Exploring local regularities for 3D object recognition

In order to find better simplicity measurements for 3D object recognition, a new set of local regularities is developed and tested in a stepwise 3D reconstruction method, including localized minimizing standard deviation of angles(L-MSDA), localized minimizing standard deviation of segment magnitudes(L-MSDSM), localized minimum standard deviation of areas of child faces (L-MSDAF), localized minimum sum of segment magnitudes of common edges (L-MSSM), and localized minimum sum of areas of child face (L-MSAF). Based on their effectiveness measurements in terms of form and size distortions, it is found that when two local regularities: L-MSDA and L-MSDSM are combined together, they can produce better performance. In addition, the best weightings for them to work together are identified as 10% for L-MSDSM and 90% for L-MSDA. The test results show that the combined usage of L-MSDA and L-MSDSM with identified weightings has a potential to be applied in other optimization based 3D recognition methods to improve their efficacy and robustness

On the algorithm for reconstruction of polyhedral objects from a single line drawing

This paper presents a new level-by-level 3D reconstruction method from a single axonometric wireframe drawing with or without hidden edges of a planar object. This method solves a 3D reconstruction problem in stepwise fashion via face propagation, and allows a detailed relative importance study of key regularities and their usages for reconstruction rules establishment, which is the focus of this paper. Based on the proposed reconstruction method, two new trustfulness measures in terms of form and size distortions have been devised to evaluate regularities and their limitations. As a result of adapting the existing concepts of the MSDA [5] and the MSDSM [9] into a stepwise system, two new localized regularities have been developed in terms of the localized minimizing standard deviation of angles (L-MSDA) and the localized minimizing standard deviation of segment magnitudes (L-MSDSM). The proposed method and the identified key regularity have been tested with many cases. A range of weightings for the combination of L-MSAD and L-MSDSM regularities have been identified for practical use. The test results show that (1) the level-by-level reconstruction method is useful to 3D reconstruction, (2) the combined usage of localized key regularities L-MSDA and L-MSDSM, can produce satisfactory results with the proposed method, and (3) the demonstrated success of the combined usage of L-MSDA and L-MSDSM suggests that the concept of combining MSDA and MSDSM should be utilized in any optimization-based reconstruction approaches to improve their accuracy

Hyperspectral Imaging for the Nondestructive Quality Assessment of the Firmness of Nanguo Pears Under Different Freezing/Thawing Conditions

Firmness changes in Nanguo pears under different freezing/thawing conditions have been characterized by hyperspectral imaging (HSI). Four different freezing/thawing conditions (the critical temperatures, numbers of cycles, holding time and cooling rates) were set in this experiment. Four different pretreatment methods were used: multivariate scattering correction (MSC), standard normal variate (SNV), Savitzky-Golay standard normal variate (S-G-SNV) and Savitzky-Golay multiplicative scattering correction (S-G-MSC). Combined with competitive adaptive reweighted sampling (CARS) to identify characteristic wavelengths, firmness prediction models of Nanguo pears under different freezing/thawing conditions were established by partial least squares (PLS) regression. The performance of the firmness model was analyzed quantitatively by the correlation coefficient (R), the root mean square error of calibration (RMSEC), the root mean square error of prediction (RMSEP) and the root mean square error of cross validation (RMSECV). The results showed that the MSC-PLS model has the highest accuracy at different cooling rates and holding times; the correlation coefficients of the calibration set (Rc) were 0.899 and 0.927, respectively, and the correlation coefficients of the validation set (Rp) were 0.911 and 0.948, respectively. The accuracy of the SNV-PLS model was the highest at different numbers of cycles, and the Rc and the Rp were 0.861 and 0.848, respectively. The RMSEC was 65.189, and the RMSEP was 65.404. The accuracy of the S-G-SNV-PLS model was the highest at different critical temperatures, with Rc and Rp values of 0.854 and 0.819, respectively, and RMSEC and RMSEP values of 74.567 and 79.158, respectively