Lens distortion: image capture, racism and subversion from colonial photography to the iborder

Esta intervenção surgiu do aumento da utilização de tecnologias de reconhecimento facial na gestão da população, mais especificamente no controle da migração europeia. Inspirado por essas circunstâncias, reflito sobre o uso de lentes ópticas desde o início do uso da câmera pelos europeus como arma colonial até os dispositivos de captura de imagens atuais como uma ferramenta de rastreamento para detectar e acampar pessoas em movimento. Creio que esta metodologia arqueológica com sensibilidade estética permite revelar como as técnicas disciplinares contemporâneas de captação de imagens são produzidas por uma relação complexa de poder e saber enquadrada na mesma lógica biométrica de procura da verdade que marcou a dominação colonial europeia. Concluo minha intervenção apresentando uma poderosa obra de arte de uma artista contemporânea que rompe a reivindicação ilusória de verdade científica e imparcialidade que ainda coloniza o sistema de verificação visual e evocando raízes africanas esquecidas da modernidade, em última instância perturbando seu conjunto de relações de poder.

CALIBRATION OF SMARTPHONE’S REAR DUAL CAMERA SYSTEM

This paper aims to calibrate smartphone’s rear dual camera system which is composed of two lenses, namely; wide-angle lens and telephoto lens. The proposed approach handles large sized images. Calibration was done by capturing 13 photos for a chessboard pattern from different exposure positions. First, photos were captured in dual camera mode. Then, for both wide-angle and telephoto lenses, image coordinates for node points of the chessboard were extracted. Afterwards, intrinsic, extrinsic, and lens distortion parameters for each lens were calculated. In order to enhance the accuracy of the calibration model, a constrained least-squares solution was applied. The applied constraint was that the relative extrinsic parameters of both wide-angle and telephoto lenses were set as constant regardless of the exposure position. Moreover, photos were rectified in order to eliminate the effect of lens distortion. For results evaluation, two oriented photos were chosen to perform a stereo-pair intersection. Then, the node points of the chessboard pattern were used as check points.

Model-Independent Lens Distortion Correction Based on Sub-Pixel Phase Encoding

Lens distortion can introduce deviations in visual measurement and positioning. The distortion can be minimized by optimizing the lens and selecting high-quality optical glass, but it cannot be completely eliminated. Most existing correction methods are based on accurate distortion models and stable image characteristics. However, the distortion is usually a mixture of the radial distortion and the tangential distortion of the lens group, which makes it difficult for the mathematical model to accurately fit the non-uniform distortion. This paper proposes a new model-independent lens complex distortion correction method. Taking the horizontal and vertical stripe pattern as the calibration target, the sub-pixel value distribution visualizes the image distortion, and the correction parameters are directly obtained from the pixel distribution. A quantitative evaluation method suitable for model-independent methods is proposed. The method only calculates the error based on the characteristic points of the corrected picture itself. Experiments show that this method can accurately correct distortion with only 8 pictures, with an error of 0.39 pixels, which provides a simple method for complex lens distortion correction.

Using Convolution Neural Network for Defective Image Classification of Industrial Components

Computer vision provides effective solutions in many imaging relation problems, including automatic image segmentation and classification. Artificially trained models can be employed to tag images and identify objects spontaneously. In large-scale manufacturing, industrial cameras are utilized to take constant images of components for several reasons. Due to the limitations caused by motion, lens distortion, and noise, some defective images are captured, which are to be identified and separated. One common way to address this problem is by looking into these images manually. However, this solution is not only very time-consuming but is also inaccurate. The paper proposes a deep learning-based artificially intelligent system that can quickly train and identify faulty images. For this purpose, a pretrained convolution neural network based on the PyTorch framework is employed to extract discriminating features from the dataset, which is then used for the classification task. In order to eliminate the chances of overfitting, the proposed model also employed Dropout technology to adjust the network. The experimental study reveals that the system can precisely classify the normal and defective images with an accuracy of over 91%.

Research on Calibration Method of Binocular Vision System Based on Neural Network

In binocular vision inspection system, the calibration of detection equipment is the basis to ensure the subsequent detection accuracy. The current calibration methods have the disadvantages of complex calculation, low precision, and poor operability. In order to solve the above problems, the calibration method of binocular camera, the correction method of lens distortion, and the calibration method of projector in the binocular vision system based on surface structured light are studied in this paper. For lens distortion correction, on the basis of analyzing the traditional correction methods, a distortion correction method based on radial basis function neural network is proposed. Using the excellent nonlinear mapping ability of RBF neural network, the distortion correction models of different lenses can be obtained quickly. It overcomes the defect that the traditional correction model cannot adjust adaptively with the type of lens. The experimental results show that the accuracy of the method can meet the requirements of system calibration.

F-THETA LENS DISTORTION

The features of F-Theta lenses, the values of the characteristics of the most common models are considered. The conditions for achieving the F-Theta law and the telecentric run of the main rays in the optical scheme of the lens are identified. A formula for the distortion required to provide a linear law, depending on the angle of the inclined beam of rays relative to the optical axisis given. The criteria for the permissible error of deviation from the exact compliance with the linear law are formulated. The relevance of the development of new optical systems for F-Theta lenses is justified.