Artificial Neural Network Based Automatic Number Plate Recognition System

This paper deals with Automatic Number Plate Recognition (ANPR) using Artificial Neural Network. The ANPR system includes steps like pre-processing, localization, character segmentation and character recognition. The developed system first detects the vehicle and then captures the vehicle image. The captured image is pre-processed in order to enhance it for further processing. In localization the license plate region is located and cropped from the complete image. In character segmentation images of individual alpha-numeric characters are extracted from the localized plate. In this paper, we proposed Neural Network based character recognition. Scaled Conjugate Gradient Backpropogation algorithm is used for training the neural network. This system is implemented in MATLAB R2014b

Logo recognition using Artificial Neural Network (ANN) / Nor Hamidah Abdul Ghafar

This project is about logo recognition using Artificial Neural Network (ANN). In order to recognize the logo, a training phase using back propagation technique was implemented. Based on study of existing research, many image and pattern recognition has been done by using Artificial Neural Network and back propagation technique. Logo was scanned or captured trough the Internet. There are five logo and each logo have four different size or fi-om different source. Logo must firstly done process of pre-processing by using MatLab 6.5 in order to normalize the logo to a specific size and for noise removal. In addition, the edge detection for the logo also used MatLab 6.5 to get the logo parameter and transform the logo into binary representation. The binary representation was used for the input node of neural network for back propagation training algorithm. To ensure a good performance of logo recognition prototype, numbers of experiments are done by adjusting the parameters of back propagation training algorithm. Finally, this research found that Artificial Neural Network and back propagation algorithm is suitable for image and pattern recognition

LAND COVER CLASSIFICATION BASED ON MODIS IMAGERY DATA USING ARTIFICIAL NEURAL NETWORKS

Remote sensing has been widely used to obtain land cover information using automated classification. Land cover is a measure of what is overlaying the surface of the earth. Accurate mapping of land cover on a regional scale is useful in such fields as precision agriculture or forest management and is one of the most important applications in remote sensing. In this study, multispectral MODIS Terra NDVI images and an artificial neural network (ANN) were used in land cover classification. Artificial neural network is a computing tool that is designed to simulate the way the human brain analyzes and process information. Artificial neural networks are one of the commonly applied machine learning algorithm, and they have become popular in the analysis of remotely sensed data, particularly in classification or feature extraction from image data more accurately than conventional method. This paper focuses on an automated classification system based on a pattern recognition neural network. Variational mode decomposition method is used as an image data pre-processing tool in this classification system. The result of this study will be land cover map

Modified Distributive Arithmetic based 2D-DWT for Hybrid (Neural Network-DWT) Image Compression

Artificial Neural Networks ANN is significantly used in signal and image processing techniques for pattern recognition and template matching Discrete Wavelet Transform DWT is combined with neural network to achieve higher compression if 2D data such as image Image compression using neural network and DWT have shown superior results over classical techniques with 70 higher compression and 20 improvement in Mean Square Error MSE Hardware complexity and power issipation are the major challenges that have been addressed in this work for VLSI implementation In this work modified distributive arithmetic DWT and multiplexer based DWT architecture are designed to reduce the computation complexity of hybrid architecture for image compression A 2D DWT architecture is designed with 1D DWT architecture and is implemented on FPGA that operates at 268 MHz consuming power less than 1

Identifikasi Tingkat Manis Buah Belimbing Berdasarkan Citra Red Green Blue Menggunakan Fuzzy Neural Network

  Fuzzy Neural Network (FNN) has a capability to classify a pattern within two different classes which a classical Neural Network (NN) is failed to do so. The fuzzy pattern classification use membership degree on output of neuron as learning target. This research aim is to develop an artificial intelligence system model for non-destructive classification of starfruit using Fuzzy Neural Network. The input parameter is the estimator parameter of starfruit sweet level of red, green and blue index color obtained from image processing. The best result of starfruit sweet level identification using FNN with three classification class target (sour, medium and sweet) is achieved with 25 neurons in hidden layer and 14th epoch with 100% accuracy.   Keyword : classification, fuzzy neural network, starfruit, non-destructive grading, pattern recognition.   &nbsp

Result Oriented Based Face Recognition using Neural Network with Erosion and Dilation Technique

It has been observed that many face recognition algorithms fail to recognize faces after plastic surgery and wearing the spec/glasses which are the new challenge to automatic face recognition. Face detection is one of the challenging problems in the image processing. This seminar, introduce a face detection and recognition system to detect (finds) faces from database of known people. To detect the face before trying to recognize it saves a lot of work, as only a restricted region of the image is analyzed, opposite to many algorithms which work considering the whole image. In This , we gives study on Face Recognition After Plastic Surgery (FRAPS )and after wearing the spec/glasses with careful analysis of the effects on face appearance and its challenges to face recognition. To address FRAPS and wearing the spec/glasses problem, an ensemble of An Optimize Wait Selection By Genetic Algorithm For Training Artificial Neural Network Based On Image Erosion and Dilution Technology. Furthermore, with our impressive results, we suggest that face detection should be paid more attend to. To address this problem, we also used Edge detection method to detect i/p image properly or effectively. With this Edge Detection also used genetic algorithm to optimize weight using artificial neural network (ANN)and save that ANN file to database .And use that ANN file to compare face recognition in future DOI: 10.17762/ijritcc2321-8169.16041

KOMBINASI ALGORITMA JARINGAN SYARAF TIRUAN LEARNING VECTOR QUANTIZATION (LVQ) DAN SELF ORGANIZING KOHONEN PADA KECEPATAN PENGENALAN POLA TANDA TANGAN

Signature is a special form of handwriting that contain special characters and additional forms are often used as proof of a person's identity verification. Partially legible signature, but many signatures that can not be read. However, a signature can be handled as an image so that it can be recognized using pattern recognition applications in image processing. Because the signature is the primary mechanism for authentication and authorization in legal transactions,the need for research on the development of recognition applications and automatic signature verification and efficiently increases from year to year. The method is widely used in signature recognition is a method of artificial neural network. On artificial neural networks are learning and recognition. One neural network algorithm is Learning Vector Quantization ( LVQ ) and Self Organizing Kohonen. Processes that occur in the neural network method requires a relatively long time. It is influenced by the number of data samples are used as a means of weight training update. The more and the large size of the pattern being trained, the longer the time it takes the network. LVQ is a method of training the unsupervised competitive layer will automatically learn to classify input vectors into certain classes. The classes are generated depends on the distance between the input vectors. If there are 2 input vectors are nearly as competitive layer will then classify both the input vectors into the same class. Kohonen Self Organizing Network is one of the neural network model which uses learning methods or unguided unsupervised neural network model that resembles humans. To speed up the computing process in the training and recognition is then developed an algorithm and a combination of LVQ and Self Organizing Kohonen by modifying the weight given to obtain a shorter time in the process of training and recognition

Backpropagation Neural Network for Book Classification Using the Image Cover

Artificial Neural Networks are known to provide a good model forclassification. The goal of this research is to classify books in Bahasa (Bahasa Indonesia) using its cover. The data is in the form of scanned images, each with the size of 300 cm height, 130 cm width, and 96 dpi image resolution the research conducted features extraction using image processing method, MSER (Maximally Stable Externally Regions) to identify the area of book title, and Tesseract Optical Character Recognition (OCR) to detect the title. Next, features extracted from MSER and OCR are converted into a numerical matrix as the input to the Backpropagation Artificial Neural Network. The accuracy obtained using one hidden layer and 15 neurons is 63.31%. Meanwhile, the evaluation using 2 hidden layers with a combination of 15 and 35 neurons resulted in accuracy of 79.89%. The ability of the model to classify the book was affected by the image quality, variation, and number of training data

Object recognition in lake and estuary environments

Traditionally, autonomous underwater vehicles employ multiple configurations of sensor payloads in order to accomplish a specific mission. Due to advances in imaging technology, imaging sonar arrays and optical imaging devices are among these payloads. Independent of mission specifics, the majority of imaging data is either stored onboard the vehicle or transmitted to a base station for later analysis. In either situation, there is limited local real time analysis and limited mission duration. One focus for increasing real time analysis is the reduction of image information. By using image processing techniques, such as edge detection, less relevant information can be eliminated while preserving important object features. This reduced object information is then used as inputs to a neural network. A neural network is a cognitive algorithm which has the ability to adapt to achieve desired tasks. These networks are able to generalize and make decisions based on partial or limited input information. The goal of this research is to create an autonomous in-situ recognition system for marine environments, specifically the processing and classification of object image data. Image information will be applied to a neural network approach to mimic higher order decision making in an artificial cognitive algorithm