Current calibration methods for multimodal systems consisting of structured light and thermography use calibration targets with physical characteristics. However, defects in the manufacturing of these targets are common. Therefore, these methods are prone to undesired errors. We propose a calibration method for a multimodal system (a visible camera, projector, and thermal imaging camera) that does not require the construction of a physical calibration target. For this purpose, thanks to an auxiliary camera, we use a digital screen to obtain the intrinsic parameters of the camera, and a mirror to obtain the intrinsic and extrinsic parameters of the projector and the thermal imaging camera. The experimental results demonstrate that it is possible to elude the challenging task of fabricating physical targets without compromising the accuracy of the system calibration compared to conventional methods.
Reliably detecting or tracking 3D features is challenging. It often requires preprocessing and filtering stages, along with fine-tuned heuristics for reliable detection. Alternatively, artificial intelligence-based strategies have recently been proposed; however, these typically require many manually labeled images for training. We introduce a method for 3D feature detection by using a convolutional neural network and a single 3D image obtained by fringe projection profilometry. We cast the problem of 3D feature detection as an unsupervised detection problem. Hence, the goal is to use a neural network that learns to detect specific features in 3D images using a single unlabeled image. Therefore, we implemented a deep-learning method that exploits inherent symmetries to detect objects with few training data and without ground truth. Subsequently, using a pyramid methodology of rescaling each image to be processed, we achieved feature detections of different sizes. Finally, we unified the detections using a non-maximum suppression algorithm. Preliminary results show that the method provides reliable detection under different scenarios with a more flexible training procedure than other competing methods.
Improving the accuracy of structured light calibration methods has led to the development of pixel-wise calibration models built on top of conventional pinhole-camera models. Because phase encodes depth and transversal information, the pixel-wise methods provide high flexibility to map phase to XYZ coordinates. However, there are different approaches for producing phase-to-coordinate mapping, and there is no consensus on the most appropriate one. In this study, we highlight the current limitations, especially in depth range and accuracy, of several recent pixel-wise calibration methods, along with experimental performance verifications. The results show that there are opportunities for further improving these methods to overcome existing limitations from conventional calibration methods, particularly for low-cost hardware
In structured-light systems, the lens distortions of the camera and the projector reduce the measurement accuracy when calibrated as a standard stereo-vision system. The conventional compensation via distortion coefficients reduces the error, but still leaves a significant residual. Recently, we proposed a hybrid calibration procedure that leverages the standard calibration approach to improve measurement accuracy. This hybrid procedure consisted of building a pixel-wise phase-to-coordinate mapping based on adjusted 3D data obtained from the standard stereo-vision method. Here, we show experimentally that the measurement accuracy can be significantly improved, even using the linear pinhole model and linear mapping functions. We then move to consider the nonlinear model to improve the measurement accuracy further. Encouraging results show that this new calibration method increases the measurement accuracy without requiring elaborate calibration procedures or sophisticated ancillary equipment.
In structured-light systems, the lens distortions of the camera and the projector reduce the measurement accuracy when calibrated as a standard stereo-vision system. The conventional compensation via distortion coefficients reduces the error, but still leaves a significant residual. Recently, we proposed a hybrid calibration procedure that leverages the standard calibration approach to improve measurement accuracy. This hybrid procedure consisted of building a pixel-wise phase-to-coordinate mapping based on adjusted 3D data obtained from the standard stereo-vision method. Here, we show experimentally that the measurement accuracy can be significantly improved, even using the linear pinhole model and linear mapping functions. We then move to consider the nonlinear model to improve the measurement accuracy further. Encouraging results show that this new calibration method increases the measurement accuracy without requiring elaborate calibration procedures or sophisticated ancillary equipment.
It has become customary to calibrate a camera-projector pair in a structured light (SL) system as a stereo-vision setup. The 3D reconstruction is carried out by triangulation from the detected point at the camera sensor and its correspondence at the projector DMD. There are several algebraic formulations to obtain the coordinates of the 3D point, especially in the presence of noise. However, it is not clear what is the best triangulation approach. In this study, we aimed to determine the most suitable triangulation method for SL systems in terms of accuracy and execution time. We assess different strategies in which both coordinates in the projector are known (point-point correspondence) and the case in which only the one coordinate in the DMD is known (pointline correspondence). We also introduce the idea of estimating the second projector coordinate with epipolar constraints. We carried out simulations and experiments to evaluate the differences between the triangulation methods, considering the phase-depth sensitivity of the system. Our results show that under suboptimal phasedepth sensitivity conditions, the triangulation method does influence the overall accuracy. Therefore, the system should be arranged for optimal phase-depth sensitivity so that any triangulation method ensures the same accuracy.
Fringe Projection Profilometry (FPP) is a widely used technique for optical three-dimensional (3D) shape measurement. Among the existing 3D shape measurement techniques, FPP provides a whole-field 3D reconstruction of objects in a non-contact manner, with high resolution, and fast data processing. The key to accurate 3D shape measurement is the proper calibration of the measurement system. Currently, most calibration procedures in FPP rely on phase-coordinate mapping (PCM) or back-projection stereo-vision (SV) methods. The PCM technique consists in mapping experimental metric XYZ coordinates to recovered phase values by fitting a predetermined function. However, it requires accurately placing 2D or 3D targets at different distances and orientations. Conversely, in the SV method, the projector is regarded as an inverse camera, and the system is modeled using triangulation principles. Therefore, the calibration process can be carried out using 2D targets placed in arbitrary positions and orientations, resulting in a more flexible procedure. In this work, we propose a hybrid calibration procedure that combines SV and PCM methods. The procedure is highly flexible, robust to lens distortions, and has a simple relationship between phase and coordinates. Experimental results show that the proposed method has advantages over the conventional SV model since it needs fewer acquired images for the reconstruction process, and due to its low computational complexity the reconstruction time decreases significantly.
KEYWORDS: Skin, Calibration, Cameras, 3D modeling, 3D metrology, Imaging systems, Fringe analysis, 3D image processing, Dermatology, Profilometers, 3D acquisition, 3D imaging metrology, Medical diagnostic instruments
The skin prick test (SPT) is the standard method for the diagnosis of allergies. It consists in placing an array of allergen drops on the skin of a patient, typically the volar forearm, and pricking them with a lancet to provoke a specific dermal reaction described as a wheal. The diagnosis is performed by measuring the diameter of the skin wheals, although wheals are not usually circular which leads to measurement inconsistencies. Moreover, the conventional approach is to measure their size with a ruler. This method has been proven prone to inter- and intra-observer variations. We have developed a 3D imaging system for the 3D reconstruction of the SPT. Here, we describe the proposed method for the automatic measurements of the wheals based on 3D data processing to yield reliable results. The method is based on a robust parametric fitting to the 3D data for obtaining the diameter directly. We evaluate the repeatability of the system under 3D reconstructions for different object poses. Although the system provides higher accuracy in the measurement, we compare the results to those produced by a physician.
Retinal images are used for diagnostic purposes by ophthalmologists. However, despite controlled conditions in acquisition retinal images often suffer from non-uniform illumination which hinder their clinical use. In this work we propose the compensation of the illumination by modeling the intensity as a sum of non-stationary signals using bidimensional empirical mode decomposition (BEMD). We compare the estimation and compensation of the background illumination with a widely used technique based retinal image pixel classification. The proposed method has shown to provide a better estimation of the background illumination, particularly in complicated areas such as the optic disk (usually bright) and the periphery of fundus images (usually dim).
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