Abstract
Most nonrigid motions use shape-based methods to solve the problem; however, the use of discrete cosine transform trajectory-based methods to solve the nonrigid motion problem is also very prominent. The signal undergoes discrete transformation due to the transform characteristics of the discrete cosine transform. The correlation of the data is well extracted such that a better compression of data is achieved. However, it is important to select the number and sequence of discrete cosine transform trajectory basis appropriately. The error of reconstruction and operational costs will increase for a high value of K (number of trajectory basis). On the other hand, a lower value of K would lead to the exclusion of information components. This will lead to poor accuracy as the structure of the object cannot be fully represented. When the number of trajectory basis is determined, the combination form has a considerable influence on the reconstruction algorithm. This article selects an appropriate number and combination of trajectory basis by analyzing the spectrum of re-projection errors and realizes the automatic selection of trajectory basis. Then, combining with the probability framework of normal distribution of a low-order model matrix, the energy information of the high-frequency part is retained, which not only helps maintain accuracy but also improves reconstruction efficiency. The proposed method can be used to reconstruct the three-dimensional structure of sparse data under more precise prior conditions and lower computational costs.
Highlights
Three-dimensional (3-D) reconstruction encompasses many fields such as image processing, stereoscopic vision, and biological engineering, and has attracted considerable research interest in computer vision
Shape-based 3-D reconstruction has the advantages of simplicity and convenience, but in the reconstruction process, the shape basis reconstructed for all the different sequences
The advantage of the 3-D reconstruction based on the shape trajectory is that it combines the advantages of both the trajectory basis and shape basis; the disadvantage is that the a priori unknown is added
Summary
Three-dimensional (3-D) reconstruction encompasses many fields such as image processing, stereoscopic vision, and biological engineering, and has attracted considerable research interest in computer vision. 3-D motion reconstruction involves recovering camera rotation matrix R and 3-D structure S of a nonrigid body from a given set of 2-D dynamic image sequences. The advantages of force-based 3-D reconstruction are that it is based on the deformed low-rank force space to formulate the problem, which can better explain the acquired a priori information and more accurately represent the behavior of the actual object but in the process of reconstruction, in addition to determining the force and reality. The reconstruction-based trajectory-based method solves the limitations of the above three methods, the number and type of predefined trajectory basis in this method are difficult to select. The method based on automatic selection basis-probability model proposed in this article can effectively solve the problems caused by the number of trajectory basis, combination of trajectory basis, and complex prior knowledge
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