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Research and Implementation of Partition-Interpolation Craniofacial Reconstruction Method Based on Knowledge Base

Author: WangMengYang
Tutor: GengGuoHua
School: Northwestern University
Course: Computer Software and Theory
Keywords: Craniofacial recovery Feature point calibration Soft tissue thickness Knowledge base Interpolation
CLC: TP391.41
Type: Master's thesis
Year: 2010
Downloads: 79
Quote: 3
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Abstract


Craniofacial recovery is only a skull information relevant domain knowledge based on reproduction process in the face of the face, the bones in the criminal investigation cases identifying the looks of the field of archeology ancient people to reproduce and have a wide range of virtual plastic surgery the application. Skull feature points at all types of soft tissue thickness the knowledge construction craniofacial recovery Knowledge Base; anthropological factors as a basis for the selection of knowledge; the selected knowledge based on a design partition interpolation method craniofacial recovery; research on subsequent craniofacial Recovery Expert System has a certain significance. The main research work are as follows: 1) sample model generation. CT (computed tomography) technology to collect a large number of live skull and craniofacial data, and data pre-processing; MC (Marching Cube) algorithm based on the CT slice skull and craniofacial three-dimensional model reconstruction. 2) feature point calibration and the measurement of the thickness of the soft tissue. Demand recovery method according to this article defines the skull and Craniofacial 58 corresponding feature points; using a manual, automatic combination calibration method taking into account the accuracy and efficiency of the feature points three-dimensional calibration; using spatial point-to-point European Euclidean distance formula feature points at rapid measurement of the thickness of the soft tissue. 3) craniofacial restoration Building knowledge base and knowledge reasoning strategy formulation. Extracted samples of soft tissue thickness information to generate instances of knowledge; instance knowledge in accordance with the classification of the type of the skull, and by calculating the instance in the same type of knowledge mean to get the skull of various types of soft tissue thickness values, that is to restore the guidance of knowledge; BNF (Backus- Naur Form) paradigm and production of knowledge in knowledge and rules; proposed based on the knowledge reasoning strategy of the index table, according to their type, pending the recovery skull select the best match recovery guidance knowledge. 4) the craniofacial recovery method. Normal - distance method by the pending recovery skull feature points and selected recovery guidance knowledge that soft tissue thickness values ??generated pending the recovery of the craniofacial feature points, and to the the craniofacial feature point data points to a head partition, partition interpolation and the overall recovery of the split. The method for ensuring the effect of recovery at the same time to improve the recovery efficiency. 5) recover SKINREC craniofacial prototype system. The results of this study integrated application to establish a the craniofacial recovery prototype system. System to achieve a sample model generation, feature point calibration, measurement of the thickness of the soft tissue, Building knowledge base, knowledge selected craniofacial model generation, three-dimensional display and other functions. The system is simple, recovery to good effect. This research was supported by the National Natural Science Foundation of China (60736008) support in craniofacial morphology and craniofacial reconstruction.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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