noRANSAC for fundamental matrix estimation

The estimation of the fundamental matrix from a set of corresponding points is a relevant topic in epipolar stereo geometry [10]. Due to the high amount of outliers between the matches, RANSAC-based approaches [7, 13, 29] have been used to obtain the fundamental matrix. In this paper two new contributes are presented: a new normalized epipolar error measure which takes into account the shape of the features used as matches [17] and a new strategy to compare fundamental matrices. The proposed error measure gives good results and it does not depend on the image scale. Moreover, the new evaluation strategy describes a valid tool to compare different RANSAC-based methods because it does not rely on the inlier ratio, which could not correspond to the best allowable fundamental matrix estimated model, but it makes use of a reference ground truth fundamental matrix obtained by a set of corresponding points given by the use

BANSAC: A dynamic BAyesian Network for adaptive SAmple Consensus

RANSAC-based algorithms are the standard techniques for robust estimation in computer vision. These algorithms are iterative and computationally expensive; they alternate between random sampling of data, computing hypotheses, and running inlier counting. Many authors tried different approaches to improve efficiency. One of the major improvements is having a guided sampling, letting the RANSAC cycle stop sooner. This paper presents a new adaptive sampling process for RANSAC. Previous methods either assume no prior information about the inlier/outlier classification of data points or use some previously computed scores in the sampling. In this paper, we derive a dynamic Bayesian network that updates individual data points' inlier scores while iterating RANSAC. At each iteration, we apply weighted sampling using the updated scores. Our method works with or without prior data point scorings. In addition, we use the updated inlier/outlier scoring for deriving a new stopping criterion for the RANSAC loop. We test our method in multiple real-world datasets for several applications and obtain state-of-the-art results. Our method outperforms the baselines in accuracy while needing less computational time.Comment: ICCV 2023 pape

Combining Shape and Learning for Medical Image Analysis

Automatic methods with the ability to make accurate, fast and robust assessments of medical images are highly requested in medical research and clinical care. Excellent automatic algorithms are characterized by speed, allowing for scalability, and an accuracy comparable to an expert radiologist. They should produce morphologically and physiologically plausible results while generalizing well to unseen and rare anatomies. Still, there are few, if any, applications where today\u27s automatic methods succeed to meet these requirements.\ua0The focus of this thesis is two tasks essential for enabling automatic medical image assessment, medical image segmentation and medical image registration. Medical image registration, i.e. aligning two separate medical images, is used as an important sub-routine in many image analysis tools as well as in image fusion, disease progress tracking and population statistics. Medical image segmentation, i.e. delineating anatomically or physiologically meaningful boundaries, is used for both diagnostic and visualization purposes in a wide range of applications, e.g. in computer-aided diagnosis and surgery.The thesis comprises five papers addressing medical image registration and/or segmentation for a diverse set of applications and modalities, i.e. pericardium segmentation in cardiac CTA, brain region parcellation in MRI, multi-organ segmentation in CT, heart ventricle segmentation in cardiac ultrasound and tau PET registration. The five papers propose competitive registration and segmentation methods enabled by machine learning techniques, e.g. random decision forests and convolutional neural networks, as well as by shape modelling, e.g. multi-atlas segmentation and conditional random fields

On the sample consensus robust estimation paradigm: comprehensive survey and novel algorithms with applications.

Master of Science in Statistics and Computer Science.University of KwaZulu-Natal, Durban 2016.This study begins with a comprehensive survey of existing variants of the Random Sample Consensus (RANSAC) algorithm. Then, five new ones are contributed. RANSAC, arguably the most popular robust estimation algorithm in computer vision, has limitations in accuracy, efficiency and repeatability. Research into techniques for overcoming these drawbacks, has been active for about two decades. In the last one-and-half decade, nearly every single year had at least one variant published: more than ten, in the last two years. However, many existing variants compromise two attractive properties of the original RANSAC: simplicity and generality. Some introduce new operations, resulting in loss of simplicity, while many of those that do not introduce new operations, require problem-specific priors. In this way, they trade off generality and introduce some complexity, as well as dependence on other steps of the workflow of applications. Noting that these observations may explain the persisting trend, of finding only the older, simpler variants in ‘mainstream’ computer vision software libraries, this work adopts an approach that preserves the two mentioned properties. Modification of the original algorithm, is restricted to only search strategy replacement, since many drawbacks of RANSAC are consequences of the search strategy it adopts. A second constraint, serving the purpose of preserving generality, is that this ‘ideal’ strategy, must require no problem-specific priors. Such a strategy is developed, and reported in this dissertation. Another limitation, yet to be overcome in literature, but is successfully addressed in this study, is the inherent variability, in RANSAC. A few theoretical discoveries are presented, providing insights on the generic robust estimation problem. Notably, a theorem proposed as an original contribution of this research, reveals insights, that are foundational to newly proposed algorithms. Experiments on both generic and computer-vision-specific data, show that all proposed algorithms, are generally more accurate and more consistent, than RANSAC. Moreover, they are simpler in the sense that, they do not require some of the input parameters of RANSAC. Interestingly, although non-exhaustive in search like the typical RANSAC-like algorithms, three of these new algorithms, exhibit absolute non-randomness, a property that is not claimed by any existing variant. One of the proposed algorithms, is fully automatic, eliminating all requirements of user-supplied input parameters. Two of the proposed algorithms, are implemented as contributed alternatives to the homography estimation function, provided in MATLAB’s computer vision toolbox, after being shown to improve on the performance of M-estimator Sample Consensus (MSAC). MSAC has been the choice in all releases of the toolbox, including the latest 2015b. While this research is motivated by computer vision applications, the proposed algorithms, being generic, can be applied to any model-fitting problem from other scientific fields

Fast and robust image feature matching methods for computer vision applications

Service robotic systems are designed to solve tasks such as recognizing and manipulating objects, understanding natural scenes, navigating in dynamic and populated environments. It's immediately evident that such tasks cannot be modeled in all necessary details as easy as it is with industrial robot tasks; therefore, service robotic system has to have the ability to sense and interact with the surrounding physical environment through a multitude of sensors and actuators. Environment sensing is one of the core problems that limit the deployment of mobile service robots since existing sensing systems are either too slow or too expensive. Visual sensing is the most promising way to provide a cost effective solution to the mobile robot sensing problem. It's usually achieved using one or several digital cameras placed on the robot or distributed in its environment. Digital cameras are information rich sensors and are relatively inexpensive and can be used to solve a number of key problems for robotics and other autonomous intelligent systems, such as visual servoing, robot navigation, object recognition, pose estimation, and much more. The key challenges to taking advantage of this powerful and inexpensive sensor is to come up with algorithms that can reliably and quickly extract and match the useful visual information necessary to automatically interpret the environment in real-time. Although considerable research has been conducted in recent years on the development of algorithms for computer and robot vision problems, there are still open research challenges in the context of the reliability, accuracy and processing time. Scale Invariant Feature Transform (SIFT) is one of the most widely used methods that has recently attracted much attention in the computer vision community due to the fact that SIFT features are highly distinctive, and invariant to scale, rotation and illumination changes. In addition, SIFT features are relatively easy to extract and to match against a large database of local features. Generally, there are two main drawbacks of SIFT algorithm, the first drawback is that the computational complexity of the algorithm increases rapidly with the number of key-points, especially at the matching step due to the high dimensionality of the SIFT feature descriptor. The other one is that the SIFT features are not robust to large viewpoint changes. These drawbacks limit the reasonable use of SIFT algorithm for robot vision applications since they require often real-time performance and dealing with large viewpoint changes. This dissertation proposes three new approaches to address the constraints faced when using SIFT features for robot vision applications, Speeded up SIFT feature matching, robust SIFT feature matching and the inclusion of the closed loop control structure into object recognition and pose estimation systems. The proposed methods are implemented and tested on the FRIEND II/III service robotic system. The achieved results are valuable to adapt SIFT algorithm to the robot vision applications

Localization of autonomous ground vehicles in dense urban environments

The localization of autonomous ground vehicles in dense urban environments poses a challenge. Applications in classical outdoor robotics rely on the availability of GPS systems in order to estimate the position. However, the presence of complex building structures in dense urban environments hampers a reliable localization based on GPS. Alternative approaches have to be applied In order to tackle this problem. This thesis proposes an approach which combines observations of a single perspective camera and odometry in a probabilistic framework. In particular, the localization in the space of appearance is addressed. First, a topological map of reference places in the environment is built. Each reference place is associated with a set of visual features. A feature selection is carried out in order to obtain distinctive reference places. The topological map is extended to a hybrid representation by the use of metric information from Geographic Information Systems (GIS) and satellite images. The localization is solved in terms of the recognition of reference places. A particle lter implementation incorporating this and the vehicle's odometry is presented. The proposed system is evaluated based on multiple experiments in exemplary urban environments characterized by high building structures and a multitude of dynamic objects

A Novel Improved Probability-Guided RANSAC Algorithm for Robot 3D Map Building

This paper presents a novel improved RANSAC algorithm based on probability and DS evidence theory to deal with the robust pose estimation in robot 3D map building. In this proposed RANSAC algorithm, a parameter model is estimated by using a random sampling test set. Based on this estimated model, all points are tested to evaluate the fitness of current parameter model and their probabilities are updated by using a total probability formula during the iterations. The maximum size of inlier set containing the test point is taken into account to get a more reliable evaluation for test points by using DS evidence theory. Furthermore, the theories of forgetting are utilized to filter out the unstable inliers and improve the stability of the proposed algorithm. In order to boost a high performance, an inverse mapping sampling strategy is adopted based on the updated probabilities of points. Both the simulations and real experimental results demonstrate the feasibility and effectiveness of the proposed algorithm

Probabilistic Outlier Removal for Stereo Visual Odometry

Thesis (MEng)--Stellenbosch University, 2017.ENGLISH ABSTRACT: The field of autonomous navigation is currently receiving significant attention from researchers in both academia and industry. With an end goal of fully autonomous vehicle systems, an increased effort is being made to develop systems that are more efficient, reliable and safe than human-controlled vehicles. Furthermore, the low cost and compact nature of cameras have led to an increased interest in vision-based navigation techniques. Despite their popularity, measurements obtained from cameras are often noisy and contaminated with outliers. A critical requirement for consistent and reliable autonomous navigation is the ability to identify and remove these outliers when measurements are highly uncertain. The focus of the research presented in this thesis is therefore on effective and efficient outlier removal. Many existing outlier removal methods are limited in their ability to handle datasets that are contaminated by a significant number of outliers in real-time. Furthermore, many of the current techniques perform inconsistently in the presence of high measurement noise. This thesis proposes methods for probabilistic outlier removal in a robust, real-time visual odometry framework. No assumptions are made about the vehicle motion or the environment, thereby keeping the research in a general form and allowing it to be applied to a wide variety of applications. The first part of this thesis details the modelling of sensor measurements obtained from a camera pair. The mapping process from 3D space to image space is described mathematically and the concept of triangulating matched image features is presented. Stereo measurements are modelled as random variables that are assumed to be normally distributed in image coordinates. Two techniques used for uncertainty propagation, linearisation and the unscented transform, are investigated. The results of experiments, performed on synthetic datasets, are presented and show that the unscented transform outperforms linearisation when used to approximate the distributions of reconstructed, 3D features. The second part of this thesis presents the development of a novel outlier removal technique, which is reliable and efficient. Instead of performing outlier removal with the standard hypothesise-and-verify approach of RANSAC, a novel mechanism is developed that uses a probabilistic measure of shape similarity to identify sets of points containing outliers. The measure of shape similarity is based on inherent spatial constraints, and is combined with an adaptive sampling approach to determine the probability of individual points being outliers. This novel approach is compared against a state-of-the-art RANSAC technique, where experiments indicate that the proposed method is more efficient and leads to more consistent motion estimation results. The novel outlier removal approach is also incorporated into a robust visual odometry pipeline that is tested on both synthetic and practical datasets. The results obtained from visual odometry experiments indicate that the proposed method is significantly faster than RANSAC, making it viable for real-time applications, and reliable for outlier removal even when measurements are highly uncertain.AFRIKAANSE OPSOMMING: Die area van outonome navigasie kry tans vele aandag van navorsers in akademie en in die bedryf. Met ’n einddoel van volledige outonome navigasie voertuigstelsels, word ’n verhoogde poging gemaak om stelsels te ontwerp wat meer effektief, betroubaar en veiliger is as menslik beheerde voertuie. Verder, die lae prys en kompakte struktuur van kameras het gelei tot ’n verhoogde belangstelling in visie gebaseerde navigasie tegnieke. Ten spyte van hierdie gewildheid, is kamera metings gewoonlik ruiserig en besoedel met uitskieters. ’n Kritiese vereiste vir konsekwente en betroubare outonome navigasie is die vermoë om uitskieters te kan identifiseer en verwyder as die metings hoogs onseker is. Die fokus van die navorsing wat in hierdie tesis aangebied sal word is dus op effektiewe en doeltreffende uitskieterverwydering. Talle bestaande uitskieterverwydermetodes is beperk in hulle vermoë om datastelle besoedel met vele uitskieters intyds te kan hanteer. Verder, talle van die huidige tegnieke tree inkonsekwent in die teenwoordigheid van hoë ruis op. Hierdie tesis stel metodes voor vir waarskynliksheid-verwydering van uitskieters in ’n kragtige, intydse, visuele verplasingsmeter raamwerk. Geen aannames word gemaak oor die voertuig se beweging of die omgewing nie. Die navorsing word dus algemeen gehou en laat toe om toegepas te word op verskillende toepassings. Die eerste gedeelte van hierdie tesis verduidelik die modellering van sensor metings geneem van ’n kamera paar. Die karteringsproses van 3D ruimte na beeld ruimte word wiskundig verduidelik en die konsep van triangulasie van ooreenstemmende beeldkenmerke word aangebied. Stereometings word gebruik as toevalsveranderlikes wat aanvaar word as normaal versprei in die beeld koördinate. Twee tegnieke wat gebruik word vir onsekerheid vooruitskatting, ’n lineariseringsmetode en die sigmapunt-transformasie, word ondersoek. Die resultate van eksperimente wat uitgevoer is op sintetiese datastelle word aangebied, en dit wys dat die sigmapunt-transformasie beter funksioneer as die lineariseringsmetode wanneer dit gebruik word om die verspreiding van gerekonstrueerde, 3D kenmerke te benader. Die tweede gedeelte van hierdie tesis bied die ontwikkeling van ’n nuwe uitskieterverwyderingsmetode, wat betroubaar en doeltreffend is aan. In plaas van uitskieters te verwyder met RANSAC se standaard tegniek van hipotetiseer-en-verifieer, word ’n nuwe meganisme ontwikkel wat vorm ooreenkoms meet om stelle punte wat uitskieters bevat te identifiseer. Die meting van vorm ooreenkoms is gebaseer op ingebore ruimtelike beperkings en word gekombineer met aanpasbare monstering om die waarskynlikheid van sekere punte om uitskieters te wees te bepaal. Hierdie nuwe benadering word vergelyk teen RANSAC waar eksperimente wys dat die voorgestelde metode meer doeltreffend is en lei tot meer konsekwente resultate. Die nuwe uitskieterverwyderingsmetode is ook opgeneem in ’n kragtige visuele verplasingsmeter wat getoets is met beide sintetiese en praktiese datastelle. Die resultate wat behaal is van die visuele verplasingsmeter eksperimente dui aan dat die voorgestelde metode aansienlik vinniger is as RANSAC, wat dit haalbaar maak vir intydse toepassings, en betroubaar is vir uitskieterverywydering al is die metings hoogs onseker