To improve the accuracy of fringe projection profilometry for a single deformed pattern, an instantaneous phase retrieval method is proposed using a wavelet ridge section and an adaptive bandpass filter based on dyadic wavelets. First, we present a concept and assumption named wavelet ridge section to depict the instantaneous phases of the deformed fringe signals, which are also degraded by noise, and then introduce a formula to calculate the width of the wavelet ridge section. Furthermore, an adaptive bandpass filter is designed for extracting the wavelet subsignals corresponding to the wavelet ridge sections to reconstruct the analytical signal. Finally, the instantaneous phase of the distorted fringe pattern is effectively retrieved. All parameters of our method are designed to be adaptive for different fringe patterns. Our experiments indicate that the proposed method is effective for measurements and outperforms other existing mainstream wavelet transform profilometry techniques, not only in accuracy but also in noise suppression performance.
A particle-inspired Monte Carlo tree estimation method is proposed to avoid repeating similar simulation and handle the
depletion problem in particle filter. Under the inspiration of particles, the method divides the state-space recursively in a
top-down manner to form a tree structure that each node in the tree is corresponding to a sub-space. Particles are
allocated to the corresponding terminal node during the procedure. Certain size of minimal sub-space or piece is
specified to terminate the dividing. Each piece is corresponding to a leaf-node of the tree structure and the prediction
probability density in it is approximated by the proportion of its particles in total particles. Instead of importance
sampling for each particle, the method takes uniformly random measurements to compute the posterior probability
density in each piece. As a result, the method is applied to growth model and has better performance in high SNR
environments compared with the Sampling Importance Resampling method.
Modeling the three-dimensional (3-D) shape of plant stems is important in the study of plant growth in precision agriculture. To construct a 3-D model of real plant stems from images quickly, a novel volumetric method based on line-based models is proposed. Line-based models are constructed on the coarse 3-D skeleton of the plant stems, then carved with respect to silhouette consistency. The surface points on the plant stems are calculated from line-based models. Finally, a mesh surface model can be extracted from the surface points. The proposed method can give precise results together with low time complexity and space complexity. Experiments based on both synthetic and real data are presented to evaluate the speed and preciseness of the proposed method.
3D reconstruction is one of main techniques for computer vision. A novel 3D convex surface reconstruction
method is presented in this paper, which is based on visual hull principle. The real object is supposed to be surrounded
by a 3D grid bounding box filled with voxels. A series of images of the object are captured by a calibrated camera in
different locations. For each image, a series of virtual rays, each of which starts from an image silhouette point and drills
through the camera center, intersect the voxels in bounding box to obtain a number of potential object surface points.
Afterward, the potential surface points are projected onto the other images to eliminate the pseudo surface points which
must locate outside the object image area in at least one image. When all images are processed, the surface points of
whole object are obtained and then 3D surface is reconstructed. The experiment illuminates the feasibility and validity
of our approach.
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