems to stitch DR images, which can be helpful for scoliosis or lower extremity deformities in the diagnosis, and pre-operative planning are of great importance. In this paper, the stitching and retrieval of medical images planned. To conquer the background noise in medical images, and improve the recovery of quality and stitching rapidity of medical images, a random sample consensus (RANSAC) algorithm is useful to stitching the images of Chest digital radiography by scale-invariant feature transform (SIFT) and speeded-up robust features (SURF) feature extraction. Down-sampling utilizes to lessen the size of the images and reduction the measure of calculation. In the interim, the phase correlation is engaged to discover the overlapping region. After feature matching and perspective transformation, the stitched image is gotten dependent on the homography. At last, experimentation has finished showing the presentation.

Subpixel translation estimation using phase correlation is a fundamental task for numerous applications in the remote sensing community. The major drawback of the existing subpixel phase correlation methods lies in their sensitivity to corruption, including aliasing and noise, as well as the poor performance in the case of practical remote sensing data. This paper presents a novel subpixel phase correlation method using singular value decomposition (SVD) and the unified random sample consensus (RANSAC) algorithm. In the proposed method, SVD theoretically converts the translation estimation problem to one dimensions for simplicity and efficiency, and the unified RANSAC algorithm acts as a robust estimator for the line fitting, in this case for the high accuracy, stability, and robustness. The proposed method integrates the advantages of Hoge's method and the RANSAC algorithm and avoids the corresponding shortfalls of the original phase correlation method based only on SVD. A pixel-to-pixel dense matching scheme on the basis of the proposed method is also developed for practical image registration. Experiments with both simulated and real data were carried out to test the proposed method. In the simulated case, the comparative results estimated from the generated synthetic image pairs indicate that the proposed method outperforms the other existing methods in the presence of both aliasing and noise, in both accuracy and robustness. Moreover, the pixel locking effect that commonly occurs in subpixel matching was also investigated. The degree of pixel locking effect was found to be significantly weakened by the proposed method, as compared with the original Hoge's method. In the real data case, experiments using different bands of ZY-3 multispectral sensor-corrected images demonstrate the promising performance and feasibility of the proposed method, which is able to identify seams of the image stitching between sub-charge-coupled device units.

An important aspect of chemoinformatics and material-informatics is the usage of machine learning algorithms to build Quantitative Structure Activity Relationship (QSAR) models. The RANdom SAmple Consensus (RANSAC) algorithm is a predictive modeling tool widely used in the image processing field for cleaning datasets from noise. RANSAC could be used as a “one stop shop” algorithm for developing and validating QSAR models, performing outlier removal, descriptors selection, model development and predictions for test set samples using applicability domain. For “future” predictions (i.e., for samples not included in the original test set) RANSAC provides a statistical estimate for the probability of obtaining reliable predictions, i.e., predictions within a pre-defined number of standard deviations from the true values. In this work we describe the first application of RNASAC in material informatics, focusing on the analysis of solar cells. We demonstrate that for three datasets representing different metal oxide (MO) based solar cell libraries RANSAC-derived models select descriptors previously shown to correlate with key photovoltaic properties and lead to good predictive statistics for these properties. These models were subsequently used to predict the properties of virtual solar cells libraries highlighting interesting dependencies of PV properties on MO compositions.

A computational problem that arises frequently in computer vision is that of estimating the parameters of a model from data that have been contaminated by noise and outliers. More generally, any practical system that seeks to estimate quantities from noisy data measurements must have at its core some means of dealing with data contamination. The random sample consensus (RANSAC) algorithm is one of the most popular tools for robust estimation. Recent years have seen an explosion of activity in this area, leading to the development of a number of techniques that improve upon the efficiency and robustness of the basic RANSAC algorithm. In this paper, we present a comprehensive overview of recent research in RANSAC-based robust estimation by analyzing and comparing various approaches that have been explored over the years. We provide a common context for this analysis by introducing a new framework for robust estimation, which we call Universal RANSAC (USAC). USAC extends the simple hypothesize-and-verify structure of standard RANSAC to incorporate a number of important practical and computational considerations. In addition, we provide a general-purpose C++ software library that implements the USAC framework by leveraging state-of-the-art algorithms for the various modules. This implementation thus addresses many of the limitations of standard RANSAC within a single unified package. We benchmark the performance of the algorithm on a large collection of estimation problems. The implementation we provide can be used by researchers either as a stand-alone tool for robust estimation or as a benchmark for evaluating new techniques.

Aiming at the problems that the current RANSAC (Random Sample Consensus) algorithm has too large randomness and is susceptible to external point interference, which leads to the reduction of matching accuracy, an improved RANSAC algorithm combining feature matching confidence and grid clustering is proposed. Firstly, rough matching is carried out by the FLANN algorithm, and confidence analysis is carried out on the coarse matching point pairs, then expanding grid clustering around the high confidence point pairs. Multiple local optimal interior points are screened to optimize the global interior points and improve the matching accuracy of feature points. The experimental results show that the improved RANSAC in this paper increases the existence probability of interior points, avoids too many wrong feature matching affecting the model effect of the homography matrix, and improves the accuracy of feature matching. KeywordsRANSAC algorithmGrid clusteringConfidence analysis

Purpose:Current systems for obtaining respiratory information at time of treatment typically require the use of additional equipment and add to patient setup time. In this study, we develop a fully automated method to track respiratory motion by tracking the motion of the diaphragm surface, making use of only current imaging technology and without the need for additional mechanical, pneumatic, or optical devices.Methods:A statistical algorithm, RANdom SAmple Consensus (RANSAC), was used for direct tracking of the diaphragm surface through kV CBCT projection images. A third order polynomial was used to model the shape of the diaphragm. Model constraints based on the diaphragm shape as well as a small motion assumption (between adjacent projections) were incorporated to constrain the model. The changing position of the diaphragm along the Superior‐Inferior direction as the patient respires then gives the respiratory trace.Results:On the patients studied, the respiratory trace can be obtained with between 5,000 and 1,000,000 iterations of the RANSAC algorithm, per projection. Visual comparison with the projection images in a cine like mode shows the computed respiratory trace matches well with the true respiratory cycle. With a low number of iterations (<10,000 per projection), the diaphragm location computation time is similar to projection image acquisition and therefore the trace can be computed in real time.Conclusion:The RANSAC algorithm is a viable method for obtaining a respiratory trace with the patient on the treatment couch. Implementing the RANSAC algorithm on a GPU should allow for a significantly larger number of iterations while maintaining the ability to acquire the respiratory trace in real time. This method of obtaining a respiratory trace should reduce costs for technology such as 4DCBCT as well as reduce patient setup times and improve patient comfort.

Acoustic source localization has been an hot research topic with widespread applications in many fields. In the noisy environment or when the reverberation is considerable, the source localization problem becomes challenging and many existing algorithms deteriorate. The paper proposes a robust algorithm which combines the RANdom SAmple Consensus (RANSAC) algorithm, and the Generalized Cross-Correlation (GCC) based Time Difference of Arrival (TDOA). Experiments in real world data show that the proposed algorithm has significantly better performance than the traditional algorithm.

Using a single sensor to determine the pose estimation of a device cannot give accurate results. This paper presents a fusion of an inertial sensor of six degrees of freedom (6-DoF) which comprises the 3-axis of an accelerometer and the 3-axis of a gyroscope, and a vision to determine a low-cost and accurate position for an autonomous mobile robot. For vision, a monocular vision-based object detection algorithm speeded-up robust feature (SURF) and random sample consensus (RANSAC) algorithms were integrated and used to recognize a sample object in several images taken. As against the conventional method that depend on point-tracking, RANSAC uses an iterative method to estimate the parameters of a mathematical model from a set of captured data which contains outliers. With SURF and RANSAC, improved accuracy is certain; this is because of their ability to find interest points (features) under different viewing conditions using a Hessain matrix. This approach is proposed because of its simple implementation, low cost, and improved accuracy. With an extended Kalman filter (EKF), data from inertial sensors and a camera were fused to estimate the position and orientation of the mobile robot. All these sensors were mounted on the mobile robot to obtain an accurate localization. An indoor experiment was carried out to validate and evaluate the performance. Experimental results show that the proposed method is fast in computation, reliable and robust, and can be considered for practical applications. The performance of the experiments was verified by the ground truth data and root mean square errors (RMSEs).

Estimating the homography matrix between images captured under radically different camera poses and zoom factors is a complex challenge. Traditional methods rely on the Random Sample Consensus (RANSAC) algorithm, which requires pairs of homologous points, pre-matched based on local image feature vectors. Sampling consensus is a core step in many Artificial Intelligence (AI) algorithms that enable computer systems to recognize patterns in data. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i>-RANSAC, an algorithm for homography estimation that eliminates the need for feature vectors or explicit point pairing, while it optionally supports point labeling into two classes. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i>-RANSAC introduces a novel geometric (cheiral) criterion that intelligently rejects implausible point configurations at the beginning of each iteration, while leveraging concave quadrilaterals typically discarded by similar algorithms. A post-hoc criterion at the end of each iteration improves accuracy further. Analytical derivations of the expected maximum iterations are provided, considering success probabilities and outlier rates, enabling adaptive performance tuning. The algorithm is validated on a demanding task: estimating homography between video frames of football matches captured by 12 cameras with highly divergent viewpoints. Results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i>-RANSAC significantly outperforms state-of-the-art classical methods, combined with deep learning-based salient point detection, in terms of average reprojection error and success rates. The relevant implementation is available in https://github.com/gnousias/H-RANSAC.

In order to improve the speed of image registration,a fast method based on improved RANdom SAmple Consensus(RANSAC) algorithm was proposed.At first,Harris corner detector was used to extract the feature points in the reference and target images.Next,based on the proximity and similarity of their intensity,the feature points were matched.Finally,the improved RANSAC algorithm was used to estimate transform matrix more fast and accurately.To improve the speed of computation,a method of pre-detection was used to discard those temporary models that were not preview models.To delete outliers a random block selecting method was used to select samples,which improves the precisions.The experiment shows that this algorithm reduces the amount of computation largely and improves the speed of image registration when the precisions do not have notable change.

In recent years, biking has been popular and getting more and more attention in many country. However, because of its poor protectability, the accident will often result in serious consequences. This study combines a bicycle, a camera and a handheld computing device, and applies a vision-based approaching-vehicle detection algorithm to remind driver to ”watch” his/her back. For the approaching-vehicle detection algorithm, firstly, the optical flow estimation is applied to track a small number of feature points. These tracked points are utilized to estimate the homography transform between two consecutive frames by using RANdom SAmple Consensus algorithm (RANSAC). Therefore, two consecutive frames can be aligned and moving foreground can thus be extracted by performing image subtraction. Once again, feature points within the moving foreground are tracked and weighted by the camera geometry of vanishing point. The weighting procedure eliminates false alarms and effectively detects the vehicles which are approaching the camera. The system has been successfully implemented on the iPhone platform, and validated the feasibility of the system and the effectiveness of the algorithm in the general urban scenario.