|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM328961795 |
| 003 |
DE-627 |
| 005 |
20231225204053.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231225s2021 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TIP.2021.3101398
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1096.xml
|
| 035 |
|
|
|a (DE-627)NLM328961795
|
| 035 |
|
|
|a (NLM)34351859
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Guzman-Pando, Abimael
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a DeepFoveaNet
|b Deep Fovea Eagle-Eye Bioinspired Model to Detect Moving Objects
|
| 264 |
|
1 |
|c 2021
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 07.10.2021
|
| 500 |
|
|
|a Date Revised 07.10.2021
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status MEDLINE
|
| 520 |
|
|
|a Birds of prey especially eagles and hawks have a visual acuity two to five times better than humans. Among the peculiar characteristics of their biological vision are that they have two types of foveae; one shallow fovea used in their binocular vision, and a deep fovea for monocular vision. The deep fovea allows these birds to see objects at long distances and to identify them as possible prey. Inspired by the biological functioning of the deep fovea a model called DeepFoveaNet is proposed in this paper. DeepFoveaNet is a convolutional neural network model to detect moving objects in video sequences. DeepFoveaNet emulates the monocular vision of birds of prey through two Encoder-Decoder convolutional neural network modules. This model combines the capacity of magnification of the deep fovea and the context information of the peripheral vision. Unlike algorithms to detect moving objects, ranked in the first places of the Change Detection database (CDnet14), DeepFoveaNet does not depend on previously trained neural networks, neither on a huge number of training images for its training. Besides, its architecture allows it to learn spatiotemporal information of the video. DeepFoveaNet was evaluated in the CDnet14 database achieving high performance and was ranked as one of the ten best algorithms. The characteristics and results of DeepFoveaNet demonstrated that the model is comparable to the state-of-the-art algorithms to detect moving objects, and it can detect very small moving objects through its deep fovea model that other algorithms cannot detect
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Chacon-Murguia, Mario I
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 30(2021) vom: 01., Seite 7090-7100
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
|
| 773 |
1 |
8 |
|g volume:30
|g year:2021
|g day:01
|g pages:7090-7100
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2021.3101398
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 30
|j 2021
|b 01
|h 7090-7100
|