Hybrid road recognition method using fuzzy c-means and back-propagation neural network and image processing

2003 ◽  
Author(s):  
Chaofeng Li ◽  
Maolong Yang ◽  
Chengxian Shi ◽  
Deshen Xia
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Recognition Method ◽  
Fuzzy C Means

Application of Improved Back Propagation Neural Network for the Recognition of Composite Insulator Hydrophobicity Grade

2013 ◽  
Vol 373-375 ◽  
pp. 1155-1158
Author(s):  
Kang Yan ◽  
Zhong Yuan Zhang
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Median Filter ◽  
Back Propagation ◽  
Histogram Equalization ◽  
Back Propagation Neural Network ◽  
Threshold Method ◽  
Safe Operation ◽  
Composite Insulator ◽  
Adaptive Median Filter

The detection of hydrophobicity is an important way to evaluate the performance of composite insulator, which is helpful to the safe operation of composite insulator. In this paper, the image processing technology and Back Propagation neural network is introduced to recognize the composite insulator hydrophobicity grade. First, hydrophobic image is preprocessed by histogram equalization and adaptive median filter, then the image was segmented by Ostu threshold method, and four features associated with hydrophobicity are extracted. Finally, the improved Back Propagation neural network is adopted to recognize composite insulator hydrophobicity grade. The experimental results show that the improved Back Propagation neural network can accurately recognize the composite insulator hydrophobicity

Hailstone Classifier Based on Back Propagation Neural Network

2013 ◽  
Vol 433-435 ◽  
pp. 685-690
Author(s):  
Xiang Yang Liu ◽  
Hui Song Wan ◽  
Yuan Yuan Zhang ◽  
Shu Ming Jiang
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Image Feature ◽  
Radar Image ◽  
Data Support ◽  
Color Texture

The Back Propagation (BP) neural network was used for the construction of the hailstone classifier. Firstly, the database of the radar image feature was constructed. Through the image processing, the color, texture, shape and other dimensional features should be extracted and saved as the characteristic database to provide data support for the follow-up work. Secondly, Through the BP neural network, a machine for hail classifications can be built to achieve the hail samples auto-classification.

scholarly journals Brain Tumor Classification and Segmentation using DTCW Transform, Back Propagation Neural Network and Spatial Fuzzy C-Means Clustering

2020 ◽  
Vol 9 (5) ◽  
pp. 2073-2079
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Brain Mri ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Mr Images ◽  
Data Set ◽  
Fuzzy C Means ◽  
Tumor Identification ◽  
Fuzzy C Means Clustering

A novel method is presented in this paper for finding brain tumor and classifying it using the back-propagation neural network is proposed. Spatial Fuzzy C-Means clustering is utilized for the segmentation of image to identify the influenced area of brain MRI picture. Automated detection of tumors in brain MR images is urgent in many diagnosis processes. Because of noise, blurred edges, the detection, and classification of brain tumor are very difficult. This paper presents one programmed brain tumor identification strategy to expand the exactness and yield and diminishing the determination time. The objective is ordering the tissues to three classes of typical, start and malignant. The size and the location tumor is very important for doctors for defining the treatment of tumor. The proposed determination strategy comprises of four phases, pre-processing of MR images, feature extraction, and classification. The features are extracted using Dual-Tree Complex wavelet transformation (DTCWT). Back Propagation Neural Network (BPN) is employed for finding brain tumor in MRI images. In the last stage, a productive scheme is proposed for segmentation depends on the Spatial Fuzzy C-Means Clustering. The performance analysis clearly proves that the proposed scheme is more efficient and the efficiency of the scheme is measured with sensitivity and specificity. The evaluation is performed on the image data set of 15 MRI images of brain.

scholarly journals Performance Analysis of ANN based Gait Recognition

2018 ◽  
pp. 112-125 ◽  
Author(s):  
Taniya Sharma ◽  
Satnam Singh Dub ◽  
Bhanu Gupta
Keyword(s):  
Neural Network ◽  
Background Subtraction ◽  
Distance Measure ◽  
Recognition Performance ◽  
Low Cost ◽  
Gait Recognition ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Recognition Method ◽  
Current Frame

It is well-known that biometrics are a powerful tool for reliable automated person identification. Automatic gait recognition is one of the newest of the emergent biometrics and has many advantages over other biometrics. The most notable advantage is that it does not require contact with the subjects nor does it require the subject to be near a camera. This work employs a gait recognition process with binary silhouette-based input images and Artificial Neural Network (ANN) based classification in MATLAB. The performance of the recognition method depends significantly on the quality of the extracted binary silhouettes. In this work, a computationally low-cost fuzzy correlogram based method is employed for background subtraction. Even highly robust background subtraction and shadow elimination algorithms produce erroneous outputs at times with missing body portions, which consequently affect the recognition performance. Frame Difference Energy Image (FDEI) reconstruction is performed to alleviate the detrimental effect of improperly extracted silhouettes and to make the recognition method robust to partial incompleteness. Subsequently, features are extracted via two methods and fed to the BPNN (Back Propagation Neural Network based classifier which uses feature vector (exemplars) to compute similarity scores and carry out identification using weight vectors i.e. Frame-to-Exemplar-Distance (FED) vector. The FED uses the distance measure between pre-determined feature vectors and the weight vectors of the current frame as an identification criterion. The ANN performance is evaluated for recognition and speed parameters at different training gait angles.

Detection for Corn/Weed Images Using Moment Invariants by BPNN Classifier

2012 ◽  
Vol 605-607 ◽  
pp. 2183-2186
Author(s):  
Lan Lan Wu ◽  
Jie Wu ◽  
You Xian Wen ◽  
Hui Peng ◽  
Zhi Hui Zhu
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Imaging System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Detection Accuracy ◽  
Training Set ◽  
Shape Model ◽  
Binary Images ◽  
Invariant Moments

This study was conducted to discriminate the weed from the corn in a field combined neural network classifier with image processing technology. The corn and weed images were scanned using a colour imaging system. In the first step, an approximate location of the object of interest was determined by minimum enclosing rectangle, in which image processing was done to obtain the binary image. In the second step, the seven invariant moments were extracted from binary images and used as input to the back propagation neural network (BPNN) classifier. The training set was used to construct shape model representing the objects. The detection accuracy was enhanced by adjusting the number of neurons in the network. Experimental results showed that the BPNN classifier achieved overall detection accuracy of 94.52% with 7-28-1.

scholarly journals IDENTIFICATION OF NITROGEN STATUS IN Brassica juncea L.USING COLOR MOMENT GLCM AND BACKPROPAGATION NEURAL NETWORK

2020 ◽  
Author(s):  
Satya Kumara
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Feature Extraction ◽  
Brassica Juncea ◽  
Back Propagation ◽  
Texture Feature ◽  
Back Propagation Neural Network ◽  
Texture Feature Extraction ◽  
Nitrogen Status ◽  
Color Moment

Vegetables cultivation using hydroponic is becoming popular now days because of its irrigation and fertilizer efficiency. One type of vegetable which can be cultivated using hydroponic is green mustard (Brassica juncea L.) tosakan variety. This vegetable is harvested in the vegetative phase, approximately aged of 30 days after planting. In addition, during the vegetative phase, this plant requires more nitrogen for growth of vegetative organs. The lack of nitrogen will lead to slow growth and the leaves turn yellow. In this study, non-destructive technology was developed to identify nitrogen status through the image of green mustard leaf by using digital image processing and artificial neural network. The image processing method used was the color moment for color feature extraction, gray level co-occurrence matrix (GLCM) for texture feature extraction and back propagation neural network to identify nitrogen status from the image of leaf. The input image data resulted from acquisition process was RGB color image which was converted to HSV. Prior to the color and texture feature extraction and texture, acquisition image was segmented and cropped to get the leaf image only. Next Step was to conduct training using back propagation neural network with two hidden layer combinations, 20,000 iteration epoch. Accuracy of the test results using those methods was 97.82%. The result indicates those three methods is reliable to identify nitrogen status in the leaf of green mustard.

scholarly journals Incorporating FCM and Back Propagation Neural Network for Image Segmentation

2014 ◽  
pp. 16-21
Author(s):  
Ebrahim. Aghajari ◽  
Dr.Mrs. Gharpure Damayanti
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Input Data ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Input Image ◽  
Discrete Wavelet ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm ◽  
The Membership Function

Hybrid image segmentation is proposed in this paper. The input image is firstly preprocessed in order to extract the features using Discrete Wavelet Transform (DWT) .The features are then fed to Fuzzy C-means algorithm which is unsupervised. The membership function created by Fuzzy C-means (FCM) is used as a target to be fed in neural network. Then the Back Propagation Neural network (BPN) has been trained based on targets which is obtained by (FCM) and features as input data. Combining the FCM information and neural network in unsupervised manner lead us to achieve better segmentation .The proposed algorithm is tested on various Berkeley database gray level images.

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