Tourist Guide via Image Processing Techniques

2021 ◽  
Vol 4 (2) ◽  
pp. 144-148
Author(s):  
Rabia Noor Enam ◽  
Muhammad Tahir ◽  
Syed Muhammad Nabeel Mustafa ◽  
Rehan Qureshi ◽  
Hasan Shahid
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Dimensional Space ◽  
Image Data ◽  
Predictive Performance ◽  
Two Dimensional ◽  
Image Processing Techniques ◽  
Modern Computer ◽  
New Type ◽  
Processing Techniques

To achieve the goal of identification, image processing and recognition is performed on the actual picture transformation. The amount of information included in an image is enormous because it is a two-dimensional space. Neural network image recognition is a new type of picture recognition technology developed by modern computer innovation. In this paper we have used neural network to implement image-based location identification. Using the image data, we have evaluated our proposed model’s predictive performance. By including more hidden layers in the convolution neural network, it is feasible to improve the network's training speed and reliability by having a large amount of data. However, 80% of the dataset is used for training and the remaining 20% is used for testing in our studies. The preliminary findings have shown that using a neural network to detect photos is both successful and practical. The proposed methods serve as a guide for travelers or tourists who are unfamiliar with a country. They simply need to point the camera at any historical or well-known location to learn more about it.

AIRCRAFT CLASSIFICATION USING IMAGE PROCESSING TECHNIQUES AND ARTIFICIAL NEURAL NETWORKS

2011 ◽  
Vol 25 (08) ◽  
pp. 1321-1335 ◽  
Author(s):  
ADIL GURSEL KARACOR ◽  
ERDAL TORUN ◽  
RASIT ABAY
Keyword(s):  
Image Processing ◽  
Image Data ◽  
Radar Data ◽  
Still Image ◽  
Image Processing Techniques ◽  
Black And White ◽  
Rf Signals ◽  
Different Types ◽  
Artificial Neural ◽  
Processing Techniques

Identifying the type of an approaching aircraft, should it be a helicopter, a fighter jet or a passenger plane, is an important task in both military and civilian practices. The task in question is normally done by using radar or RF signals. In this study, we suggest an alternative method that introduces the use of a still image instead of RF or radar data. The image was transformed to a binary black and white image, using a Matlab script which utilizes Image Processing Toolbox commands of Matlab, in order to extract the necessary features. The extracted image data of four different types of aircraft was fed into a three-layered feed forward artificial neural network for classification. Satisfactory results were achieved as the rate of successful classification turned out to be 97% on average.

scholarly journals Machine Learning and Fuzzy Logic based Detection, Classification and Grading of Leaf Disease in Plants

2020 ◽  
Vol 9 (7S) ◽  
pp. 38-42
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Fuzzy Logic ◽  
Learning Algorithm ◽  
Expert Advice ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Image Processing Techniques ◽  
Processing Techniques

India is an agricultural country where most of people are depends on the agriculture. When Plants are infected by the virus, fungus and bacteria, they are mostly seen on leaves and stems of the plants. Because of that, plants production is decreased also economy of the country is decreased. The farmer has to identify the disease and decide which pesticide will be used to control the disease in plants. To finding out which disease affect the plants, the farmer contacts the expert for the solution. The expert gives the advice based on its knowledge and information but sometimes seeking the expert advice is time consuming, expensive and may be not accurate. So, to solve this problem, the image processing techniques and Machine Learning algorithm like Neural Network, Fuzzy Logic and Support Vector Machine gives the better, accurate and affordable solution to control the plants disease than manual method.

A Study on Various Image Processing Techniques and Hardware Implementation Using Xilinx System Generator

2017 ◽  
pp. 215-230
Author(s):  
Jyotsna Rani ◽  
Ram Kumar ◽  
Abahan Sarkar ◽  
Fazal A. Talukdar
Keyword(s):  
Image Processing ◽  
Hardware Implementation ◽  
Image Data ◽  
Tool Support ◽  
Environment Design ◽  
Image Processing Techniques ◽  
System Generator ◽  
Xilinx System Generator ◽  
Support Software ◽  
Processing Techniques

This article reviews the various image processing techniques in MATLAB and also hardware implementation in FPGA using Xilinx system generator. Image processing can be termed as processing of images using mathematical operations by using various forms of signal processing techniques. The main aim of image processing is to extract important features from an image data and process it in a desired manner and to visually enhance or to statistically evaluate the desired aspect of the image. This article provides an insight into the various approaches of Digital Image processing techniques in Matlab. This article also provides an introduction to FPGA and also a step by step tutorial in handling Xilinx System Generator. The Xilinx System Generator tool is a new application in image processing and offers a friendly environment design for the processing. This tool support software simulation, but the most important is that can synthesize in FPGAs hardware, with the parallelism, robust and speed, this features are essentials in image processing. Implementation of these algorithms on a FPGA is having advantage of using large memory and embedded multipliers. Advances in FPGA technology with the development of sophisticated and efficient tools for modelling, simulation and synthesis have made FPGA a highly useful platform.

Use of Deep Neural Network for Optical Character Recognition

2020 ◽  
pp. 219-254
Author(s):  
Abhishek Das ◽  
Mihir Narayan Mohanty
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Comparative Study ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Deep Neural Network ◽  
Detailed Review ◽  
Image Processing Techniques ◽  
Optical Character ◽  
Processing Techniques

In this chapter, the authors have given a detailed review on optical character recognition. Various methods are used in this field with different accuracy levels. Still there are some difficulties in recognizing handwritten characters because of different writing styles of different individuals even in a particular language. A comparative study is given to understand different types of optical character recognition along with different methods used in each type. Implementation of neural network in different forms is found in most of the works. Different image processing techniques like OCR with CNN, RNN, combination of CNN and RNN, etc. are observed in recent research works.

A Study on Various Image Processing Techniques and Hardware Implementation Using Xilinx System Generator

2018 ◽  
pp. 930-945
Author(s):  
Jyotsna Rani ◽  
Ram Kumar ◽  
Abahan Sarkar ◽  
Fazal A. Talukdar
Keyword(s):  
Image Processing ◽  
Hardware Implementation ◽  
Image Data ◽  
Tool Support ◽  
Environment Design ◽  
Image Processing Techniques ◽  
System Generator ◽  
Xilinx System Generator ◽  
Support Software ◽  
Processing Techniques

This article reviews the various image processing techniques in MATLAB and also hardware implementation in FPGA using Xilinx system generator. Image processing can be termed as processing of images using mathematical operations by using various forms of signal processing techniques. The main aim of image processing is to extract important features from an image data and process it in a desired manner and to visually enhance or to statistically evaluate the desired aspect of the image. This article provides an insight into the various approaches of Digital Image processing techniques in Matlab. This article also provides an introduction to FPGA and also a step by step tutorial in handling Xilinx System Generator. The Xilinx System Generator tool is a new application in image processing and offers a friendly environment design for the processing. This tool support software simulation, but the most important is that can synthesize in FPGAs hardware, with the parallelism, robust and speed, this features are essentials in image processing. Implementation of these algorithms on a FPGA is having advantage of using large memory and embedded multipliers. Advances in FPGA technology with the development of sophisticated and efficient tools for modelling, simulation and synthesis have made FPGA a highly useful platform.

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