Role of convolutional neural networks for any real time image classification, recognition and analysis

2021 ◽  
Author(s):  
K. Kranthi Kumar ◽  
M. Dileep Kumar ◽  
Ch. Samsonu ◽  
K. Vamshi Krishna
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Real Time Image ◽  
Time Image ◽  
Classification Recognition

Real-Time Image Deblurring and Super Resolution Using Convolutional Neural Networks

2021 ◽  
pp. 381-394
Author(s):  
Nidhi Galgali ◽  
Melita Maria Pereira ◽  
N. K. Likitha ◽  
B. R. Madhushri ◽  
E. S. Vani ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Image Deblurring ◽  
Real Time Image ◽  
Time Image

scholarly journals A Homogeneous Algorithm for Motion Estimation and Compensation by Using Cellular Neural Networks

2010 ◽  
Vol 5 (5) ◽  
pp. 719 ◽  
Author(s):  
Cristian Grava ◽  
Alexandru Gacsádi ◽  
Ioan Buciu
Keyword(s):  
Neural Networks ◽  
Motion Estimation ◽  
Real Time ◽  
Cellular Neural Networks ◽  
Image Sequences ◽  
Artificial Vision ◽  
Real Time Image ◽  
Field Programmable ◽  
Real Time Applications ◽  
Time Image

In this paper we present an original implementation of a homogeneous algorithm for motion estimation and compensation in image sequences, by using Cellular Neural Networks (CNN). The CNN has been proven their efficiency in real-time image processing, because they can be implemented on a CNN chip or they can be emulated on Field Programmable Gate Array (FPGA). The motion information is obtained by using a CNN implementation of the well-known Horn & Schunck method. This information is further used in a CNN implementation of a motion-compensation method. Through our algorithm we obtain a homogeneous implementation for real-time applications in artificial vision or medical imaging. The algorithm is illustrated on some classical sequences and the results confirm the validity of our algorithm.

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.

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