The problem of camera calibration is two-fold. On the one hand, the parameters are estimated from known correspondences between the captured image and the real world. On the other, these correspondences themselves—typically in the form of chessboard corners—need to be found. Many distinct approaches for this feature template extraction are available, often of large computational and/or implementational complexity. We exploit the generalized nature of deep learning networks to detect checkerboard corners: our proposed method is a convolutional neural network (CNN) trained on a large set of example chessboard images, which generalizes several existing solutions. The network is trained explicitly against noisy inputs, as well as inputs with large degrees of lens distortion. The trained network that we evaluate is as accurate as existing techniques while offering improved execution time and increased adaptability to specific situations with little effort. The proposed method is not only robust against the types of degradation present in the training set (lens distortions, and large amounts of sensor noise), but also to perspective deformations, e.g., resulting from multi-camera set-ups.

MATE: Machine Learning for Adaptive Calibration Template Detection.

Chessboard corner detection is a necessary procedure of the popular chessboard pattern-based camera calibration technique, in which the inner corners on a two-dimensional chessboard are employed as calibration markers. In this study, an automatic chessboard corner detection algorithm is presented for camera calibration. In authors' method, an initial corner set is first obtained with an improved Hessian corner detector. Then, a novel strategy that utilises both intensity and geometry characteristics of the chessboard pattern is presented to eliminate fake corners from the initial corner set. After that, a simple yet effective approach is adopted to sort the detected corners into a meaningful order. Finally, the sub-pixel location of each corner is calculated. The proposed algorithm only requires a user input of the chessboard size, while all the other parameters can be adaptively calculated with a statistical approach. The experimental results demonstrate that the proposed method has advantages over the popular OpenCV chessboard corner detection method in terms of detection accuracy and computational efficiency. Furthermore, the effectiveness of the proposed method used for camera calibration is also verified in authors' experiments.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2015.0126

Automatic chessboard corner detection method

Aiming to improve the checkerboard corner detection robustness against the images with poor quality, such as lens distortion, extreme poses, and noise, we propose a novel detection algorithm which can maintain high accuracy on inputs under multiply scenarios without any prior knowledge of the checkerboard pattern. This whole algorithm includes a checkerboard corner detection network and some post-processing techniques. The network model is a fully convolutional network with improvements of loss function and learning rate, which can deal with the images of arbitrary size and produce correspondingly-sized output with a corner score on each pixel by efficient inference and learning. Besides, in order to remove the false positives, we employ three post-processing techniques including threshold related to maximum response, non-maximum suppression, and clustering. Evaluations on two different datasets show its superior robustness, accuracy and wide applicability in quantitative comparisons with the state-of-the-art methods, like MATE, ChESS, ROCHADE and OCamCalib.

CCDN: Checkerboard Corner Detection Network for Robust Camera Calibration

Planarity of checkerboards is a widely used feature for extrinsic calibration of camera and LiDAR. In this study, we propose two analytically derived covariances of (i) plane parameters and (ii) plane measurement, for precise extrinsic calibration of camera and LiDAR. These covariances allow the graded approach in planar feature correspondences by exploiting the uncertainty of a set of given features in calibration. To construct plane parameter covariance, we employ the error model of 3D corner points and the analytically formulated plane parameter errors. Next, plane measurement covariance is directly derived from planar regions of point clouds using the out-of-plane errors. In simulation validation, our method is compared to an existing uncertainty-excluding method using the different number of target poses and the different levels of noise. In field experiment, we validated the applicability of the proposed analytic plane covariances for precise calibration using the basic planarity-based method and the latest planarity-and-linearity-based method.

Analytic Plane Covariances Construction for Precise Planarity-based Extrinsic Calibration of Camera and LiDAR

In this paper, we propose an accurate automatic chessboard corner detecting algorithm of sub-pixel precision. The algorithm is based on a novel hyperbolic tangent model. A set of corners are detected initially by Harris' algorithm. Then the hyperbolic tangent model for each corner point is optimized by parameter adjustment. Finally, we use the optimal model to obtain the sub-pixel position of each corner with fake corner elimination. Experiments on three datasets show that the proposed method achieves a detection rate of at least 98% and a reprojection error in camera calibration of at most 0.85 pixels. Our algorithm significantly outperforms the methods in three related works.

A new algorithm for accurate and automatic chessboard corner detection

The authors point out the limitations of SUSAN corner detector in detecting chessboard corner, then describe an improved SUSAN(Smallest Univalue Segment Assimilating Nucleus) detector algorithm for detecting chessboard corner on the basis of symmetrical geometry structure of USAN (Univalue Segment Assimilating Nucleus) area. And the algorithm has been applied to the chessboard images on real photos. The improved algorithm can quickly detect corner from real photos shot from every angle. The theory of detecting corner at sub-pixel level is Orthogonal Vector Theory, that is, vector from the corner to its adjacent area pixel point should be vertical to gray grads of the adjacent area pixel point. In order to get the coordinate of corner at sub-pixel level, we establish the neighboring area equation and solve it via iterative method, and propose to check its validity according to cross ratio invariability in perspective projection. Keywords-insert chessboard corner; USAN; sub-pixel; cross ratio invariability

A Fast and Accurate Algorithm for Chessboard Corner Detection

Iron-oxidizing bacteria (FeOB) could oxidize Fe(II) and mediate biomineralization, which provides the possibility for its potential application in arsenic (As) remediation. In the present study, a strain named Ochrobactrum EEELCW01 isolated previously, was inoculated into paddy soils to investigate the effect of FeOB inoculation on the As migration and transformation in paddy soils. The results showed that inoculation of Ochrobactrum sp. increased the proportion of As in iron-aluminum oxide binding fraction, which reduced the As bioavailability in paddy soils and effectively reduced the As accumulation in rice tissues. Moreover, the inoculation of iron oxidizing bacteria increased the abundance of KD4-96, Pedosphaeraceae and other bacteria in the soils, which could reduce the As toxicity in the soil through biotransformation. The abundance of metabolites such as carnosine, MG (0:0/14:0/0:0) and pantetheine 4’-phosphate increased in rhizosphere soils inoculated with FeOB, which indicated that the defense ability of soil-microorganism-plant system against peroxidation caused by As was enhanced. This study proved that FeOB have the potential application in remediation of As pollution in paddy soil, FeOB promotes the formation of iron oxide in paddy soil, and then adsorbed and coprecipitated with arsenic. On the other hand, the inoculation of Ochrobactrum sp. change soil microbial community structure and soil metabolism, increase the abundance of FeOB in soil, promote the biotransformation process of As in soil, and enhance the resistance of soil to peroxide pollution (As pollution).

Arsenic Transformation in Soil-Rice System Affected by Iron-Oxidizing Strain (Ochrobactrum sp.) and Related Soil Metabolomics Analysis

Arsenic biotransformation genes and As transportation in soil-rice system affected by iron-oxidizing strain (Ochrobactrum sp.).

Oleic acid desaturase (FAD2) is the key enzyme that produces polyunsaturated fatty acids in rapeseed (Brassica napus L), which is one of the main oil crops. RNA interference (RNAi) is an emerging technique that provides new opportunities for the generation of new traits in plants. To increase oleic acid content and reduce linoleic and linolenic acid content in rapeseed, we constructed an ihpRNA plant expression vector of the FAD2 gene and obtained transgenic plants for multiple generations by stable inheritance. In this study, third-generation transgenic plants (T3), seventh-generation transgenic plants (T7), and wild-type plants (WT) were used. The differences in microbial community diversity between transgenic plants and wild-type plants and the up- and downregulation of rhizosphere metabolite contents were investigated. In conclusion, the results showed that the soil microbial community structure was stable, the general microbial community structure was not changed by the transgenic rhizosphere exudates, and no significant harmful root exudate of transgenic rapeseed on the environment was found through the microbial community and metabolomics analysis. This work may provide an understanding of the impact of RNAi on plant metabolites and a safety evaluation method for transgenic plants and a reference for rapeseed breeding.

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Libing Xia

Papers

A Fast and Accurate Algorithm for Chessboard Corner Detection

Arsenic Transformation in Soil-Rice System Affected by Iron-Oxidizing Strain (Ochrobactrum sp.) and Related Soil Metabolomics Analysis

Arsenic biotransformation genes and As transportation in soil-rice system affected by iron-oxidizing strain (Ochrobactrum sp.).

Safety evaluation of FAD2 RNAi transgenic Brassica napus L. based on microbial diversity and metabonomic analysis

Arsenic Transportation and its Biotransformation Genes in Soil-Rice System Affected by Iron Oxidizing Strain (Ochrobactrum Sp.)