Content-based Three-dimensional Engineering Shape Search
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Pattern Recognition
Adaptive supervision of moving objects for mobile robotics applications
Robotics and Autonomous Systems
Three-dimensional shape searching: state-of-the-art review and future trends
Computer-Aided Design
A new method for the registration of three-dimensional point-sets: The Gaussian Fields framework
Image and Vision Computing
Zernike velocity moments for sequence-based description of moving features
Image and Vision Computing
A discriminative 3D wavelet-based descriptors: Application to the recognition of human body postures
Pattern Recognition Letters
Projective invariants of co-moments of 2D images
Pattern Recognition
Matching 2D and 3D articulated shapes using the eccentricity transform
Computer Vision and Image Understanding
Combining 2d and 3d features to classify protein mutants in hela cells
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Determination of 3-D object orientation from projections
Pattern Recognition Letters
Clinical experience sharing by similar case retrieval
Proceedings of the 1st ACM international workshop on Multimedia indexing and information retrieval for healthcare
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Recognition of three-dimensional objects independent of size, position, and orientation is an important and difficult problem of scene analysis. The use of three-dimensional moment invariants is proposed as a solution. The generalization of the results of two-dimensional moment invariants which had linked two-dimensional moments to binary quantics is done by linking three-dimensional moments to ternary quantics. The existence and number of nth order moments in two and three dimensions is explored. Algebraic invariants of several ternary forms under different orthogonal transformations are derived by using the invariant property of coefficients of ternary forms. The result is a set of three-dimensional moment invariants which are invariant under size, orientation, and position change. This property is highly significant in compressing the data which are needed in three-dimensional object recognition. Empirical examples are also given.