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Parametric Feature Detection
We propose an algorithm to automatically construct feature detectors for arbitrary parametric features. To obtain a high level of robustness we advocate the use of realistic multi-parameter feature models and incorporate optical and sensing effects. Each feature is represented as a densely sampled parametric manifold in a low dimensional subspace of a Hilbert space. During detection, the brightness distribution around each image pixel is projected into the subspace. If the projection lies sufficiently close to the feature manifold, the feature is detected and the location of the closest manifold point yields the feature parameters. The concepts of parameter reduction by normalization, dimension reduction, pattern rejection, and heuristic search are all employed to achieve the required efficiency. By applying the algorithm to appropriate parametric feature models, detectors have been constructed for five features, namely, step edge, roof edge, line, corner, and circular disc. Detailed experiments are reported on the robustness of detection and the accuracy of parameter estimation. In the case of the step edge, our results are compared with those obtained using popular detectors. We conclude with a brief discussion on the use of relaxation to rene outputs from multiple feature detectors, and sketch a hardware architecture for a general feature detection machine
Robust Geometry Estimation using the Generalized Voronoi Covariance Measure
The Voronoi Covariance Measure of a compact set K of R^d is a tensor-valued
measure that encodes geometric information on K and which is known to be
resilient to Hausdorff noise but sensitive to outliers. In this article, we
generalize this notion to any distance-like function delta and define the
delta-VCM. We show that the delta-VCM is resilient to Hausdorff noise and to
outliers, thus providing a tool to estimate robustly normals from a point cloud
approximation. We present experiments showing the robustness of our approach
for normal and curvature estimation and sharp feature detection
Feature extraction and selection for defect classification of pulsed eddy current NDT
Pulsed eddy current (PEC) is a new emerging nondestructive testing (NDT) technique using a broadband pulse excitation with rich frequency information and has wide application potentials. This technique mainly uses feature points and response signal shapes for defect detection and characterization, including peak point, frequency analysis, and statistical methods such as principal component analysis (PCA). This paper introduces the application of Hilbert transform to extract a new descending feature point and use the point as a cutoff point of sampling data for detection and feature estimation. The response signal is then divided by the conventional rising, peak, and the new descending points. Some shape features of the rising part and descending part are extracted. The characters of shape features are also discussed and compared. Various feature selection and integrations are proposed for defect classification. Experimental studies, including blind tests, show the validation of the new features and combination of selected features in defect classification. The robustness of the features and further work are also discussed
A delay estimation approach to change-point detection
The change-point detection problem is cast into a delay estimation. Using a local piecewise polynomial representation and some elementary algebraic manipulations, we give an explicit characterization of a change-point as a solution of a given polynomial equation. A key feature of this polynomial equation is its coefficients being composed by short time window iterated integrals of the noisy signal. The so designed change-point detector shows good robustness to various type of noises
LDSO: Direct Sparse Odometry with Loop Closure
In this paper we present an extension of Direct Sparse Odometry (DSO) to a
monocular visual SLAM system with loop closure detection and pose-graph
optimization (LDSO). As a direct technique, DSO can utilize any image pixel
with sufficient intensity gradient, which makes it robust even in featureless
areas. LDSO retains this robustness, while at the same time ensuring
repeatability of some of these points by favoring corner features in the
tracking frontend. This repeatability allows to reliably detect loop closure
candidates with a conventional feature-based bag-of-words (BoW) approach. Loop
closure candidates are verified geometrically and Sim(3) relative pose
constraints are estimated by jointly minimizing 2D and 3D geometric error
terms. These constraints are fused with a co-visibility graph of relative poses
extracted from DSO's sliding window optimization. Our evaluation on publicly
available datasets demonstrates that the modified point selection strategy
retains the tracking accuracy and robustness, and the integrated pose-graph
optimization significantly reduces the accumulated rotation-, translation- and
scale-drift, resulting in an overall performance comparable to state-of-the-art
feature-based systems, even without global bundle adjustment
A Generalized Multi-Modal Fusion Detection Framework
LiDAR point clouds have become the most common data source in autonomous
driving. However, due to the sparsity of point clouds, accurate and reliable
detection cannot be achieved in specific scenarios. Because of their
complementarity with point clouds, images are getting increasing attention.
Although with some success, existing fusion methods either perform hard fusion
or do not fuse in a direct manner. In this paper, we propose a generic 3D
detection framework called MMFusion, using multi-modal features. The framework
aims to achieve accurate fusion between LiDAR and images to improve 3D
detection in complex scenes. Our framework consists of two separate streams:
the LiDAR stream and the camera stream, which can be compatible with any
single-modal feature extraction network. The Voxel Local Perception Module in
the LiDAR stream enhances local feature representation, and then the
Multi-modal Feature Fusion Module selectively combines feature output from
different streams to achieve better fusion. Extensive experiments have shown
that our framework not only outperforms existing benchmarks but also improves
their detection, especially for detecting cyclists and pedestrians on KITTI
benchmarks, with strong robustness and generalization capabilities. Hopefully,
our work will stimulate more research into multi-modal fusion for autonomous
driving tasks
Robust Outlier Detection Method Based on Local Entropy and Global Density
By now, most outlier-detection algorithms struggle to accurately detect both
point anomalies and cluster anomalies simultaneously. Furthermore, a few
K-nearest-neighbor-based anomaly-detection methods exhibit excellent
performance on many datasets, but their sensitivity to the value of K is a
critical issue that needs to be addressed. To address these challenges, we
propose a novel robust anomaly detection method, called Entropy Density Ratio
Outlier Detection (EDROD). This method incorporates the probability density of
each sample as the global feature, and the local entropy around each sample as
the local feature, to obtain a comprehensive indicator of abnormality for each
sample, which is called Entropy Density Ratio (EDR) for short in this paper. By
comparing several competing anomaly detection methods on both synthetic and
real-world datasets, it is found that the EDROD method can detect both point
anomalies and cluster anomalies simultaneously with accurate performance. In
addition, it is also found that the EDROD method exhibits strong robustness to
the number of selected neighboring samples, the dimension of samples in the
dataset, and the size of the dataset. Therefore, the proposed EDROD method can
be applied to a variety of real-world datasets to detect anomalies with
accurate and robust performances
A robust and efficient video representation for action recognition
This paper introduces a state-of-the-art video representation and applies it
to efficient action recognition and detection. We first propose to improve the
popular dense trajectory features by explicit camera motion estimation. More
specifically, we extract feature point matches between frames using SURF
descriptors and dense optical flow. The matches are used to estimate a
homography with RANSAC. To improve the robustness of homography estimation, a
human detector is employed to remove outlier matches from the human body as
human motion is not constrained by the camera. Trajectories consistent with the
homography are considered as due to camera motion, and thus removed. We also
use the homography to cancel out camera motion from the optical flow. This
results in significant improvement on motion-based HOF and MBH descriptors. We
further explore the recent Fisher vector as an alternative feature encoding
approach to the standard bag-of-words histogram, and consider different ways to
include spatial layout information in these encodings. We present a large and
varied set of evaluations, considering (i) classification of short basic
actions on six datasets, (ii) localization of such actions in feature-length
movies, and (iii) large-scale recognition of complex events. We find that our
improved trajectory features significantly outperform previous dense
trajectories, and that Fisher vectors are superior to bag-of-words encodings
for video recognition tasks. In all three tasks, we show substantial
improvements over the state-of-the-art results
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