Advanced Cellular Neural Networks Image Processing

2011 ◽  
pp. 45-50
Author(s):  
J. Álvaro Fernández
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Cellular Neural Network ◽  
Research Community ◽  
Vlsi Circuits ◽  
Anatomy And Physiology ◽  
The Core ◽  
Scientific Papers ◽  
Artificial Neural Network Ann ◽  
Cellular Computer

Since its introduction to the research community in 1988, the Cellular Neural Network (CNN) (Chua & Yang, 1988) paradigm has become a fruitful soil for engineers and physicists, producing over 1,000 published scientific papers and books in less than 20 years (Chua & Roska, 2002), mostly related to Digital Image Processing (DIP). This Artificial Neural Network (ANN) offers a remarkable ability of integrating complex computing processes into compact, real-time programmable analogic VLSI circuits as the ACE16k (Rodríguez et al., 2004) and, more recently, into FPGA devices (Perko et al., 2000). CNN is the core of the revolutionary Analogic Cellular Computer (Roska et al., 1999), a programmable system based on the so-called CNN Universal Machine (CNN-UM) (Roska & Chua, 1993). Analogic CNN computers mimic the anatomy and physiology of many sensory and processing biological organs (Chua & Roska, 2002). This article continues the review started in this Encyclopaedia under the title Basic Cellular Neural Network Image Processing.

Basic Cellular Neural Networks Image Processing

2011 ◽  
pp. 218-222
Author(s):  
J. Álvaro Fernández
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Continuous Time ◽  
Engineering Application ◽  
Cellular Neural Network ◽  
Vlsi Circuits ◽  
Ann Model ◽  
Anatomy And Physiology ◽  
Artificial Neural Network Ann ◽  
Cellular Computer

Since its seminal publication in 1988, the Cellular Neural Network (CNN) (Chua & Yang, 1988) paradigm have attracted research community’s attention, mainly because of its ability for integrating complex computing processes into compact, real-time programmable analogic VLSI circuits (Rodríguez et al., 2004). Unlike cellular automata, the CNN model hosts nonlinear processors which, from analogic array inputs, in continuous time, generate analogic array outputs using a simple, repetitive scheme controlled by just a few real-valued parameters. CNN is the core of the revolutionary Analogic Cellular Computer, a programmable system whose structure is the so-called CNN Universal Machine (CNN-UM) (Roska & Chua, 1993). Analogic CNN computers mimic the anatomy and physiology of many sensory and processing organs with the additional capability of data and program storing (Chua & Roska, 2002). This article reviews the main features of this Artificial Neural Network (ANN) model and focuses on its outstanding and more exploited engineering application: Digital Image Processing (DIP).

Experimenting with Recognition Accelerator for Pavement Distress Identification

1996 ◽  
Vol 1536 (1) ◽  
pp. 130-135 ◽  
Author(s):  
Sudhakar Nallamothu ◽  
Kelvin C. P. Wang
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Coulomb Energy ◽  
Processing Unit ◽  
Basic Principles ◽  
Pavement Distress ◽  
The Core ◽  
Artificial Neural Network Ann ◽  
Computer Board

A study was conducted using a computer board embedded with an artificial neural network (ANN) microchip for pattern recognition of pavement distress classification. The basic principles behind ANNs and pattern recognition are discussed. The hardware architecture of the Ni1000 recognition accelerator chip, which is the core of the ANN computer board, is presented, and the principle of operation of the restricted coulomb energy algorithm used in the chip is discussed. It is demonstrated that the Ni1000 Recognition Accelerator chip can be used for pattern recognition of pavement distress. Distresses in pavement images have been successfully classified using the Ni1000 recognition accelerator. The Ni1000 has the potential to be used as the core processing unit for distress classification at highway speeds.

scholarly journals An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6281
Author(s):  
Ander Zuazo ◽  
Jordi Grinyó ◽  
Vanesa López-Vázquez ◽  
Erik Rodríguez ◽  
Corrado Costa ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Sea ◽  
Chlorophyll Concentration ◽  
Biological Information ◽  
Test Accuracy ◽  
Times Series ◽  
Transient Activity ◽  
Paragorgia Arborea ◽  
Artificial Neural Network Ann

Imaging technologies are being deployed on cabled observatory networks worldwide. They allow for the monitoring of the biological activity of deep-sea organisms on temporal scales that were never attained before. In this paper, we customized Convolutional Neural Network image processing to track behavioral activities in an iconic conservation deep-sea species—the bubblegum coral Paragorgia arborea—in response to ambient oceanographic conditions at the Lofoten-Vesterålen observatory. Images and concomitant oceanographic data were taken hourly from February to June 2018. We considered coral activity in terms of bloated, semi-bloated and non-bloated surfaces, as proxy for polyp filtering, retraction and transient activity, respectively. A test accuracy of 90.47% was obtained. Chronobiology-oriented statistics and advanced Artificial Neural Network (ANN) multivariate regression modeling proved that a daily coral filtering rhythm occurs within one major dusk phase, being independent from tides. Polyp activity, in particular extrusion, increased from March to June, and was able to cope with an increase in chlorophyll concentration, indicating the existence of seasonality. Our study shows that it is possible to establish a model for the development of automated pipelines that are able to extract biological information from times series of images. These are helpful to obtain multidisciplinary information from cabled observatory infrastructures.

scholarly journals Modular Cellular Neural Network Structure for Wave-Computing-Based Image Processing

ETRI Journal ◽  
2013 ◽  
Vol 35 (2) ◽  
pp. 207-217 ◽  
Author(s):  
Mojtaba Karami ◽  
Reza Safabakhsh ◽  
Mohammad Rahmati
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Network Structure ◽  
Cellular Neural Network ◽  
Neural Network Structure

Study of Highway Crack Diagnosis Based on Cellular Neural Network

2013 ◽  
Vol 427-429 ◽  
pp. 2013-2017
Author(s):  
Sheng Zhuo Yao ◽  
Guo Dong Li ◽  
Fu Xin Zhang ◽  
Lin Ge
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Cellular Neural Network ◽  
Cellular Neural Networks ◽  
Quality Information ◽  
Image Detection ◽  
The Road ◽  
Standard Level ◽  
On The Road

Road quality information detect system is an important component in architecture quality detect system, also is the basement of successfully working of other related project for the whole country. The study of detecting the road crack is the key to insure the security of accurately detect the road quality in transportation system. In this paper, we come up with a fixed way of road undersized rift image detection by using cellular neural networks. By image processing, building rift networks and details networks and adding the model of similarity undersized rift networks. It can avoid the problem that can not accurately detect undersized crack by only taking the crack feature value. The experiment proved that fixed crack detect computing is easy to do, more accurate to detect the undersized cracks on the road and can reach the standard level of current detect technique.

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