scholarly journals Real-time image classification using LBP and ensembles of ELM

2016 ◽  
Vol 8 (1) ◽  
pp. 101-109 ◽  
Author(s):  
Stevica Cvetkovic ◽  
Boban Rajkovic ◽  
Sasa Nikolic
Keyword(s):  
Image Classification ◽  
Real Time ◽  
Real Time Image ◽  
Time Image

scholarly journals Real-Time Color Image Classification Based On Deep Learning Network

2019 ◽  
Vol 54 (5) ◽  
Author(s):  
Mohammed Hamzah Abed ◽  
Atheer Hadi Issa Al-Rammahi ◽  
Mustafa Jawad Radif
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Real Time ◽  
Color Image ◽  
Input Image ◽  
Proposed Model ◽  
Real Time Image ◽  
Deep Learning Network ◽  
Fully Connected ◽  
Time Image

Real-time image classification is one of the most challenging issues in understanding images and computer vision domain. Deep learning methods, especially Convolutional Neural Network (CNN), has increased and improved the performance of image processing and understanding. The performance of real-time image classification based on deep learning achieves good results because the training style, and features that are used and extracted from the input image. This work proposes an interesting model for real-time image classification architecture based on deep learning with fully connected layers to extract proper features. The classification is based on the hybrid GoogleNet pre-trained model. The datasets that are used in this work are 15 scene and UC Merced Land-Use datasets, used to test the proposed model. The proposed model achieved 92.4 and 98.8 as a higher accuracy.

scholarly journals Recognition of underlying surface using a convolutional neural network on a single-board computer

Informatics ◽  
2020 ◽  
Vol 17 (3) ◽  
pp. 36-43
Author(s):  
D. A. Paulenka ◽  
V. A. Kovalev ◽  
E. V. Snezhko ◽  
V. A. Liauchuk ◽  
E. I. Pechkovsky
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Real Time ◽  
Mobile Systems ◽  
Underlying Surface ◽  
Raspberry Pi ◽  
Convolutional Network ◽  
Real Time Image ◽  
Time Image

The results of the development of hardware and software system (micromodule), which detects and classifies underlying surface images of the Earth are presented. The micromodule can be installed on board of a light unmanned aerial vehicle (drone). The device has the size 5.2×7.4×3.1 cm, the weight52 g, runs on a Raspberry Pi Zero Wireless single-board microcomputer and uses a convolutional neural network based on MobileNetV2 architecture for real-time image classification. When developing the micromodule, the authors aimed to achieve a real-time image classification on inexpensive mobile equipment with low computing power so that the classification quality is  comparable  to  popular  deep  convolutional  network  architectures. The provided information could be useful for engineers and researchers who are developing compact budget mobile systems for processing, analyzing and recognition of images.

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