| Title |
Fast 2D/3D object representation with growing neural gas
|
|---|---|
| Published in |
Neural Computing and Applications, September 2016
|
| DOI | 10.1007/s00521-016-2579-y |
| Pubmed ID | |
| Authors |
Anastassia Angelopoulou, Jose Garcia Rodriguez, Sergio Orts-Escolano, Gaurav Gupta, Alexandra Psarrou |
| Abstract |
This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 18 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 4 | 22% |
| Professor > Associate Professor | 3 | 17% |
| Student > Doctoral Student | 2 | 11% |
| Student > Master | 2 | 11% |
| Student > Ph. D. Student | 1 | 6% |
| Other | 2 | 11% |
| Unknown | 4 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 10 | 56% |
| Engineering | 3 | 17% |
| Neuroscience | 1 | 6% |
| Unknown | 4 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 April 2018.
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#19,237,853
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Outputs from Neural Computing and Applications
#1,007
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Outputs of similar age
#247,330
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Outputs of similar age from Neural Computing and Applications
#7
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