In this article, we investigate the use of joint a-entropy for 3D ear matching by incorporating the local shape feature of 3D ears into the joint a-entropy. First, we extract a sut^cient number of key points from the ...In this article, we investigate the use of joint a-entropy for 3D ear matching by incorporating the local shape feature of 3D ears into the joint a-entropy. First, we extract a sut^cient number of key points from the 3D ear point cloud, and fit the neighborhood of each key point to a single-value quadric surface on product parameter regions. Second, we define the local shape feature vector of each key point as the sampling depth set on the parametric node of the quadric surface. Third, for every pair of gallery ear and probe ear, we construct the minimum spanning tree （MST） on their matched key points. Finally, we minimize the total edge weight of MST to estimate its joint a-entropy the smaller the entropy is, the more similar the ear pair is. We present several examples to demonstrate the advantages of our algorithm, including low time complexity, high recognition rate, and high robustness. To the best of our knowledge, it is the first time that, in computer graphics, the classical information theory of joint a-entropy is used to deal with 3D ear shape recognition.展开更多
Ear morphological traits such as volume and shape are important features of maize and the quantitative associations among them can help understand kernel yield determination. 150 mature ears each of 4 maize cultivars ...Ear morphological traits such as volume and shape are important features of maize and the quantitative associations among them can help understand kernel yield determination. 150 mature ears each of 4 maize cultivars were collected from field experiments, and ear length(L), diameter(D), area(S) and volume(V) were recorded for individual ears, kernel weight per ear also recorded for a portion of the examined ears. Following principles of dimensional analysis, 8 theoretical equations of 3 sets,which relate ear higher dimensions to its length and diameter, were developed and parameterized and validated with the field observations. The 3 optimized equations showed that the shape of ears in maize can be featured with 3 dimensionless form factors, namely diameter-to-length ratio(c=D/L), areal form factor(b=S/L/D), and volumetric form factor(a=V/L/D/D). Statistically,all of them were significantly different among cultivars, and a's values varied from 0.582 to 0.612, and b's 0.839-0.868, and c's 0.242-0.308. Volumetric form factor and areal form factor could estimate precisely ear volume and area respectively, but diameter-to-length ratio was not suitable to estimate ear diameter by its length. Ear volume explained almost all variation of ear kernel weight and product L*D*D did the same substantially. Dimensional analysis proved to be promising in understanding relationship among morphological traits of ears in maize. Its application in crop researches should improve our knowledge of the physical properties of crop plants.展开更多
Association mapping has emerged as a new tool to elucidate complex quantitative trait loci in maize, but there are few reports about systematic association analysis for the specific SSR markers with agronomic traits o...Association mapping has emerged as a new tool to elucidate complex quantitative trait loci in maize, but there are few reports about systematic association analysis for the specific SSR markers with agronomic traits of interest in China. We investigated the morphological and genetic diversity and population structure for 76 maize recombinant inbred lines, and then association analysis were further performed between 48 simple sequence repeat loci and 17 morphological traits, consisting of nine ear-related traits and eight other traits. The 48 SSR markers were screened out and further classified into two groups including a group of loci in regions harboring reported quantitative trait loci that affect ear shape and a group of markers distributing on the whole genome randomly. The result indicated that the population of recombinant inbred lines was structured, showing five subpopulations. Our association results revealed that there were 82, 59, and 40 significant associations detected by K-test, logistic regression, and both analysis, respectively. When the 17 traits were considered separately, the significant associations between Q-SSRs and E-traits were raised to 27.8%, whereas the other groups of combinations ranged between 2.3 and 6.3%. As the proportion of significant associations is higher among the Q-SSR subset of markers and the subset of traits related to ear shape than those for all of the other combinations, we conclude that this approach is valid for establishing true positive marker-trait relationships. Our results also demonstrated that association mapping could complement and enhance previous QTL information for marker-assisted selection.展开更多
基金It was supported in part by the National Natural Science Foundation of China under Grant Nos. 61472170, 61170143, 60873110, and Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia under Grant No. ITSM201301. Acknowledgement The work presented in this paper was done during Xiao-Peng Sun＇s visit at the graphics group of Michigan State University. Thank University of North Dakota for the biometrics database, thank Dr. Yi-Ying Tong for helpful discussions and review, and thank the reviewers of CVM2015 for constructive comments.
文摘In this article, we investigate the use of joint a-entropy for 3D ear matching by incorporating the local shape feature of 3D ears into the joint a-entropy. First, we extract a sut^cient number of key points from the 3D ear point cloud, and fit the neighborhood of each key point to a single-value quadric surface on product parameter regions. Second, we define the local shape feature vector of each key point as the sampling depth set on the parametric node of the quadric surface. Third, for every pair of gallery ear and probe ear, we construct the minimum spanning tree （MST） on their matched key points. Finally, we minimize the total edge weight of MST to estimate its joint a-entropy the smaller the entropy is, the more similar the ear pair is. We present several examples to demonstrate the advantages of our algorithm, including low time complexity, high recognition rate, and high robustness. To the best of our knowledge, it is the first time that, in computer graphics, the classical information theory of joint a-entropy is used to deal with 3D ear shape recognition.
基金Supported by the National Natural Science Foundation of China(31271658)National Key Research and Development Program of China(2016YFD0300306)
文摘Ear morphological traits such as volume and shape are important features of maize and the quantitative associations among them can help understand kernel yield determination. 150 mature ears each of 4 maize cultivars were collected from field experiments, and ear length(L), diameter(D), area(S) and volume(V) were recorded for individual ears, kernel weight per ear also recorded for a portion of the examined ears. Following principles of dimensional analysis, 8 theoretical equations of 3 sets,which relate ear higher dimensions to its length and diameter, were developed and parameterized and validated with the field observations. The 3 optimized equations showed that the shape of ears in maize can be featured with 3 dimensionless form factors, namely diameter-to-length ratio(c=D/L), areal form factor(b=S/L/D), and volumetric form factor(a=V/L/D/D). Statistically,all of them were significantly different among cultivars, and a's values varied from 0.582 to 0.612, and b's 0.839-0.868, and c's 0.242-0.308. Volumetric form factor and areal form factor could estimate precisely ear volume and area respectively, but diameter-to-length ratio was not suitable to estimate ear diameter by its length. Ear volume explained almost all variation of ear kernel weight and product L*D*D did the same substantially. Dimensional analysis proved to be promising in understanding relationship among morphological traits of ears in maize. Its application in crop researches should improve our knowledge of the physical properties of crop plants.
基金supported by the National Key Technologies R&D Program of China during the 11th Five-Year Plan period of Hebei Province (06220108D-2)
文摘Association mapping has emerged as a new tool to elucidate complex quantitative trait loci in maize, but there are few reports about systematic association analysis for the specific SSR markers with agronomic traits of interest in China. We investigated the morphological and genetic diversity and population structure for 76 maize recombinant inbred lines, and then association analysis were further performed between 48 simple sequence repeat loci and 17 morphological traits, consisting of nine ear-related traits and eight other traits. The 48 SSR markers were screened out and further classified into two groups including a group of loci in regions harboring reported quantitative trait loci that affect ear shape and a group of markers distributing on the whole genome randomly. The result indicated that the population of recombinant inbred lines was structured, showing five subpopulations. Our association results revealed that there were 82, 59, and 40 significant associations detected by K-test, logistic regression, and both analysis, respectively. When the 17 traits were considered separately, the significant associations between Q-SSRs and E-traits were raised to 27.8%, whereas the other groups of combinations ranged between 2.3 and 6.3%. As the proportion of significant associations is higher among the Q-SSR subset of markers and the subset of traits related to ear shape than those for all of the other combinations, we conclude that this approach is valid for establishing true positive marker-trait relationships. Our results also demonstrated that association mapping could complement and enhance previous QTL information for marker-assisted selection.
