Robust Object Classification Approach using Spherical Harmonics

Point clouds produced by either 3D scanners or multi-view images are often imperfect and contain noise or outliers. This paper presents an end-to-end robust spherical harmonics approach to classifying 3D objects. The proposed framework first uses the voxel grid of concentric spheres to learn features over the unit ball. We then limit the spherical harmonics order level to suppress the effect of noise and outliers. In addition, the entire classification operation is performed in the Fourier domain. As a result, our proposed model learned features that are less sensitive to data perturbations and corruptions. We tested our proposed model against several types of data perturbations and corruptions, such as noise and outliers. Our results show that the proposed model has fewer parameters, competes with state-of-art networks in terms of robustness to data inaccuracies, and is faster than other robust methods. Our implementation code is also publicly available1

Robust pooling through the data mode: Robust Point cloud Classification and Segmentation Through Mode Pooling

The task of learning from point cloud data is always challenging due to the often occurrence of noise and outliers in the data. Such data inaccuracies can significantly influence the performance of state-of-the-art deep learning networks and their ability to classify or segment objects. While there are some robust deep-learning approaches, they are computationally too expensive for real-time applications. This paper proposes a deep learning solution that includes novel robust pooling layers which greatly enhance network robustness and perform significantly faster than state-of-the-art approaches. The proposed pooling layers replace conventional pooling layers in networks with global pooling operations such as PointNet and DGCNN. The proposed pooling layers look for data mode/cluster using two methods, RANSAC, and histogram, as clusters are indicative of models. We tested the proposed pooling layers on several tasks such as classification, part segmentation, and points normal vector estimation. The results show excellent robustness to high levels of data corruption with less computational requirements as compared to robust state-of-the-art methods. our code can be found at https://github.com/AymanMukh/ModePooling

Missing data compensation for safety-critical components in a drive-by-wire system

In this article, a new multistep ahead predictive filtering scheme is introduced. The proposed technique is essential for proper operation of safety-critical components in a drive-by-wire car. Because limited computational time and memory are available in drive-by-wire systems, the main advantage of our scheme is that it only requires one set of finite impulse response (FIR) filter weights to be tuned while it can be used for different numbers of steps ahead predictions. To verify and compare the proposed filter, our model and four competing methods were applied to predict up to four missing samples of displacement sensor data from a brake pedal in a brake-by-wire system. Experimental results show that prediction performance of our proposed FIR filter is higher than, or at least comparable to, other filters with the same memory requirements and the computational overhead of data missing handling by our proposed method is considerably lower than other comparable methods. Hence, the proposed filter has a superior performance in missing data compensation for drive-by-wire systems

Fusion of Redundant Information in Brake-By-Wire Systems Using a Fuzzy Voter

In safety critical systems such as brake-by-wire, fault tolerance is usually provided by virtue of having redundant sensors and processing hardware. The redundant information provided by such components should be properly fused to achieve a reliable estimate of the safety critical variable that is sensed or processed by the redundant sensors or hardware. Voting methods are well-known solutions for this category of fusion problems. In this paper, a new voting method, using a fuzzy system for decision-making, is presented. The voted output of the proposed scheme is a weighted average of the sensors signals where the weights are calculated based on the antecedents and consequences of some fuzzy rules in a rulebase. In a case study, we have tested the fuzzy voter along with the well-known majority voting method for a by-wire brake pedal that is equipped with a displacement sensor and two force sensors. Our experimental results show that the performance of the proposed voting method is desirable in the presence of short circuits to ground or supply, excessive noise and sensor drifts. Voting error (in terms of mean square error) is reduced by 82% by the proposed fuzzy voting method, compared to majority voting

Multi-Bernoulli sample consensus for simultaneous robust fitting of multiple structures in machine vision

In many image processing applications, such as parametric range and motion segmentation, multiple instances of a model are fitted to data points. The most common robust fitting method, RANSAC, and its extensions are normally devised to segment the structures sequentially, treating the points belonging to other structures as outliers. Thus, the ratio of inliers is small and successful fitting requires a very large number of random samples, incurring cumbrous computation. This paper presents a new method to simultaneously fit multiple structures to data points in a single run. We model the parameters of multiple structures as a random finite set with multi-Bernoulli distribution. Simultaneous search for all structure parameters is performed by Bayesian update of the multi-Bernoulli parameters. Experiments involving segmentation of numerous structures show that our method outperforms well-known methods in terms of estimation error and computational cost. The fast convergence and high accuracy of our method make it an excellent choice for real-time estimation and segmentation of multiple structures in image processing applications

An M-estimator for High Breakdown Robust Estimation in Computer Vision

Several high breakdown robust estimators have been developed to solve computer vision problems involving parametric modeling and segmentation of multi-structured data. Since the cost functions of these estimators are not differentiable functions of parameters, they are commonly optimized by random sampling. This random search can be computationally cumbersome in cases involving segmentation of multiple structures. This paper introduces a high breakdown M-estimator (called HBM for short) with a differentiable cost function that can be directly optimized by iteratively reweighted least squares regression. The fast convergence and high breakdown point of HBM make this estimator an outstanding choice for segmentation of multi-structured data. The results of a number of experiments on range image segmentation and fundamental matrix estimation problems are presented. Those experiments involve both synthetic and real image data and benchmark the performance of HBM estimator both in terms of accurate segmentation of numerous structures in the data and convergence speed in comparison against a number of modern robust estimators developed for computer vision applications (e.g. pbM and ASKC). The results show that HBM outperforms other estimators in terms of computation time while exhibiting similar or better accuracy of estimation and segmentation

Consistency of robust estimators in multi-structual visual data segmentation

A theoretical framework is presented to study the consistency of robust estimators used in vision problems involving extraction of fine details. A strong correlation between asymptotic performance of a robust estimator and the asymptotic bias of its scale estimate is mathematically demonstrated where the structures are assumed to be linear corrupted by Gaussian noise. A new measure for the inconsistency of scale estimators is defined and formulated by deriving the functional forms of four recent high-breakdown robust estimators. For each estimator, the inconsistency measures are numerically evaluated for a range of mutual distances between structures and inlier ratios, and the minimum mutual distance between the structures, for which each estimator returns a non-bridging fit, is calculated