Geodesic Object Representation and Recognition

Hamza, A. Ben; Krim, Hamid

doi:10.1007/978-3-540-39966-7_36

Cited by 65 publications

(57 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Hamza and Krim [20] applied geodesic distance using shape distributions ( [35]) for 3D shape classification. Zhao and Davis [48] used the color information along the shortest path within a human silhouette.…”

Section: Geodesic Distances For 3d Surfacesmentioning

confidence: 99%

Shape Classification Using the Inner-Distance

Ling

Jacobs

2007

IEEE Trans. Pattern Anal. Mach. Intell.

1,025

920

View full text Add to dashboard Cite

Abstract-Part structure and articulation are of fundamental importance in computer and human vision. We propose using the innerdistance to build shape descriptors that are robust to articulation and capture part structure. The inner-distance is defined as the length of the shortest path between landmark points within the shape silhouette. We show that it is articulation insensitive and more effective at capturing part structures than the Euclidean distance. This suggests that the inner-distance can be used as a replacement for the Euclidean distance to build more accurate descriptors for complex shapes, especially for those with articulated parts. In addition, texture information along the shortest path can be used to further improve shape classification. With this idea, we propose three approaches to using the inner-distance. The first method combines the inner-distance and multidimensional scaling (MDS) to build articulation invariant signatures for articulated shapes. The second method uses the inner-distance to build a new shape descriptor based on shape contexts. The third one extends the second one by considering the texture information along shortest paths. The proposed approaches have been tested on a variety of shape databases, including an articulated shape data set, MPEG7 CE-Shape-1, Kimia silhouettes, the ETH-80 data set, two leaf data sets, and a human motion silhouette data set. In all the experiments, our methods demonstrate effective performance compared with other algorithms.

show abstract

Section: Geodesic Distances For 3d Surfacesmentioning

confidence: 99%

Shape Classification Using the Inner-Distance

Ling

Jacobs

2007

IEEE Trans. Pattern Anal. Mach. Intell.

1,025

920

View full text Add to dashboard Cite

show abstract

“…These allow intrinsic geometric information to be captured by low dimensional signatures. An elegant example of this is the geodesic shape distribution of [20] where information theoretic measures are used to compare probability distributions representing 3-D object surfaces. In the domain of graph theory there have also been attempts to address the problem of 3-D shape matching using representations based on Reeb graphs [21,48].…”

Section: (Bottom Row)mentioning

confidence: 99%

Retrieving articulated 3-D models using medial surfaces

Siddiqi

Zhang

Macrini

et al. 2007

Machine Vision and Applications

238

135

View full text Add to dashboard Cite

We consider the use of medial surfaces to represent symmetries of 3-D objects. This allows for a qualitative abstraction based on a directed acyclic graph of components and also a degree of invariance to a variety of transformations including the articulation of parts. We demonstrate the use of this representation for 3-D object model retrieval. Our formulation uses the geometric information A preliminary version of this article was published in EMMCVPR 2005. In this extended version we have included results on the significantly larger McGill Shape Benchmark, making a stronger case for the advantages of our method for models with articulating parts. We have also included expanded introduction, medial surface computation, matching, indexing, experimental results, and discussion sections, along with several new figures. associated with each node along with an eigenvalue labeling of the adjacency matrix of the subgraph rooted at that node. We present comparative retrieval results against the techniques of shape distributions (Osada et al.) and harmonic spheres (Kazhdan et al.) on 425 models from the McGill Shape Benchmark, representing 19 object classes. For objects with articulating parts, the precision vs recall curves using our method are consistently above and to the right of those of the other two techniques, demonstrating superior retrieval performance. For objects that are rigid, our method gives results that compare favorably with these methods.

show abstract

“…It was made by selecting atoms of highest electrical potential on exposed surface area and adding terms such as lipophilicity [24], which represents the hydrophobic effect [25][26][27], and geodesic distance between points of highest electrical potential on surface, which exemplifies the geometric complementarity between protein and ligand. A geodesic is a locally length-minimizing curve used for shape analysis, surface evolution and object recognition [28]. This analysis at the atomic level aims to offer new insights towards the understanding of the effectiveness of each atom in establishing hydrogen bonds on molecular surface and inspire development of novel methods to analyse small ligands.…”

Section: Introductionmentioning

confidence: 99%