Introduction to mathematical morphology
Computer Vision, Graphics, and Image Processing
A survey of free-form object representation and recognition techniques
Computer Vision and Image Understanding
3D Model Retrieval with Spherical Harmonics and Moments
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
Shape classification using smooth principal components
Pattern Recognition Letters
Morphological scale-space with application to three-dimensional object recognition
Morphological scale-space with application to three-dimensional object recognition
A mathematical morphology approach to image based 3D particle shape analysis
Machine Vision and Applications
Feature-based similarity search in 3D object databases
ACM Computing Surveys (CSUR)
Multimedia Tools and Applications
A survey of content based 3D shape retrieval methods
Multimedia Tools and Applications
TransforMesh: a topology-adaptive mesh-based approach to surface evolution
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Using mathematical morphology for similarity search of 3D objects
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
On volume distribution features based 3d model retrieval
ICAT'06 Proceedings of the 16th international conference on Advances in Artificial Reality and Tele-Existence
SHREC'10 track: large scale retrieval
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
Cortical 3D Face and Object Recognition Using 2D Projections
International Journal of Creative Interfaces and Computer Graphics
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In this paper we present a method for retrieving 3D polygonal objects by using two sets of multiresolution signatures. Both sets are based on the progressive elimination of object's details by iterative processing of the 3D meshes. The first set, with five parameters, is based on mesh smoothing. This mainly affects an object's surface. The second set, with three parameters, is based on difference volumes after successive mesh erosions and dilations. Characteristic feature vectors are constructed by combining the features at three mesh resolutions of each object. In addition to being invariant to mesh resolution, the feature vectors are invariant to translation, rotation and size of the objects. The method was tested on a set of 40 complex objects with mesh resolutions different from those used in constructing the feature vectors. By using all eight features, the average ranking rate obtained was 1.075: 37 objects were ranked first and only 3 objects were ranked second. Additional tests were carried out to determine the significance of individual features and all combinations. The same ranking rate of 1.075 can be obtained by using some combinations of only three features.