De alta resolução, de alta velocidade, de imagens de vídeo tridimensional com Franja Digital Técnicas de Projeção

1. System Configuration A schematic of the system is shown in Figure 1. Generate the fringe patterns for projection. These can be prepared well in advance by using an image programming environment such as MATLAB, OpenCV, or QT. Construct the patterns according to the three-step phase-shifting algorithm in18. Make three images, shifted in phase offset from each other by 2π/3. For binary defocusing, use a dithering technique to generate sinusoidal patterns using only black and white pixels as described in19. Select the digital light processing projector. A high-speed binary defocusing system requires a faster, specialized projector such as the DLP LightCommander with ALP High Speed module. Be sure to use the binary or monochromatic setting to project the fringe images. Since the image values are purely on or off, nonlinearity adjustments are not necessary. Utilize the projector’s software program to upload the patterns for phase shifting. Select the camera. Choose a black-and-white CCD or CMOS camera with the correct capture rate for the system. Avoid color cameras for 3D capture since color is not needed and color cameras require nonlinearity and gamma adjustments. Keep in mind that the camera will need to capture the entire set of fringe images for each 3D video frame. High-quality sinusoidal systems require precise synchronization between the projector and the camera; for binary defocusing systems this requirement is more relaxed20. Determine the desired maximum (x, y) range and the distance from the projector to the object (d0). Choose a range that makes sense for the application, but be sure that the area is slightly larger than the subject to reduce any optical boundary effects. Since the light output of a projector is a frustum, this (x, y) range will drive d0. Move the projector relative to a large flat projection surface until the desired (x, y) range is found, then measure d0 with a tape measure. Select the camera lens with the proper focal length. Using the camera’s sensor size, find the focal length such that the field of view at the distance d0 is the same as the desired imaging range (x, y). Determine the separation distance between the projector and the camera. A trade-off occurs here between noise and shadowing. At a large angle between these components, triangulation between feature points is more obvious, but more features get lost in shadow from the camera’s perspective. At a small angle, triangulation becomes difficult, increasing noise in the results. Typically, 10-15° is a good compromise. 2. System Calibration This reference plane calibration is the simplest and most readily accessible method of calibration for the system. Therefore, it is the best for getting started. More accurate calibration methods are available in the literature for specific sinusoidal16 and binary defocusing17 applications. For maximum accuracy, calibration should be performed just before data capture. After calibration, the camera and projector should not be displaced relative to each other. Focus/defocus the projector. Carefully defocus the projection lens until the patterns at the imaging plane resemble high-quality sinusoids. This may require an iterative process of examining the data quality (Section 4) and adjusting the lens. Capture fringe images of a reference plane. Place a flat white board at the focal plane of the projector and in the field of view of the camera. A 3/16 in (5 mm) thick white foam core board works well, provided that the surface facing the system is not shiny or significantly blemished or torn. Record and save fringe images of this board for the data processing step. Capture fringe images of a reference object of known dimensions. For this step, a rigid foam cube is one simple example. Cover the cube with squares of 1/16 in (1.5 mm) white adhesive foam to make it diffuse. Place it at the focal plane of the camera in the camera’s field of view and record fringe images for the processing step. 3. Data Acquisition Place the object or invite the subject to sit at the focal plane of the camera. For a human subject, warn him or her that the projector light may be bright. A black cloth backdrop can be used behind the subject to hide extraneous surroundings. Adjust the camera aperture to optimize the light level. Sample fringe images should be as bright as possible, but not saturated. Dark images will have too much noise, while saturated areas (significant regions of maximum brightness) in images will result in the loss of details in the saturated region. Capture the desired number of frames. Be sure to bring a hard drive large enough to hold all of the captured images for both the subject and the calibration datasets. With the .OBJ file format, a 3D video recorded at 30 Hz for 1 min at a resolution of 640 x 480 could be over 50 GB. 4. Data Analysis and Visualization With software optimized for speed such as our in-house GUI, this step can take place during data capture. Real-time processing allows the user to immediately detect if the resulting data is desirable for the application and adjust if necessary. However, post processing can be more flexible and higher in accuracy. Post processing is also much simpler to implement and the best place to begin. Compute the wrapped phase for both the calibration and subject data. In the three-step phase-shifting algorithm in18, phase describes the position of a point within the cosine function. Since we have three equations and three unknowns, we can solve the equations used to generate the fringe images in step 1.1 for the phase at each point. Because of the arctangent function, the computed phase is in the range (-π, π]; hence it is called “wrapped phase.” To improve the processing speed, we developed a fast phase wrapping algorithm that is discussed in21. Unwrap the phase maps. Adopt a phase-unwrapping algorithm that detects the 2π jumps in the phase and removes them by adding or subtracting multiples of 2π. We have used the fast algorithm in22 in previous systems to unwrap the phase robustly yet quickly. In the video, we demonstrate the multifrequency technique described in15, which uses additional sets of three phase-shifted patterns at differing frequencies. The wrapped phase maps from each set of three can be combined to robustly yield a single unwrapped phase map. This technique increases the depth range for accurate capture with binary defocusing. Optional: Compute the 2D texture. Averaging sets of three neighboring fringe images will wash out the fringe stripes and generate the 2D texture map. This can be mapped onto the 3D data during visualization if desired. Convert the unwrapped phase maps into depth. As described in23, depth can be computed for the calibration cube as the difference between the calibration cube phase map and the reference plane phase map. Compare this computed depth to the known depth to compute the correct depth scaling factor c0. Then, compute the depth for the subject by subtracting the reference plane phase from the subject’s phase and multiplying the results by c0. Compute the x- and y-coordinates. Apply the scaling factor c0 to the calibration cube depth map. Determine the conversion factor ρ from the cube dimensions in pixels to the known cube dimensions in the xy plane. Multiply the pixel count in the subject data by ρ to compute the x and y coordinates. Visualize the data. Individual frames can be saved in our in-house format and viewed with simple MATLAB code or saved in .OBJ format and viewed with a commercial 3D modeling program such as Blender. Due to the large amount of data in each frame, these applications may be sluggish on some computers. For more responsiveness or for live video display, write software using a computer graphics library such as OpenGL or Direct3D. This software can take advantage of the graphics processing unit (GPU) to rapidly generate the x, y, and z coordinates from the unwrapped phase, form a triangle mesh, compute lighting normals, and display the results. Using the GPU, we have achieved up to 30 Hz live 3D data visualization with approximately 300,000 points per frame.