Obtaining The Transfer Function Of A Camera Mount Using Ansys And Matlab

In this thesis, the transfer function of a camera mount is obtained using ANSYS and MATLAB. In a heads-up display (HUD) application, achieving precise image registration requires careful consideration of the camera\u27s position relative to the vehicle. Vibrations can alter the camera\u27s position, with tilt being the most impactful due to its increased displacement effect as objects are further away.This research tackles vibration by predicting the position of the camera using transfer function. To determine the transfer function for complex structures, such as camera mounts, this study aims to first establish a transfer function for a simpler model, like a cantilever beam with a tip-mass. By contrasting the transient response from ANSYS with the theoretical response of the system, the ANSYS modeling is validated as part of the process. The system identification toolbox in MATLAB is then used to produce the transfer function based on the frequency response function (FRF) that was developed in ANSYS harmonic analysis. By comparing the simulated outputs to ANSYS transient responses with the same inputs, the transfer function is verified. The results show that the transfer function developed successfully predicts the position of the camera with an accuracy of 98%

Data-Driven Radiometric Photo-Linearization

In computer vision and computer graphics, a photograph is often considered a photometric representation of a scene. However, for most camera models, the relation between recorded pixel value and the amount of light received on the sensor is not linear. This non-linear relationship is modeled by the camera response function which maps the scene radiance to the image brightness. This non-linear transformation is unknown, and it can only be recovered via a rigorous radiometric calibration process. Classic radiometric calibration methods typically estimate a camera response function from an exposure stack (i.e., an image sequence captured with different exposures from the same viewpoint and time). However, for photographs in large image collections for which we do not have control over the capture process, traditional radiometric calibration methods cannot be applied. This thesis details two novel data-driven radiometric photo-linearization methods suit- able for photographs captured with unknown camera settings and under uncontrolled conditions. First, a novel example-based radiometric linearization method is pro- posed, that takes as input a radiometrically linear photograph of a scene (i.e., exemplar), and a standard (radiometrically uncalibrated) image of the same scene potentially from a different viewpoint and/or under different lighting, and which produces a radiometrically linear version of the latter. Key to this method is the observation that for many patches, their change in appearance (from different viewpoints and lighting) forms a 1D linear subspace. This observation allows the problem to be reformulated in a form similar to classic radiometric calibration from an exposure stack. In addition, practical solutions are proposed to automatically select and align the best matching patches/correspondences between the two photographs, and to robustly reject outliers/unreliable matches. Second, CRF-net (or Camera Response Function net), a robust single image radiometric calibration method based on convolutional neural net- works (CNNs) is presented. The proposed network takes as input a single photograph, and outputs an estimate of the camera response function in the form of the 11 PCA coefficients for the EMoR camera response model. CRF-net is able to accurately recover the camera response function from a single photograph under a wide range of conditions

Reconstruction of high dynamic range images with poisson noise modeling and integrated denoising

In this paper, we present a new method for High Dynamic Range (HDR) reconstruction based on a set of multiple photographs with different exposure times. While most existing techniques take a deterministic approach by assuming that the acquired low dynamic range (LDR) images are noise-free, we explicitly model the photon arrival process by assuming sensor data corrupted by Poisson noise. Taking the noise characteristics of the sensor data into account leads to a more robust way to estimate the non-parametric camera response function (CRF) compared to existing techniques. To further improve the HDR reconstruction, we adopt the split-Bregman framework and use Total Variation for regularization. Experimental results on real camera images and ground-truth data show the effectiveness of the proposed approach

An investigation of the facsimile camera response to object motion

A general analytical model of the facsimile camera response to object motion is derived as an initial step toward characterizing the resulting image degradation. This model expresses the spatial convolution of a time-varying object radiance distribution and camera point-spread function for each picture element in the image. Time variations and these two functions during each convolution account for blurring of small image detail, and variations between, as well as during, successive convolutions account for geometric image distortions. If the object moves beyond the angular extent of several picture elements while it is being imaged, then geometric distortion tends to dominate blurring as the primary cause of image degradation. The extent of distortion depends not only on object size and velocity but also on the direction of object motion, and is therefore difficult to classify in a general sense

Characterization of digital dispersive spectrometers by low coherence interferometry

We propose a procedure to determine the spectral response of digital dispersive spectrometers without previous knowledge of any parameter of the system. The method consists of applying the Fourier transform spectroscopy technique to each pixel of the detection plane, a CCD camera, to obtain its individual spectral response. From this simple procedure, the system-point spread function and the effect of the finite pixel width are taken into account giving rise to a response matrix that fully characterizes the spectrometer. Using the response matrix information we find the resolving power of a given spectrometer, predict in advance its response to any virtual input spectrum and improve numerically the spectrometer's resolution. We consider that the presented approach could be useful in most spectroscopic branches such as in computational spectroscopy, optical coherence tomography, hyperspectral imaging, spectral interferometry and analytical chemistry, among others.Fil: Martínez Matos, Ó.. Universidad Complutense de Madrid; EspañaFil: Rickenstorff, C.. Universidad Complutense de Madrid; EspañaFil: Zamora, S.. Universidad Complutense de Madrid; EspañaFil: Izquierdo, J. G.. Universidad Complutense de Madrid; EspañaFil: Vaveliuk, Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; Argentin