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RISS 인기검색어
- 검색키워드
- 저자 : Chelberg (검색결과 3 건)
- 무료
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Model-Based 3D Object Recognition Using Bayesian Indexing
Yi,June Ho,Chelberg, David M 성균관대학교 1998 학술회의지원논문목록집 Vol.1998 No.-
This research features the rapid recognition of three-dimen-sional objects, focusing on efficient indexing. A major concern in practical vision systems is how to retrieve the best matched models without exploring all possible object matches. We have employed a Bayesian framework to achieve efficient indexing of model objects. A decision-theoretic measure of the discriminatory power of a feature for a model object is defined in terms of posterior probability. Domain-specific knowledge compiled off-line from CAD model data is used in order to estimate posterior probabilities that define the discriminatory power of featuures for model objects. In order to speed up the indexing or selection of correct objects. WE generate and verify the object hypotheses for features detected in a scene in the order of the discriminatory power of these features for model objects. Based on the principles described above. we have implemented a working prototype vision system using a feature structure called an LSG (local surface group) for generating object hypotheses. Our object recognition system can employ a wide class of features for generation of object hypotheses. In order to verify an object hypothesis, we estimate the view of the hypothesized model object and render the model object for the computed view. The object hypothesis is then verified by finding additional features in the scene that match those present in the rendered image. Experimental result on synthetic and real range images show the effectiveness of the indexing scheme. ⓒ 1998 Acadcraic Prcess
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An Invariant Performance Measure for Surface Reconstruction Using the Volume Between Two Surfaces
Yi,June Ho,Chelberg, David M 성균관대학교 1998 학술회의지원논문목록집 Vol.1998 No.-
In this paper, we propose the volume between two surfaces normalized by the surface area (interpreted as average distance between two surfaces) as an invariant quantitative measure for comparing surface reconstruction results of the explicit form, z(x, y). The invariant property of the volume quantity provides the same measure with respect to an arbitrary coordinate system. By normalizing the volume by the surface area, the values of the measure can be compared for different size of images. We also present a novel computationally simple and efficient way of computing the volume between two surfaces and the surface area using a least-squared-error plane appoximation of a surface patch defined over a restangular grid. Experiments indicate that the method gives equivalent performance as other more complicated and computationally expensive methods. The advantages of this new method are that computation is simple and efficient.
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Jacob Frazier,Jae Yom,Cheryl Howe,David Chelberg 대한운동학회 2018 아시아 운동학 학술지 Vol.20 No.3
[OBJECTIVES] Athletic performance and injury prevention are important for athletes and coaches. Different types of movement analyses have been created to aid in injury prevention and performance. The reliability of the Microsoft Kinect™ for movement analysis has not been widely tested. If reliable and accurate it could decrease the cost and time necessary for movement analysis. The purpose of this study was to determine if the Microsoft Kinect™ is an accurate measure of knee displacement during the parallel squat when compared to the Dartfish Team Pro Software 6.0. [METHODS] Twenty nine healthy recreational athletes participated in the study and used the Dartfish Team Pro Software 6.0 to validate the Microsoft Kinect™ as a tool to measure knee displacement. Subjects performed a parallel squat with a 2.1m long dowel rod. This exercise was used to compare value between systems. The intraclass correlation coefficient and paired-samples t-test were used to compare Dartfish Team Pro Software 6.0 and Microsoft Kinect™. Intrarater reliability of each system was also assessed. [RESULTS] There were 29 participants in the study. The interclass correlation coefficient for Dartfish Team Pro Software 6.0 and Microsoft Kinect™ showed that the Microsoft Kinect™ had a high-reliability ICC = 0.96. Intrarater reliability for Kinect™ and Dartfish were .98 and .99, respectively. The mean difference between systems for measured knee displacement was 1.06 cm. The mean for the Microsoft Kinect™ was 49.11 ± 1.9 and 50.16 ± 96 for the Dartfish (p > 0.05). [CONCLUSIONS] The Microsoft Kinect™ is reliable against the Dartfish Team Pro Software 6.0 as a tool to measure knee displacement using the parallel squat. It appears for healthy young adults, the Microsoft Kinect™ is reliable for movement analysis.
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