Due to the presence of rain, the visibility of images captured outdoors on rainy days will be severely degraded. Rain removal using image processing technology can reduce the influence of rain to estimate rain-free images. However, existing traditional rain removal methods are very time-consuming, and newly emerging deep learning-based methods require a large amount of data and computational resources, resulting in long time consumption and poor visual effect. To solve the problems of existing methods, a new method is proposed to remove oblique rain streaks in windy conditions better. Firstly, the directional gradient constraint is proposed to locate oblique rain streaks in the rain layer effectively. Then, a sparse prior for oblique rain streaks is presented to enhance oblique rain streaks removal. After that, an optimization problem combining the directional gradient, sparse priors, and non-negativity constraints is presented. Finally, the alternating direction method of multipliers is exploited to solve the optimization problem effectively. Experiment results show that our method outperforms other methods in removing oblique rain streaks and requires less time.

Geometric primitives contained in three‐dimensional (3D) point clouds can provide the meaningful and concise abstraction of 3D data, which plays a vital role in improving 3D vision‐based intelligent applications. However, how to efficiently and robustly extract multiple geometric primitives from point clouds is still a challenge, especially when multiple instances of multiple classes of geometric primitives are present. In this study, a novel energy minimisation‐based algorithm for multi‐class multi‐instance geometric primitives extraction from the 3D point cloud is proposed. First, an improved sampling strategy is proposed to generate model hypotheses. Then, an improved strategy to establish the neighbourhood is proposed to help construct and optimise an energy function for points labelling. After that, hypotheses and parameters of models are refined. Iterate this process until the energy does not decrease. Finally, models of multi‐class multi‐instance geometric primitives are simultaneously and robustly extracted from the 3D point cloud. In comparison with the state‐of‐the‐art methods, it can automatically determine the classes and numbers of geometric primitives in the 3D point cloud. Experimental results with synthetic and real data validate the proposed algorithm.

Camera calibration is a necessary step to extract 3D information from 2D images. Since the ID object is easy to construct and without self-occlusion, the ID calibration proposed by Zhang has received many attentions. However, the progress in ID calibration mainly focuses on reducing restrictions on the ID object's movements. The calibration accuracy still demands improvements. In this paper, the computational model of ID calibration is reformulated, noises in ID calibration are analyzed with this model, and an heteroscedastic error-in- variables model-based ID calibration algorithm is proposed. In comparison with exiting algorithms, the proposed algorithm has advantages of high accuracy with a small number of measurements, rapid convergence and weak insensitivity to initial conditions. Experiments with both synthetic and real image data validate the proposed algorithm.

Catadioptric imaging systems are widely used in many applications such as robot navigation, surveillance or 3D reconstruction due to their large field of view. However, the catadioptric image has a larger deformation than the perspective image, which makes catadioptric camera calibration more difficult. Previous approaches using lines need conic fitting, which is a hard work for catadioptric images. This paper proposes a nonlinear method based on the constraint from the projection of a space line. A simple and intuitive estimate of the initial camera parameters is also presented, which can be used in some applications without requirement for high accuracy. The proposed method needs no conic fitting that highly affects the calibration accuracy. The validity of the proposed approach is shown by experiments.

The wheel sinkage greatly affects the mobility and localization accuracy of a mobile robot in outdoor deformable terrains. A vision-based method to measure the sinkage is presented. It firstly computes the homography between the wheel plane in 3D space and the image plane of a mounted camera. Then identify the wheel rim in the image by the estimated homography. The difference in image intensity is used to detect wheel-terrain contact points. Considering that the imaging procedure is a projection transformation and some geometric properties do not preserve, detected contact points are transformed into the wheel plane with the estimated homography. Finally compute the wheel sinkage in the wheel plane. System errors introduced by existing methods are eliminated. Additionally, the proposed method does not need costly and complex sensors to measure the relative position between the camera and wheel. Results of experiments show its validity and feasibility.

Trifocal tensor encapsulates the geometric constraints between three views. It plays an important role in computer vision. However elements in measurement matrix of existing linear trifocal tensor estimation algorithms are products of several measurement data, which can amplify measurement error. The factorization algorithm of trifocal tensor estimation is presented, which can overcome this shortcoming. To overcome the deficiency that the extended solution space of factorization algorithm may result in an unstable solution, some modifications are adopted. Synthetic and real image data experiments show that the proposed algorithm is accurate and robust. This provides a new way different from normalized linear algorithm to improve performance of linear algorithm.

It is well known that in order to calibrate a single camera with a one-dimensional (1D) calibration object, the object must undertake some constrained motions, in other words, it is impossible to calibrate a single camera if the object motion is of general one. For a multi-camera setup, i.e., when the number of camera is more than one, can the cameras be calibrated by a 1D object under general motions? In this work, we prove that all cameras can indeed be calibrated and a calibration algorithm is also proposed and experimentally tested. In contrast to other multi-camera calibration method, no one calibrated "base" camera is needed. In addition, we show that for such multi-camera cases, the minimum condition of calibration and critical motions are similar to those of calibrating a single camera with 1D calibration object.