With the development of machine vision technology, in the process of visual navigation with images, it is necessary to match the local geometric features or global features of the images; however, the matching of local geometric features is low in accuracy and difficult to be used in tracking. In contrast, template-based global feature matching can directly use the information of the entire image, and it has high robustness to illumination variations and occlusions, so it has attracted widespread attention. At present, the classical matching algorithms based on templates mainly include Sum of Absolute Differences (SAD), Sum of Squared Differences (SSD), Normalized Cross Correlation (NCC), and Mutual Information (MI). In order to make it more reasonable to evaluate and compare the performance of the algorithms, in this paper, we decided to compare Mean Absolute Differences (MAD), Mean Square Differences (MSD), Zero-mean Normalized Cross Correlation (ZNCC), and Normalized Mutual Information (NMI). During the experiment, the Gaussian noise, illumination variations and occlusion were applied to the current image to simulate complex navigation scenes, and then matched it with the template images. The matching values obtained by the above four matching algorithms in different scenes were collectively called as alignment metric values. The matching effects of the four algorithms were evaluated from the following aspects including the smoothness of the metric value, the number of local extremums and whether the best position was in the correct alignment position. The results showed that the accuracy of MSD was greatly affected by noise and was not suitable for scenes interfered by noise, the number of local extremums of ZNCC changed greatly under the conditions of noise, illumination changes, and occlusion, the alignment metric values became unsmooth. In comparison, the NMI showed good robustness and accuracy in different conditions.
Three-dimensional digital image correlation (3D DIC), which combines binocular stereo vision and digital image cross-correlation matching technology, can be used to restore the three-dimensional and deformation information of the object under test. 3D DIC can be accomplished by matching the subset in the left(right) image with that in the right(left). The size of the matching window is found to be critical to the measurement accuracy. Nevertheless, when the subset is small, the measurement accuracy and resolution are high but very sensitive to noise. In contrast, for large subset the measurement accuracy and resolution are lower, while the measurement is more robust to noise. To combine the advantages of high precision and robustness, the Spatio-temporal cross-correlation method is proposed in this paper. A set of speckle patterns are projected onto the objects under test. Instead of the way constructing subsets from spatial neighbor points, the way used in conventional DIC, both spatial and temporal neighbor points are utilized to construct subsets with rich information and strong characteristics. To implement the proposed scheme, we use a mechanical galvanometer to realize the projection of sequential speckle patterns and construct a stereo vision system to realize the three-dimensional reconstruction. The sub-pixel matching algorithm is used to improve the accuracy of stereo matching and 3D reconstruction. Simulations and experiments are carried out to verify the feasibility and success of the proposed scheme and system.
Polarized skylight sensor can calculate the heading angle by detecting the polarization patterns of skylight and overcome many inherent defects of the conventional navigation methods. This paper develops a real-time bionic polarized skylight sensor. In order to eliminate the sensor’s hardware errors, an indoor calibration experiment is conducted. We also propose an image processing method to enhance the sensor’s robustness in the urban environment. The comparative experiment shows that both calibration experiment and image processing algorithm can achieve good effects.
In fringe projection profilometry, phase unwrapping has long been a critical issue. Unwrapping methods can be classified as spatial unwrapping methods and temporal unwrapping methods. The spatial unwrapping method applies only to the surface of continuous objects. The temporal unwrapping method is more widely used and can be used on discontinuous or isolated objects. Nevertheless, the temporal unwrapping methods are suffering from time-consuming, such as the large number of pictures required (standard temporal unwrapping method, phase shift plus Gray code method), and high algorithm complexity (such as periodic encoding method, Fourier transform Method) Etc. Therefore, we propose a temporal unwrapping method using only three projection patterns. In this method, two linear gray scale increasing and decreasing pictures are used to obtain the cores global phase map and uniform illumination background. Another sine fringe image and the above uniformly illuminated background image are used to obtain the wrapped phase. Then the absolute phase can be achieved with the coarse global phase distribution and the wrapped phase. Experimental results prove that this method can measure three-dimensional scenes containing isolated objects.
Imaging through scattering layers plays an important role in the field of optical imaging. Because of its characteristics, we can observe some targets that are invisible or unobservable. Now, it is a simple and effective way to process images of scattering layers by autocorrelation. However, due to the memory effect and the limitation of the acquisition environment, imaging through scattering layers still lacks the ability to accurately detect unknown objects. In this paper, we analyzed the influence of memory effects and actual acquisition environment on speckle correlations imaging. By controlling the various variables of the experimental device and the image processing, different experimental images and restoration results of the images are obtained. The memory effects control the optical thickness of the scattering layer, the size of the target, and the distance from the target to the scattering layer. There must be appropriate experimental parameter settings to meet the memory effect requirements. In addition, the selection of the position of the image acquisition device determines the degree of dispersion of the speckle. Image processing is mainly for the filtering of space domain and frequency domain, and for changes in constraints in Hybrid Input-Output algorithms. Finally, comparing the influence of all the parameters on the final restored image, the reasonable acquisition scheme and image processing scheme for different targets and scattering media can be obtained. It has reference and guiding significance for the application of imaging through scattering layers via speckle correlations.
Advanced professional courses (APCs) in the senior year will lay the foundation for further graduate study. Meanwhile, they are summaries and applications of the learnt fundamental professional courses (FPCs). Thus APCs form a connecting link between the preceding and the following studies. For example, Principles and Design of Optoelectronic Instruments (PDOI) is a lecture-based APC aiming at familiarizing students with the operating principles and basic design methods of commonly used optoelectronic instruments. Students will be able to describe the operating procedure of the instruments, distinguish the structure and function of each part, and present preliminary results of both overall design and parameter design. Problem-based approach with the following implementation is a good choice for such APCs. An assignment of system design is announced as the problem at the beginning of the semester. Students are asked to (1) describe the basic working principle, (2) do the overall design and draw the schematic diagram of the system, (3) do the module devision as well as the budget, and (4) finally analyze a critical parameter of the system. Then during the explanation of corresponding chapters, four times of in-class practices are arranged to help the students finish the assignment question-by-question with the help of textbook, internet and the teacher. Compared with straightforward explanation of the chapters and leaving the assignment as a homework at last, the proposed problem-based approach helps improving the motivation and achievement of the students.
Fourier ptychography microscopy (FPM) is a recently developed computational imaging approach which surpasses the resolution barrier of a low numerical aperture (NA) imaging system. It is a powerful tool due to its ability to achieve super resolution of complex sample function, pupil aberration, LED misalignment, and beyond. However, recent studies have focused more on the optimization algorithms and set-ups instead of its theoretical background. Although some imaging laws about FPM have already been set forth, the formulas and laws are not fully defined, and the connection between diffraction theory and Fourier optics has a gap. Therefore, there exist a need for comprehensive research on physical and mathematical basis of FPM for future applications. Keeping this goal in mind, this manuscript utilizes scalar field diffraction theory to rigorously study the relationship between wavelength, the propagation mode, illumination direction of the incident wave, sample structure information and the direction of the output wave. The theoretical analysis of diffraction imaging in FPM provides a clear physical basis for not only the FPM systems, but also for the ptychography iterative engine (PIE) and any other coherent diffraction imaging techniques and systems. It can help to find the source of noise and therefore improve image quality in FPM technique and systems.
Image enhancement technique is utilized to emphasize the overall or local characteristics of pictures and widely used in aerospace, and machine vision application. However, most of these techniques are mathematical algorithms based on captured pictures instead of the imaging process. Fourier ptychographic microscopy (FPM) is a recently developed computational imaging approach which stitches together low-resolution images acquired under different angles of illumination with the same intensity in Fourier space to produce a wide-field, high-resolution complex sample image. In this article, a theoretical model about the illumination intensity is proposed. The effect of uneven illumination intensity can be reduced significantly based on our model. Furthermore, the quality of the reconstructed image can be enhanced by adjusting the intensity of the illumination light corresponding to the high frequency components of the original spectrum.