Name
Title | ||
|---|---|---|
Real-Time Indoor Scene Description for the Visually Impaired Using Autoencoder Fusion Strategies with Visible Cameras. |
Abstract | ||
|---|---|---|
This paper describes three coarse image description strategies, which are meant to promote a rough perception of surrounding objects for visually impaired individuals, with application to indoor spaces. The described algorithms operate on images (grabbed by the user, by means of a chest-mounted camera), and provide in output a list of objects that likely exist in his context across the indoor scene. In this regard, first, different colour, texture, and shape-based feature extractors are generated, followed by a feature learning step by means of AutoEncoder (AE) models. Second, the produced features are fused and fed into a multilabel classifier in order to list the potential objects. The conducted experiments point out that fusing a set of AE-learned features scores higher classification rates with respect to using the features individually. Furthermore, with respect to reference works, our method: (i) yields higher classification accuracies, and (ii) runs (at least four times) faster, which enables a potential full real-time application. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3390/s17112641 | SENSORS |
Keywords | Field | DocType |
assistive technologies,visible cameras,visually impaired (VI) people,coarse scene description,multiobject recognition,deep learning,feature fusion,image representation | Computer vision,Image description,Autoencoder,Computer science,Image representation,Fusion,Artificial intelligence,Deep learning,Classifier (linguistics),Perception,Feature learning | Journal |
Volume | Issue | ISSN |
17 | 11.0 | 1424-8220 |
Citations | PageRank | References |
1 | 0.37 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Salim Malek | 1 | 40 | 3.98 |
| Farid Melgani | 2 | 1100 | 80.98 |
| Mohamed Lamine Mekhalfi | 3 | 62 | 8.01 |
| Yakoub Bazi | 4 | 672 | 43.66 |