Restoration of Binocular Images Degraded by Optical Scattering through Estimation of Atmospheric Coefficients.

A binocular vision-based approach for the restoration of images captured in a scattering medium is presented. The scene depth is computed by triangulation using stereo matching. Next, the atmospheric parameters of the medium are determined with an introduced estimator based on the Monte Carlo method. Finally, image restoration is performed using an atmospheric optics model. The proposed approach effectively suppresses optical scattering effects without introducing noticeable artifacts in processed images. The accuracy of the proposed approach in the estimation of atmospheric parameters and image restoration is evaluated using synthetic hazy images constructed from a well-known database. The practical viability of our approach is also confirmed through a real experiment for depth estimation, atmospheric parameter estimation, and image restoration in a scattering medium. The results highlight the applicability of our approach in computer vision applications in challenging atmospheric conditions.

Are camera, projector, and camera-projector calibrations different?

Structured light projection systems have become a referent in three-dimensional optical metrology. Calibration of the cameras and projectors of these systems is one of the most critical procedures to achieve high-accuracy measurements. However, the calibration process requires some clarifications for adequate experimental implementation. For instance, it is typically assumed that the calibration of a camera-projector pair differs from calibrating a camera, and the calibration of a projector is possible only with an attached auxiliary camera. This paper presents a unified methodology for camera, projector, and camera-projector calibrations. Experimental results are discussed, providing practical insights into how structured light systems are calibrated. The MATLAB code and data employed in this study are available.

Stereo Image Matching Using Adaptive Morphological Correlation.

A stereo matching method based on adaptive morphological correlation is presented. The point correspondences of an input pair of stereo images are determined by matching locally adaptive image windows using the suggested morphological correlation that is optimal with respect to an introduced binary dissimilarity-to-matching ratio criterion. The proposed method is capable of determining the point correspondences in homogeneous image regions and at the edges of scene objects of input stereo images with high accuracy. Furthermore, unknown correspondences of occluded and not matched points in the scene can be successfully recovered using a simple proposed post-processing. The performance of the proposed method is exhaustively tested for stereo matching in terms of objective measures using known database images. In addition, the obtained results are discussed and compared with those of two similar state-of-the-art methods.

Stereo-phase rectification for metric profilometry with two calibrated cameras and one uncalibrated projector.

Fringe projection profilometry requires calibrating both cameras and projectors for metric measurements. Cameras are relatively simple to calibrate, but projectors require more sophisticated procedures. In this paper, a fringe projection profilometer with two calibrated cameras and one uncalibrated projector is developed for metric measurements. A phase rectification method, which is crucial for stereo matching, is designed by minimizing the perspective distortion. Also, a simple method for point matching using stereo rectified phase maps is proposed. The principles of metric profilometry using the proposed rectification method are introduced. The developed system is evaluated experimentally by the metric measurement of three-dimensional objects. The obtained results confirm a high accuracy of metric measurement and versatility in the design of fringe projection profilometers with uncalibrated projectors.

Homography estimation from a single-point correspondence using template matching and particle swarm optimization.

Existing feature-based methods for homography estimation require several point correspondences in two images of a planar scene captured from different perspectives. These methods are sensitive to outliers, and their effectiveness depends strongly on the number and accuracy of the specified points. This work presents an iterative method for homography estimation that requires only a single-point correspondence. The homography parameters are estimated by solving a search problem using particle swarm optimization, by maximizing a match score between a projective transformed fragment of the input image using the estimated homography and a matched filter constructed from the reference image, while minimizing the reprojection error. The proposed method can estimate accurately a homography from a single-point correspondence, in contrast to existing methods, which require at least four points. The effectiveness of the proposed method is tested and discussed in terms of objective measures by processing several synthetic and experimental projective transformed images.

Distorted pinhole camera modeling and calibration.

Camera modeling and calibration are essential tasks in modern optics. Conventionally, the pinhole model is adopted with a further extension for lens distortion. However, pinhole and distortion models are mutually dependent; thus, the standard approach induces systematic camera calibration errors. This research presents a unifying distorted pinhole camera model that includes a telecentric, distortion-free pinhole, and radial lens distortion as particular cases. An iterative calibration method based on the derived distorted pinhole model is proposed, and experimental evaluation by calibrating a camera with high radial distortion is performed. The calibration results are compared with the standard and fisheye models using a well-known commercial camera calibrator software. The proposed method outperforms the standard model and achieves accuracy comparable to the fisheye model, and the proposed approach is a versatile and accurate tool for diverse optical metrology applications.

Adaptive matched filter for implicit-target recognition: application in three-dimensional reconstruction.

The design of matched filters for optical correlators requires explicit knowledge of the shape of the target. This requirement limits its usefulness in applications where the appearance of the target is unspecified or dynamically changing. This research presents the design of an adaptive correlation filter by the optimization of the mean-squared-error criterion when the shape of the target is implicit and embedded on a cluttered background with unknown statistics in the reference image. For this, estimators to obtain the region of support of the target as well as statistical parameters of additive and nonoverlapping noise of the scene are proposed. The performance of the proposed filter is analyzed in terms of detection efficiency and location accuracy of an implicit target in the context of stereo matching and three-dimensional reconstruction.

Key concepts for phase-to-coordinate conversion in fringe projection systems.

Fringe projection systems encode the scanned object shape as a phase distribution according to the system parameters. However, to obtain the object shape in physical units of length, the demodulated phase must be converted to the coordinates of the observed points on the object surface. The design of a phase-to-coordinate conversion algorithm is straightforward when the following key concepts are considered: cameras and projectors as direction sensors, gratings as coordinate-encoding devices, absolute phase, and triangulation. In this paper, the theoretical principles of these concepts are formalized. Then, an efficient and generalized phase-to-coordinate conversion method, which supports systems with multiple cameras and projectors arranged arbitrarily, is proposed. The usefulness of this approach is illustrated by a 3D surface imaging experiment.

Homography estimation by two PClines Hough transforms and a square-radial checkerboard pattern.

The homography matrix is crucial for many optical metrology applications, such as three-dimensional surface imaging by structured-light projection. In this paper, a straightforward homography estimation method using the image of three particular points on the reference plane is proposed. For this, a new square-radial checkerboard pattern designed to generate three sets of concurrent lines is proposed. The lines and points of interest are detected using two PClines Hough transforms. Relevant concepts such as parallel coordinates and the used Hough transforms are explained. The usefulness of our proposal is verified experimentally.

Target recognition under nonuniform illumination conditions.

A two-step algorithm for reliable recognition of a target imbedded into a two-dimensional nonuniformly illuminated and noisy scene is presented. The input scene is preprocessed with a space-domain pointwise procedure followed by an optimum correlation. The preprocessing is based on an estimate of the source illumination function, whereas the correlation filter is optimized with respect to the mean-squared-error criterion for detecting a target in the preprocessed scene. Computer simulations are provided and compared with those of various common techniques in terms of recognition performance and tolerance to nonuniform illumination as well as to additive noise. Experimental optodigital results are also provided and discussed.

Adaptive phase-input joint transform correlator.

An adaptive phase-input joint transform correlator for pattern recognition is presented. The input of the system is two phase-only images: input scene and reference. The reference image is generated with a new iterative algorithm using phase-only synthetic discriminant functions. The algorithm takes into account calibration lookup tables of all optoelectronics elements used in optodigital experiments. The designed adaptive phase-input joint transform correlator is able to reliably detect a target and its distorted versions embedded into a cluttered background. Computer simulations are provided and compared with those of various existing joint transform correlators in terms of discrimination capability, tolerance to input additive noise, and to small geometric image distortions. Experimental optodigital results are also provided and discussed.

Pattern recognition with an adaptive joint transform correlator.

An adaptive joint transform correlator for real-time pattern recognition is presented. A reference image for the correlator is generated with a new iterative algorithm. The training algorithm is based on synthetic discriminant functions. The obtained reference image contains the information needed to reliably discriminate a target against known false objects and a cluttered background. Calibration lookup tables of all optoelectronics elements used are included in the design of the adaptive joint transform correlator. Two methods for the implementation of the proposed joint transform correlator in an optodigital setup are considered. Experimental results are provided and compared with those of computer simulations.