3D object classification using salient point patterns with application to craniofacial research

Atmosukarto, Indriyati; Wilamowska, Katarzyna; Heike, Carrie L.; Shapiro, Linda G.

doi:10.1016/j.patcog.2009.11.004

Cited by 20 publications

(16 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first comparison is to a previous work of representing the whole face using a global saliency map [2]. The second comparison is to a global approach of representing the whole face, instead of only a specific facial region, using a 2D histogram of azimuth-elevation angles [1].…”

Section: Resultsmentioning

confidence: 99%

“…Our methodology for representing 3D facial shape uses 2D histograms of the azimuth and elevation angles of surface normal vectors of the 3D points in the region [2]. Figure 2(a) shows the selected midface region of an individual in the dataset, while Figure 2(b) shows the 8 × 8 2D histogram of the region displayed on a color map.…”

Section: Methodsmentioning

confidence: 99%

“…Genetic programming (GP) is used to learn the different 3D shape quantifications. Experimental results show that the GP method achieves a higher classification rate than those of human experts and existing computer algorithms [1], [2]. …”

mentioning

confidence: 99%

See 2 more Smart Citations

The Use of Genetic Programming for Learning 3D Craniofacial Shape Quantifications

Atmosukarto

Shapiro

Heike

2010

2010 20th International Conference on Pattern Recognition

Self Cite

View full text Add to dashboard Cite

Craniofacial disorders commonly result in various head shape dysmorphologies. The goal of this work is to quantify the various 3D shape variations that manifest in the different facial abnormalities in individuals with a craniofacial disorder called 22q11.2 Deletion Syndrome. Genetic programming (GP) is used to learn the different 3D shape quantifications. Experimental results show that the GP method achieves a higher classification rate than those of human experts and existing computer algorithms [1], [2].

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

The Use of Genetic Programming for Learning 3D Craniofacial Shape Quantifications

Atmosukarto

Shapiro

Heike

2010

2010 20th International Conference on Pattern Recognition

Self Cite

View full text Add to dashboard Cite

show abstract

“…This can be performed without relying on additional information for searching, only using the provided shapes. Additionally, many fields (for example medicine [1,2], CAD/ CAM [3], etc.) have benefited from the large amount of approaches proposed to overcome the problem of 3D matching.…”

Section: Introductionmentioning

confidence: 99%

Data-aware 3D partitioning for generic shape retrieval

Sipiran¹,

Bustos²,

Schreck³

2013

Computers & Graphics

View full text Add to dashboard Cite

Artículo de publicación ISIIn this paper, we present a new approach for generic 3D shape retrieval based on a mesh partitioning scheme. Our method combines a mesh global description and mesh partition descriptions to represent a 3D shape. The partitioning is useful because it helps us to extract additional information in a more local sense. Thus, part descriptions can mitigate the semantic gap imposed by global description methods. We propose to find spatial agglomerations of local features to generate mesh partitions. Hence, the definition of a distance function is stated as an optimization problem to find the best match between two shape representations. We show that mesh partitions are representative and therefore it helps to improve the effectiveness in retrieval tasks. We present exhaustive experimentation using the SHREC'09 Generic Shape Retrieval Benchmark. (C) 2013 Elsevier Ltd. All rights reserved

show abstract

“…Shape descriptors have been used in a range of image processing applications ranging from shape classification [4] to medical applications [5].…”

Section: Introductionmentioning

confidence: 99%

Minkowski compactness measure

Martinez-Ortiz

Everson

2013

2013 13th UK Workshop on Computational Intelligence (UKCI)

View full text Add to dashboard Cite

Abstract-Many compactness measures are available in the literature. In this paper we present a generalised compactness measure Cq(S) which unifies previously existing definitions of compactness. The new measure is based on Minkowski distances and incorporates a parameter q which modifies the behaviour of the compactness measure. Different shapes are considered to be most compact depending on the value of q: for q = 2, the most compact shape in 2D (3D) is a circle (a sphere); for q → ∞, the most compact shape is a square (a cube); and for q = 1, the most compact shape is a square (a octahedron).For a given shape S, measure Cq(S) can be understood as a function of q and as such it is possible to calculate a spectum of Cq(S) for a range of q. This produces a particular compactness signature for the shape S, which provides additional shape information.The experiments section of this paper provides illustrative examples where measure Cq(S) is applied to various shapes and describes how measure and its spectrum can be used for image processing applications.

show abstract

3D object classification using salient point patterns with application to craniofacial research

Cited by 20 publications

References 38 publications

The Use of Genetic Programming for Learning 3D Craniofacial Shape Quantifications

The Use of Genetic Programming for Learning 3D Craniofacial Shape Quantifications

Data-aware 3D partitioning for generic shape retrieval

Minkowski compactness measure

Contact Info

Product

Resources

About