Computer-Generated Hologram Compression Research Based on BP Neural Network in Wavelet Domain

2014 ◽  
Vol 971-973 ◽  
pp. 1884-1887 ◽  
Author(s):  
A Lin Hou ◽  
Liang Wu ◽  
Qing Liao ◽  
Chong Jin Wang ◽  
Jun Liang Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Bp Neural Network ◽  
Function Approximation ◽  
Simple Structure ◽  
Inner Product ◽  
Compression Rate ◽  
Wavelet Domain ◽  
Fast Calculation ◽  
Computer Generated Hologram

The algorithm of hologram compression using BP neural network in wavelet domain is proposed. Firstly, computer-generated hologram pretreatment is carried out by wavelet transform. And then the inner product of wavelet and holograms are weighted and used to implement the feature extraction of hologram. Finally, the extracted feature vectors are substituted into neural network so as to implement the function approximation, classification and hologram compression. The experimental results clearly show the feasibility and effectiveness of the method. The compression rate can reach 0.803%and still gets a clear reconstructed image. And the algorithm has the advantages of simple structure and fast calculation speed.

Bp Neural Network Optimized by PSO and its Application in Function Approximation

2014 ◽  
Vol 945-949 ◽  
pp. 2413-2416 ◽  
Author(s):  
Jun Yi Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Function Approximation ◽  
Local Minimum ◽  
Simple Structure ◽  
Distributed Storage ◽  
Convergence Time ◽  
Bp Network ◽  
Network Training ◽  
Local Minimum Point

BP network is one of the most popular artificial neural networks because of its special advantage such as simple structure, distributed storage, parallel processing, high fault-tolerance performance, etc. However, with its extensive use in recent years, it is discovered that BP algorithm has the defects on slow convergent speed and easy convergence to a local minimum point. The paper proposes a method of BP Neural Network improved by Particle Swarm Optimization (PSO). The hybrid algorithm can not only avoid local minimum, but also raise the speed of network training and reduce the convergence time.

The Character Recognition of Vehicle's License Plate Based on BP Neural Networks

2014 ◽  
Vol 513-517 ◽  
pp. 3805-3808 ◽  
Author(s):  
Wen Bo Liu ◽  
Tao Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Bp Neural Network ◽  
Character Recognition ◽  
License Plate ◽  
Image Preprocessing ◽  
Matrix Operations ◽  
Neural Network Algorithm ◽  
Character Segment

This paper based on license plate image preprocessing ,license plate localization, and character segment ,using BP neural network algorithm to identify the license plate characters. Through k-l algorithm of characters on the feature extraction and recognition of license plate character respectively then taking the extraction of license plate character features into the character classifier to the training. When the end of training, extracting the net-work weights and offset matrix, and storing in the computer. To take the identified character images input to the MATLAB, and with the preservation weights and offset matrix operations, obtain the final results of recognition.

Human–Computer Interactive Gesture Feature Capture and Recognition in Virtual Reality

2020 ◽  
pp. 106480462092413 ◽  
Author(s):  
Fan Zhang
Keyword(s):  
Neural Network ◽  
Virtual Reality ◽  
Feature Extraction ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Extraction Methods ◽  
Scale Invariant ◽  
Feature Vectors ◽  
Accuracy And Precision ◽  
Scale Invariant Feature

With the development of computer technology, the simulation authenticity of virtual reality technology is getting higher and higher, and the accurate recognition of human–computer interaction gestures is also the key technology to enhance the authenticity of virtual reality. This article briefly introduced three different gesture feature extraction methods: scale invariant feature transform, local binary pattern and histogram of oriented gradients (HOG), and back-propagation (BP) neural network for classifying and recognizing different gestures. The gesture feature vectors obtained by three feature extraction methods were used as input data of BP neural network respectively and were simulated in MATLAB software. The results showed that the information of feature gesture diagram extracted by HOG was the closest to the original one; the BP neural network that applied HOG extracted feature vectors converged to stability faster and had the smallest error when it was stable; in the aspect of gesture recognition, the BP neural network that applied HOG extracted feature vector had higher accuracy and precision and lower false alarm rate.

scholarly journals Research on Crop Leaf Disease Identification Method Based on LM-BP Neural Network

2019 ◽  
Vol 131 ◽  
pp. 01118
Author(s):  
Fan Tongke
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Bp Neural Network ◽  
Extraction Method ◽  
Large Scale ◽  
Disease Diagnosis ◽  
Learning Technology ◽  
Feature Extraction Method ◽  
Disease Identification

Aiming at the problem of disease diagnosis of large-scale crops, this paper combines machine vision and deep learning technology to propose an algorithm for constructing disease recognition by LM_BP neural network. The images of multiple crop leaves are collected, and the collected pictures are cut by image cutting technology, and the data are obtained by the color distance feature extraction method. The data are input into the disease recognition model, the feature weights are set, and the model is repeatedly trained to obtain accurate results. In this model, the research on corn disease shows that the model is simple and easy to implement, and the data are highly reliable.

Feature Extraction and Recognition Based on the Biological Analysis

2014 ◽  
Vol 513-517 ◽  
pp. 3180-3183
Author(s):  
Wen Cang Zhao ◽  
Fan Wang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Bp Neural Network ◽  
Characteristic Parameters ◽  
Invariant Moments ◽  
Biological Analysis ◽  
Stored Product Pests ◽  
Abdominal Perimeter ◽  
Stable Characteristic

In this paper the extracted features including rectangularity,elongation, invariant moments and the four ratios of the stored product pests, which are the ratio of antennae area to torso area, the ratio of antennae perimeter to torso perimeter,the ratio of head and chest area to abdominal area, the ratio of head and chest perimeter to abdominal perimeter. Then these 13 characteristic parameters are input to BP neural network and SVM for recognition and classification. Form the results we can see that the 13 features in this paper can be well reflected the stable characteristic information of the stored product pests.

scholarly journals A Face Recognition System Using ACO-BPNN Model for Optimizing the Teaching Management System

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiuli Zhu
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Face Recognition ◽  
Human Brain ◽  
Bp Neural Network ◽  
Ant Colony Algorithm ◽  
Basic Function ◽  
Face Image ◽  
Feature Extraction Method ◽  
Bpnn Model

The basic idea of face recognition technology is to compare the matching degree between the standard face image marked with identity information and the static or dynamic face collected from the actual scene, which includes two main research contents: face feature extraction and face feature recognition. Traditional identification generally proves who we are through certificates, passwords, or certificates plus passwords. With the development of science and technology, face recognition technology will occupy an increasingly important position. Inspired by the human brain, the artificial neural network (ANN) is an information extraction system based on imitating the basic function and structure of the human brain and abstracted by physical and mathematical research methods. Based on the traditional BP neural network model, this paper proposes an ant colony algorithm-enabled BP neural network (ACO-BPNN) model and applies it to face recognition. Experimental results show that, similar to other face recognition techniques, the facial feature location needs to adapt to various changes of faces to the maximum extent, so the recognition and classification effect of the whole face feature extracted from the whole face image on the changes of such partial areas is not good, while the local feature extraction method based on ACO-BPNN can achieve a good recognition and classification effect.

Detection of Violent Video with Audio-Visual Features Based on MPEG-7

2013 ◽  
Vol 411-414 ◽  
pp. 1002-1007 ◽  
Author(s):  
Yu Qing Peng ◽  
Wei Liu ◽  
Cui Cui Zhao ◽  
Tie Jun Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Bp Neural Network ◽  
Color Space ◽  
New Method ◽  
Visual Features ◽  
Fusion Model ◽  
Data Redundancy ◽  
Dominant Color ◽  
Time Motion

In order to solve the problem that there isn’t an effective way to detect the violent video in the network, a new method using MPEG-7 audio and visual features to detect violent video was put forward. In feature extraction, the new method targeted chosen the features about audio, color, space, time, motion. Parts of MPEG-7 descriptors were added and improved: instantaneous feature of audio was added, motion intensity descriptor was customized, and a new method to extract dominant color of video was proposed. BP neural network optimized by GA was used to fuse the features. Experiment shows that these selected features are representative, discriminative and can reduce the data redundancy. Fusion model of neural network is more robust. And the method of fusing audio and visual features improves the recall and precision of video detecting.

Research on Aviation Material with Unsafe Events Prediction Based on BP Neural Network with PCA Feature Extraction

2014 ◽  
Vol 540 ◽  
pp. 488-491 ◽  
Author(s):  
Xu Sheng Gan ◽  
Hua Ping Li ◽  
Jing Shun Duanmu
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Aviation Material ◽  
Training Samples ◽  
Basic Features ◽  
Bp Neural Network Model ◽  
Better Than

In order to better predict the aviation material unsafe events, a BP neural network model based on PCA feature extraction is proposed. Firstly, the training samples of aviation material unsafe events are used to carry out the PCA feature extraction, and then using the extracted basic features, BP neural network model is established. The numerical example shows that, the hybrid model proposed is better than that of alone BP neural network model, and it is effective and feasible to establish the unsafe events model for aviation material.

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