A SIMPLE NEURAL NETWORK APPROACH TO INVARIANT IMAGE RECOGNITION

2001 ◽  
Vol 15 (01) ◽  
pp. 11-17
Author(s):  
M. ANDRECUT ◽  
M. K. ALI
Keyword(s):  
Neural Network ◽  
Distribution Function ◽  
Image Recognition ◽  
Network Architecture ◽  
Cellular Neural Network ◽  
Angle Distribution ◽  
Neural Network Architecture ◽  
Feed Forward Neural Network ◽  
Neural Network Approach ◽  
Simple Neural Network

In this paper we propose a simple neural network architecture for invariant image recognition. The proposed neural network architecture contains three specialized modules. The neurons from the first module are connected in a cellular neural network structure, which is responsible for image processing: edge detection and segmentation. The second module is a feed forward neural network for invariant feature extraction from the sensorial layer: computation of the pair distribution function and bond angle distribution function. The third module is responsible for image classification. An application to the face recognition problem is also presented.

scholarly journals Image reconstruction through a multimode fiber with a simple neural network architecture

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Changyan Zhu ◽  
Eng Aik Chan ◽  
You Wang ◽  
Weina Peng ◽  
Ruixiang Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Training Time ◽  
Modal Dispersion ◽  
Multimode Fibers ◽  
Speckle Patterns ◽  
Hidden Layer ◽  
Simple Neural Network

AbstractMultimode fibers (MMFs) have the potential to carry complex images for endoscopy and related applications, but decoding the complex speckle patterns produced by mode-mixing and modal dispersion in MMFs is a serious challenge. Several groups have recently shown that convolutional neural networks (CNNs) can be trained to perform high-fidelity MMF image reconstruction. We find that a considerably simpler neural network architecture, the single hidden layer dense neural network, performs at least as well as previously-used CNNs in terms of image reconstruction fidelity, and is superior in terms of training time and computing resources required. The trained networks can accurately reconstruct MMF images collected over a week after the cessation of the training set, with the dense network performing as well as the CNN over the entire period.

scholarly journals ISAR Image Recognition Algorithm and Neural Network Implementation

2017 ◽  
Vol 17 (4) ◽  
pp. 183-199 ◽  
Author(s):  
A. Lazarov ◽  
C. Minchev
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Image Recognition ◽  
Network Architecture ◽  
Target Identification ◽  
Recognition Algorithm ◽  
Decision Strategy ◽  
Neural Network Architecture ◽  
Line Extraction ◽  
Radar Applications

AbstractThe image recognition and identification procedures are comparatively new in the scope of ISAR (Inverse Synthetic Aperture Radar) applications and based on specific defects in ISAR images, e.g., missing pixels and parts of the image induced by target’s aspect angles require preliminary image processing before identification. The present paper deals with ISAR image enhancement algorithms and neural network architecture for image recognition and target identification. First, stages of the image processing algorithms intended for image improving and contour line extraction are discussed. Second, an algorithm for target recognition is developed based on neural network architecture. Two Learning Vector Quantization (LVQ) neural networks are constructed in Matlab program environment. A training algorithm by teacher is applied. Final identification decision strategy is developed. Results of numerical experiments are presented.

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