A novel descriptive feature extraction method of Discrete Fourier transform and neural network classifier for classification of Synthetic Aperture Radar (SAR) images is proposed. The classification process has the following stages (1) Image Segmentation using statistical Region Merging (SRM) (2) Polar transform and Feature extraction using Discrete Fourier Transform (3) Neural Network classification using back propagation. This is generally the first step in image analysis. Segmentation subdivides an image into its constituent parts or objects. The level to which this subdivision is carried depends on the problem being solved. The image segmentation in this study is performed using Statistical Region Merging proposed Richard Nock and Frank Nielsen. The key idea of the Statistical Region Merging model is to formulate image segmentation as an inference problem. Here the merging procedure is based on the theorem. Feature vectors as the input for the neural network. Polar transform is applied to segmented SAR image. The rotation problem under the Cartesian coordinates becomes the translation problem under the polar coordinates.
Object extraction using edge, motion and saliency information from videoseSAT Journals
Abstract Object detection is a process of finding the instances of object of a certain class which is useful in analysis of video or image. There are number of algorithms have been developed so far for object detection. Object detection has got significant role in variety of areas of computer vision like video surveillance, image retrieval`. In this paper presented an efficient algorithm for moving object extraction using edge, motion and saliency information from videos. Out methodology includes 4 stages: Frame generation, Pre-processing, Foreground generation and integration of cues. Foreground generation includes edge detection using sobel edge detection algorithm, motion detection using pixel-based absolute difference algorithm and motion saliency detection. Conditional Random Field (CRF) is applied for integration of cues and thus we get better spatial information of segmented object. Keywords: Object detection, Saliency information, Sobel edge detection, CRF.
Image Segmentation Using Two Weighted Variable Fuzzy K MeansEditor IJCATR
Image segmentation is the first step in image analysis and pattern recognition. Image segmentation is the process of dividing an image into different regions such that each region is homogeneous. The accurate and effective algorithm for segmenting image is very useful in many fields, especially in medical image. This paper presents a new approach for image segmentation by applying k-means algorithm with two level variable weighting. In image segmentation, clustering algorithms are very popular as they are intuitive and are also easy to implement. The K-means and Fuzzy k-means clustering algorithm is one of the most widely used algorithms in the literature, and many authors successfully compare their new proposal with the results achieved by the k-Means and Fuzzy k-Means. This paper proposes a new clustering algorithm called TW-fuzzy k-means, an automated two-level variable weighting clustering algorithm for segmenting object. In this algorithm, a variable weight is also assigned to each variable on the current partition of data. This could be applied on general images and/or specific images (i.e., medical and microscopic images). The proposed TW-Fuzzy k-means algorithm in terms of providing a better segmentation performance for various type of images. Based on the results obtained, the proposed algorithm gives better visual quality as compared to several other clustering methods.
Wireless Vision based Real time Object Tracking System Using Template MatchingIDES Editor
In the present work the concepts of template
matching through Normalized Cross Correlation (NCC) has
been used to implement a robust real time object tracking
system. In this implementation a wireless surveillance pinhole
camera has been used to grab the video frames from the non
ideal environment. Once the object has been detected it is
tracked by employing an efficient Template Matching
algorithm. The templates used for the matching purposes are
generated dynamically. This ensures that any change in the
pose of the object does not hinder the tracking procedure. To
automate the tracking process the camera is mounted on a
disc coupled with stepper motor, which is synchronized with a
tracking algorithm. As and when the object being tracked
moves out of the viewing range of the camera, the setup is
automatically adjusted to move the camera so as to keep the
object of about 360 degree field of view. The system is capable
of handling entry and exit of an object. The performance of
the proposed Object tracking system has been demonstrated
in real time environment both for indoor and outdoor by
including a variety of disturbances and a means to detect a
loss of track.
Object extraction using edge, motion and saliency information from videoseSAT Journals
Abstract Object detection is a process of finding the instances of object of a certain class which is useful in analysis of video or image. There are number of algorithms have been developed so far for object detection. Object detection has got significant role in variety of areas of computer vision like video surveillance, image retrieval`. In this paper presented an efficient algorithm for moving object extraction using edge, motion and saliency information from videos. Out methodology includes 4 stages: Frame generation, Pre-processing, Foreground generation and integration of cues. Foreground generation includes edge detection using sobel edge detection algorithm, motion detection using pixel-based absolute difference algorithm and motion saliency detection. Conditional Random Field (CRF) is applied for integration of cues and thus we get better spatial information of segmented object. Keywords: Object detection, Saliency information, Sobel edge detection, CRF.
Image Segmentation Using Two Weighted Variable Fuzzy K MeansEditor IJCATR
Image segmentation is the first step in image analysis and pattern recognition. Image segmentation is the process of dividing an image into different regions such that each region is homogeneous. The accurate and effective algorithm for segmenting image is very useful in many fields, especially in medical image. This paper presents a new approach for image segmentation by applying k-means algorithm with two level variable weighting. In image segmentation, clustering algorithms are very popular as they are intuitive and are also easy to implement. The K-means and Fuzzy k-means clustering algorithm is one of the most widely used algorithms in the literature, and many authors successfully compare their new proposal with the results achieved by the k-Means and Fuzzy k-Means. This paper proposes a new clustering algorithm called TW-fuzzy k-means, an automated two-level variable weighting clustering algorithm for segmenting object. In this algorithm, a variable weight is also assigned to each variable on the current partition of data. This could be applied on general images and/or specific images (i.e., medical and microscopic images). The proposed TW-Fuzzy k-means algorithm in terms of providing a better segmentation performance for various type of images. Based on the results obtained, the proposed algorithm gives better visual quality as compared to several other clustering methods.
Wireless Vision based Real time Object Tracking System Using Template MatchingIDES Editor
In the present work the concepts of template
matching through Normalized Cross Correlation (NCC) has
been used to implement a robust real time object tracking
system. In this implementation a wireless surveillance pinhole
camera has been used to grab the video frames from the non
ideal environment. Once the object has been detected it is
tracked by employing an efficient Template Matching
algorithm. The templates used for the matching purposes are
generated dynamically. This ensures that any change in the
pose of the object does not hinder the tracking procedure. To
automate the tracking process the camera is mounted on a
disc coupled with stepper motor, which is synchronized with a
tracking algorithm. As and when the object being tracked
moves out of the viewing range of the camera, the setup is
automatically adjusted to move the camera so as to keep the
object of about 360 degree field of view. The system is capable
of handling entry and exit of an object. The performance of
the proposed Object tracking system has been demonstrated
in real time environment both for indoor and outdoor by
including a variety of disturbances and a means to detect a
loss of track.
Image Steganography Using Wavelet Transform And Genetic AlgorithmAM Publications
This paper presents the application of Wavelet Transform and Genetic Algorithm in a novel
steganography scheme. We employ a genetic algorithm based mapping function to embed data in Discrete Wavelet
Transform coefficients in 4x4 blocks on the cover image. The optimal pixel adjustment process is applied after
embedding the message. We utilize the frequency domain to improve the robustness of steganography and, we
implement Genetic Algorithm and Optimal Pixel Adjustment Process to obtain an optimal mapping function to
reduce the difference error between the cover and the stego-image, therefore improving the hiding capacity with
low distortions. Our Simulation results reveal that the novel scheme outperforms adaptive steganography technique
based on wavelet transform in terms of peak signal to noise ratio and capacity, 39.94 dB and 50% respectively.
A Pattern Classification Based approach for Blur Classificationijeei-iaes
Blur type identification is one of the most crucial step of image restoration. In case of blind restoration of such images, it is generally assumed that the blur type is known prior to restoration of such images. However, it is not practical in real applications. So, blur type identification is extremely desirable before application of blind restoration technique to restore a blurred image. An approach to categorize blur in three classes namely motion, defocus, and combined blur is presented in this paper. Curvelet transform based energy features are utilized as features of blur patterns and a neural network is designed for classification. The simulation results show preciseness of proposed approach.
The automotive industry requires an automated system to sort different sizes and shapes
objects, images which are the mainly used component in the industry, to improve the overall
productivity. There are things at which humans are still way ahead of the machines in terms of
efficiency one of such thing is the recognition especially pattern recognition. There are several
methods which are tested for giving the machines the intelligence in efficient way for pattern
recognition purpose. The artificial neural network is one of the most optimization techniques used
for training the networks for efficient recognition. Computer vision is the science and technology of
machines that can see. The machine is made by integration of many parts to extract information from
an image in order to solve some task. Principle component analysis is a technique that will be
suitably used for the application purpose for sorting, inspection, fault diagnosis in various field.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Reconstructing the Path of the Object based on Time and Date OCR in Surveilla...ijtsrd
The inclusion of time based queries in video indexing application is enables by the recognition of time and date stamps in CCTV video. In this paper, we propose the system for reconstructing the path of the object in surveillance cameras based on time and date optical character recognition system. Since there is no boundary in region for time and date, Discrete Cosine Transform DCT method is applied in order to locate the region area. After the region for time and date is located, it is segmented and then features for the symbols of the time and date are extracted. Back propagation neural network is used for recognition of the features and then stores the result in the database. By using the resulted database, the system reconstructs the path for the object based on time. The proposed system will be implemented in MATLAB. Pyae Phyo Thu | Mie Mie Tin | Ei Phyu Win | Cho Thet Mon "Reconstructing the Path of the Object based on Time and Date OCR in Surveillance System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27981.pdfPaper URL: https://www.ijtsrd.com/home-science/education/27981/reconstructing-the-path-of-the-object-based-on-time-and-date-ocr-in-surveillance-system/pyae-phyo-thu
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Review and comparison of tasks scheduling in cloud computingijfcstjournal
Recently, there has been a dramatic increase in the popularity of cloud computing systems that rent
computing resources on-demand, bill on a pay-as-you-go basis, and multiplex many users on the same
physical infrastructure. It is a virtual pool of resources which are provided to users via Internet. It gives
users virtually unlimited pay-per-use computing resources without the burden of managing the underlying
infrastructure. One of the goals is to use the resources efficiently and gain maximum profit. Scheduling is a
critical problem in Cloud computing, because a cloud provider has to serve many users in Cloud
computing system. So scheduling is the major issue in establishing Cloud computing systems. The
scheduling algorithms should order the jobs in a way where balance between improving the performance
and quality of service and at the same time maintaining the efficiency and fairness among the jobs. This
paper introduces and explores some of the methods provided for in cloud computing has been scheduled.
Finally the waiting time and time to implement some of the proposed algorithm is evaluated
Fast Motion Estimation for Quad-Tree Based Video Coder Using Normalized Cross...CSCJournals
Motion estimation is the most challenging and time consuming stage in block based video codec. To reduce the computation time, many fast motion estimation algorithms were proposed and implemented. This paper proposes a quad-tree based Normalized Cross Correlation (NCC) measure for obtaining estimates of inter-frame motion. The measure operates in frequency domain using FFT algorithm as the similarity measure with an exhaustive full search in region of interest. NCC is a more suitable similarity measure than Sum of Absolute Difference (SAD) for reducing the temporal redundancy in video compression since we can attain flatter residual after motion compensation. The degrees of homogeneous and stationery regions are determined by selecting suitable initial fixed threshold for block partitioning. An experimental result of the proposed method shows that actual numbers of motion vectors are significantly less compared to existing methods with marginal effect on the quality of reconstructed frame. It also gives higher speed up ratio for both fixed block and quad-tree based motion estimation methods.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Artificial neural networks (ANN) consider classification as one of the most dynamic research and
application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by
back propagation algorithm. The different combinations of functions and its effect while using ANN as a
classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The
back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification
with suitable combination of training, learning and transfer functions. When the maximum likelihood
method was compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible.
Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to
be more effective than other classification algorithms.
Image Steganography Using Wavelet Transform And Genetic AlgorithmAM Publications
This paper presents the application of Wavelet Transform and Genetic Algorithm in a novel
steganography scheme. We employ a genetic algorithm based mapping function to embed data in Discrete Wavelet
Transform coefficients in 4x4 blocks on the cover image. The optimal pixel adjustment process is applied after
embedding the message. We utilize the frequency domain to improve the robustness of steganography and, we
implement Genetic Algorithm and Optimal Pixel Adjustment Process to obtain an optimal mapping function to
reduce the difference error between the cover and the stego-image, therefore improving the hiding capacity with
low distortions. Our Simulation results reveal that the novel scheme outperforms adaptive steganography technique
based on wavelet transform in terms of peak signal to noise ratio and capacity, 39.94 dB and 50% respectively.
A Pattern Classification Based approach for Blur Classificationijeei-iaes
Blur type identification is one of the most crucial step of image restoration. In case of blind restoration of such images, it is generally assumed that the blur type is known prior to restoration of such images. However, it is not practical in real applications. So, blur type identification is extremely desirable before application of blind restoration technique to restore a blurred image. An approach to categorize blur in three classes namely motion, defocus, and combined blur is presented in this paper. Curvelet transform based energy features are utilized as features of blur patterns and a neural network is designed for classification. The simulation results show preciseness of proposed approach.
The automotive industry requires an automated system to sort different sizes and shapes
objects, images which are the mainly used component in the industry, to improve the overall
productivity. There are things at which humans are still way ahead of the machines in terms of
efficiency one of such thing is the recognition especially pattern recognition. There are several
methods which are tested for giving the machines the intelligence in efficient way for pattern
recognition purpose. The artificial neural network is one of the most optimization techniques used
for training the networks for efficient recognition. Computer vision is the science and technology of
machines that can see. The machine is made by integration of many parts to extract information from
an image in order to solve some task. Principle component analysis is a technique that will be
suitably used for the application purpose for sorting, inspection, fault diagnosis in various field.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Reconstructing the Path of the Object based on Time and Date OCR in Surveilla...ijtsrd
The inclusion of time based queries in video indexing application is enables by the recognition of time and date stamps in CCTV video. In this paper, we propose the system for reconstructing the path of the object in surveillance cameras based on time and date optical character recognition system. Since there is no boundary in region for time and date, Discrete Cosine Transform DCT method is applied in order to locate the region area. After the region for time and date is located, it is segmented and then features for the symbols of the time and date are extracted. Back propagation neural network is used for recognition of the features and then stores the result in the database. By using the resulted database, the system reconstructs the path for the object based on time. The proposed system will be implemented in MATLAB. Pyae Phyo Thu | Mie Mie Tin | Ei Phyu Win | Cho Thet Mon "Reconstructing the Path of the Object based on Time and Date OCR in Surveillance System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27981.pdfPaper URL: https://www.ijtsrd.com/home-science/education/27981/reconstructing-the-path-of-the-object-based-on-time-and-date-ocr-in-surveillance-system/pyae-phyo-thu
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Review and comparison of tasks scheduling in cloud computingijfcstjournal
Recently, there has been a dramatic increase in the popularity of cloud computing systems that rent
computing resources on-demand, bill on a pay-as-you-go basis, and multiplex many users on the same
physical infrastructure. It is a virtual pool of resources which are provided to users via Internet. It gives
users virtually unlimited pay-per-use computing resources without the burden of managing the underlying
infrastructure. One of the goals is to use the resources efficiently and gain maximum profit. Scheduling is a
critical problem in Cloud computing, because a cloud provider has to serve many users in Cloud
computing system. So scheduling is the major issue in establishing Cloud computing systems. The
scheduling algorithms should order the jobs in a way where balance between improving the performance
and quality of service and at the same time maintaining the efficiency and fairness among the jobs. This
paper introduces and explores some of the methods provided for in cloud computing has been scheduled.
Finally the waiting time and time to implement some of the proposed algorithm is evaluated
Fast Motion Estimation for Quad-Tree Based Video Coder Using Normalized Cross...CSCJournals
Motion estimation is the most challenging and time consuming stage in block based video codec. To reduce the computation time, many fast motion estimation algorithms were proposed and implemented. This paper proposes a quad-tree based Normalized Cross Correlation (NCC) measure for obtaining estimates of inter-frame motion. The measure operates in frequency domain using FFT algorithm as the similarity measure with an exhaustive full search in region of interest. NCC is a more suitable similarity measure than Sum of Absolute Difference (SAD) for reducing the temporal redundancy in video compression since we can attain flatter residual after motion compensation. The degrees of homogeneous and stationery regions are determined by selecting suitable initial fixed threshold for block partitioning. An experimental result of the proposed method shows that actual numbers of motion vectors are significantly less compared to existing methods with marginal effect on the quality of reconstructed frame. It also gives higher speed up ratio for both fixed block and quad-tree based motion estimation methods.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Artificial neural networks (ANN) consider classification as one of the most dynamic research and
application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by
back propagation algorithm. The different combinations of functions and its effect while using ANN as a
classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The
back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification
with suitable combination of training, learning and transfer functions. When the maximum likelihood
method was compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible.
Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to
be more effective than other classification algorithms.
Parasitic Boost Circuit for Transform Less Active Voltage Quality RegulatorIJMTST Journal
The voltage sag compensator, based on a series-connected voltage-source inverter, is among the most cost-effective solution against voltage sags. When voltage sags happen, the transformers, which are often installed in front of critical loads for electrical isolation, are exposed to the disfigured voltages and a dc offset will occur in its flux linkage. In this paper, a new topology of series-connected compensator is presented to mitigate long duration deep sags, and the compensation ability is highly improved with a unique shunt converter structure acting as a parasitic boost circuit that has been theoretically analyzed using open loop & closed loop control schemes. Additionally, the proposed active voltage quality regulator is a cost effective solution for long duration sags that are lower than 50% of the nominal voltage as it is transformer less compared with the traditional dynamic voltage restorer. a new topology of series-connected compensator is presented to mitigate long duration deep sags, and the compensation ability is highly improved with a unique shunt converter structure acting as a parasitic boost circuit that has been theoretically analyzed.
PCA & CS based fusion for Medical Image FusionIJMTST Journal
Compressive sampling (CS), also called Compressed sensing, has generated a tremendous amount of excitement in the image processing community. It provides an alternative to Shannon/ Nyquist sampling when the signal under acquisition is known to be sparse or compressible. In this paper, we propose a new efficient image fusion method for compressed sensing imaging. In this method, we calculate the two dimensional discrete cosine transform of multiple input images, these achieved measurements are multiplied with sampling filter, so compressed images are obtained. we take inverse discrete cosine transform of them. Finally, fused image achieves from these results by using PCA fusion method. This approach also is implemented for multi-focus and noisy images. Simulation results show that our method provides promising fusion performance in both visual comparison and comparison using objective measures. Moreover, because this method does not need to recovery process the computational time is decreased very much.
A Resonant Converter with LLC for DC-to-DC Converter Based ApplicationsIJMTST Journal
Conventional voltage mode control only offers limited performance for LLC series resonant DC-to-DC converters experiencing wide variations in operational conditions. When the existing voltage mode control is employed, the closed-loop performance of the converter is directly affected by unavoidable changes in power stage dynamics. Thus, a specific control design optimized at one particular operating point could become unacceptable when the operational condition is varied. This paper presents a new current mode control scheme which could consistently provide good closed-loop performance for LLC resonant converters for the entire operational range. The proposed control scheme employs an additional feedback from the current of the resonant tank network to overcome the limitation of the existing voltage mode control. The superiority of the proposed current mode control over the conventional voltage mode control is verified using an experimental 150 W LLC series resonant DC-to-DC converter.
Coal mine accidents have been freak and steps taken to avoid them by the government is immense, yet cannot be controlled drastically. We have designed a module that can prevent these accidents. The aim of this project is to design an investigative study on underground Mine workers safety & security measures. The application software is well equipped with monitoring and controlling the coal mine region with the relay. Thus the manual intervention is totally avoided in this project.
Implementation of Back-Propagation Neural Network using Scilab and its Conver...IJEEE
Artificial neural network has been widely used for solving non-linear complex tasks. With the development of computer technology, machine learning techniques are becoming good choice. The selection of the machine learning technique depends upon the viability for particular application. Most of the non-linear problems have been solved using back propagation based neural network. The training time of neural network is directly affected by convergence speed. Several efforts are done to improve the convergence speed of back propagation algorithm. This paper focuses on the implementation of back-propagation algorithm and an effort to improve its convergence speed. The algorithm is written in SCILAB. UCI standard data set is used for analysis purposes. Proposed modification in standard backpropagation algorithm provides substantial improvement in the convergence speed.
Body Temperature & Blood Pressure Remote MonitoringIJMTST Journal
In this paper we present an electronic system to perform a non-invasive measurement of the blood pressure based on the oscillometric method, which does not suffer from the limitations of the well-known auscultatory one. With reference to other similar devices, a great improvement of our measurement system is achieved since it performs the transmission of the systolic and diastolic pressure values to a remote computer. This aspect is very important when the simultaneous monitoring of multi-patients is required. Blood pressure readings with help of developed algorithm has been calculated and transmitted via Bluetooth kit to the stationary computer. Numerical reading values of systolic and diastolic blood pressure remotely recorded and displayed with help of LCD as well stationary computer.
Final presentation for Ordinance Survey sponsored MSc ProjectIris Kramer
MSc Archaeological Computing (GIS and Survey), University of Southampton.
“An archaeological reaction to the remote sensing data explosion. Reviewing the research on semi-automated pattern recognition and assessing the potential to integrate artificial intelligence”
Classification of Osteoporosis using Fractal Texture FeaturesIJMTST Journal
In our proposed method an automatic Osteoporosis classification system is developed. The input of the system is Lumbar spine digital radiograph, which is subjected to pre-processing which includes conversion of grayscale image to binary image and enhancement using Contrast Limited Adaptive Histogram Equalization technique(CLAHE). Further Fractal Texture features(SFTA) are extracted, then the image is classified as Osteoporosis, Osteopenia and Normal using a Probabilistic Neural Network(PNN). A total of 158 images have been used, out of which 86 images are used for training the network and 32 images for testing and 40 images for validation. The network is evaluated using a confusion matrix and evaluation parameters like Sensitivity, Specificity, precision and Accuracy are computed fractal feature extraction techniques.
The greenhouse based modern agriculture of industries are the recent requirement in every part agriculture in India. In this technology, the humidity and temperature of plants are precisely controlled. Due to the variable atmospheric circumstances these conditions sometimes may vary from place to place in large farmhouse, which makes very difficult to maintain the uniformity at all the places in the farmhouse manually. It is observed that for the first time an android phone-control the Irrigation system, which could give the facilities of maintaining uniform environmental conditions are proposed. The Android Software Development Kit provides the tools and Application Programmable Interface necessary to begin developing applications on the Android platform using the Java programming language. Mobile phones have almost become an integral part of human life serving multiple needs of humans. This application makes use of the GPRS [General Packet Radio Service] feature of mobile phone as a solution for irrigation control system. GSM (Global System for Mobile Communication) is used to inform the user about the exact field condition. The information is passed onto the user request in the form of SMS. The concept of anti theft security is used It hence deters thieves from committing the theft. It also effectively prevents stealing of motor wires and it has been a persisting problem around the fields and greater challenge to the farmer.
Techniques for Improving BER and SNR in MIMO Antenna for Optimum PerformanceIJMTST Journal
The use of multiple antennas for diversity, including MIMO (Multiple Input Multiple Output) is one of the most promising wireless technologies for broadband communication applications. This antenna system is a vital study in today’s wireless communication system especially when the signal propagates through some corrupted environments. In our paper new techniques of improving bit error ratio and signal to noise ratio are discussed. Inter symbol interference is a major limitation of wireless communications. It degrades the performance significantly if the delay spread is comparable or higher than the symbol duration. To remove ISI, equalization needs to be included at the receiver end. This paper discusses the merits of the MIMO system and the techniques used for improving BER performance and SNR. In MIMO wireless communication, an equalizer is used to recover a signal that suffers from Inter symbol Interference (ISI) and the BER characteristics is improved and a good SNR can be obtained. Different equalization techniques are discussed in this paper.
Inverter Design using PV System Boost ConverterIJMTST Journal
Many types of renewable energy, such as photovoltaic (PV), wind, tidal, and geothermal energy, have attracted a lot of attention over the past decade. Among these natural resources, the PV energy is a main and appropriate renewable energy for low-voltage dc-distribution systems, owing to the merits of clean, quiet, pollution free, and abundant. In the dc-distribution applications, a power system, including renewable distributed generators (DGS), dc loads (lighting, air conditioner, and electric vehicle), and a bidirectional inverter, is shown in fig. 1,in which two PV arrays with two maximum power point trackers (MPPTS) are implemented. However, the I–V characteristics of the PV arrays are nonlinear, and they require MPPTS to draw the maximum power from each PV array. Moreover, the bidirectional inverter has to fulfill grid connection (sell power) and rectification (buy power)with power-factor correction (PFC) to control the power flow between dc bus and ac grid,and to regulate the dc bus to a certain range of voltages, such as 380± 10 v.
A New Topology for Power Quality Improvement using 3-Phase 4-Wire UPQC with R...IJMTST Journal
This paper introduces a new concept of optimal utilization of a Unified power quality conditioner (UPQC).
The series inverter of UPQC is controlled to perform simultaneous Methods: voltage sag/swell compensation
and load reactive power sharing with the shunt inverter. The active power control approach is used to
compensate voltage sag/swell and is integrated with theory of power angle control (PAC) of UPQC to
coordinate the load reactive power between the two inverters. MATLAB/SIMULINK-based simulation results
are discussed to support the developed concept. Finally, the proposed UPQC concept is validated.
An Effective Approach for Colour Image Transmission using DWT Over OFDM for B...IJMTST Journal
Image transmission over the fading channels without degrading the perceptual quality is a challenging task while mitigating the power consumption in many fields such as broadband networks, mobile communications, Image sharing and video broadcasting. Also, it is not possible to resend the lost packets every time in many applications such as video broadcasting. Here, an effective approach for color image transmission has been proposed with power saving approach over OFDM system. Experimental results shows that the reception quality of received image is good enough with various peak signal to noise ratios also saved 60% of energy.
This paper presents a theoretical result in the context of realizing high-speed hardware for parallel CRC checksums. Starting from the serial implementation widely reported in the literature, we have identified a recursive formula from the degree of the polynomial generator. Last, we from which our parallel implementation is derived. In comparison with previous works, the new scheme is faster and more compact and is independent of the technology used in its realization. In our solution, the number of bits processed in parallel can be different have also developed high-level parametric codes that are capable of generating the circuits autonomously when only the polynomial is given.
VLSI Implementation of 32-Bit Unsigned Multiplier Using CSLA & CLAAIJMTST Journal
In this project we are going to compare the performance of different adders implemented to the multipliers based on area and time needed for calculation. The CLAA based multiplier uses the delay time of 99ns for performing multiplication operation where as in CSLA based multiplier also uses nearly the same delay time for multiplication operation. But the area needed for CLAA multiplier is reduced to 31 % by the CSLA based multiplier to complete the multiplication operation.
Traffic Density Control and Accident Indicator Using WSNIJMTST Journal
Now a day’s many of the things get controlled automatically. Everything is getting controlled using the mechanical or the automated systems. In every field machines are doing the human works. But still some area is controlled manually. For example traffic controls, road control, parking controlling. Keeping these things in mind we are trying to develop the project to automate the traffic tracking for the square. To make any project more useful and acceptable by any organization we need to provide multiple features in a single project. Keeping these things in consideration proposed system is less with multiple methodologies which can be used in traffic control system It is important to know the road traffic density real time especially in mega cities for signal control and effective traffic management. In recent years, video monitoring and surveillance systems have been widely used in traffic management. Hence, traffic density estimation and vehicle classification can be achieved using video monitoring systems. In most vehicle detection methods in the literature, only the detection of vehicles in frames of the given video is emphasized. However, further analysis is needed in order to obtain the useful information for traffic management such as real time traffic density and number of vehicle types passing these roads. This paper presents emergency vehicle alert and traffic density calculation methods using IR and GPS
A NOVEL APPROACH FOR SEGMENTATION OF SECTOR SCAN SONAR IMAGES USING ADAPTIVE ...ijistjournal
The SAR and SAS images are perturbed by a multiplicative noise called speckle, due to the coherent nature of the scattering phenomenon. If the background of an image is uneven, the fixed thresholding technique is not suitable to segment an image using adaptive thresholding method. In this paper a new Adaptive thresholding method is proposed to reduce the speckle noise, preserving the structural features and textural information of Sector Scan SONAR (Sound Navigation and Ranging) images. Due to the massive proliferation of SONAR images, the proposed method is very appealing in under water environment applications. In fact it is a pre- treatment required in any SONAR images analysis system. The results obtained from the proposed method were compared quantitatively and qualitatively with the results obtained from the other speckle reduction techniques and demonstrate its higher performance for speckle reduction in the SONAR images.
A NOVEL APPROACH FOR SEGMENTATION OF SECTOR SCAN SONAR IMAGES USING ADAPTIVE ...ijistjournal
The SAR and SAS images are perturbed by a multiplicative noise called speckle, due to the coherent nature of the scattering phenomenon. If the background of an image is uneven, the fixed thresholding technique is not suitable to segment an image using adaptive thresholding method. In this paper a new Adaptive thresholding method is proposed to reduce the speckle noise, preserving the structural features and textural information of Sector Scan SONAR (Sound Navigation and Ranging) images. Due to the massive proliferation of SONAR images, the proposed method is very appealing in under water environment applications. In fact it is a pre- treatment required in any SONAR images analysis system. The results obtained from the proposed method were compared quantitatively and qualitatively with the results obtained from the other speckle reduction techniques and demonstrate its higher performance for speckle reduction in the SONAR images.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Analysis and Comparison of Quality Index Using Clustering Techniques for S...CSCJournals
In this paper, the proposed approach consists of mainly three important steps: preprocessing, gridding and segmentation of micro array images. Initially, the microarray image is preprocessed using filtering and morphological operators and it is given for gridding to fit a grid on the images using hill-climbing algorithm. Subsequently, the segmentation is carried out using the fuzzy c-means clustering. Initially the enhanced fuzzy c-means clustering algorithm (EFCMC) is implemented to effectively clustering the image whether the image may be affected by the noises or not. Then, the EFCM method was employed the real microarray images and noisy microarray images in order to investigate the efficiency of the segmentation. Finally, the segmentation efficiency of the proposed approach was compared with the various algorithms in terms of quality index and the obtained results ensures that the performance efficiency of the proposed algorithm was improved in term of quality index rather than other algorithms.
An Assessment of Image Matching Algorithms in Depth EstimationCSCJournals
Computer vision is often used with mobile robot for feature tracking, landmark sensing, and obstacle detection. Almost all high-end robotics systems are now equipped with pairs of cameras arranged to provide depth perception. In stereo vision application, the disparity between the stereo images allows depth estimation within a scene. Detecting conjugate pair in stereo images is a challenging problem known as the correspondence problem. The goal of this research is to assess the performance of SIFT, MSER, and SURF, the well known matching algorithms, in solving the correspondence problem and then in estimating the depth within the scene. The results of each algorithm are evaluated and presented. The conclusion and recommendations for future works, lead towards the improvement of these powerful algorithms to achieve a higher level of efficiency within the scope of their performance.
ABSTRACT Feature extraction plays a vital role in the analysis and interpretation of remotely sensed data. The two important components of Feature extraction are Image enhancement and information extraction. Image enhancement techniques help in improving the visibility of any portion or feature of the image. Information extraction techniques help in obtaining the statistical information about any particular feature or portion of the image. This presented work focuses on the various feature extraction techniques and area of optical character recognition is a particularly important in Image processing. Keywords— Image character recognition, Methods for Feature Extraction, Basic Gabor Filter, IDA, and PCA.
AUTOMATIC TARGET DETECTION IN HYPERSPECTRAL IMAGES USING NEURAL NETWORKijistjournal
Spectral analysis of remotely sensed images provide the required information accurately even for small targets. Hence Hyperspectral imaging is being used which follows the technique of dividing images into bands. These Hyperspectral images find their applications in agriculture, biomedical, marine analysis, oil seeps detection etc. A Hyperspectral image contains many spectra, one for each individual point on the sample’s surface and in this project the required target on the Hyperspectral image is going to be detected and classified. Hyperspectral remote sensing image classification is a challenging problem because of its high dimensional inputs, many class outputs and limited availability of reference data. Therefore some powerful techniques to improve the accuracy of classification are required. The objective of our project is to reduce the dimensionality of the Hyperspectral image using Principal Component Analysis followed by classification using Neural Network. The project is to be implemented using MATLAB.
Spectral analysis of remotely sensed images provide the required information accurately even for small
targets. Hence Hyperspectral imaging is being used which follows the technique of dividing images into
bands. These Hyperspectral images find their applications in agriculture, biomedical, marine analysis, oil
seeps detection etc. A Hyperspectral image contains many spectra, one for each individual point on the
sample’s surface and in this project the required target on the Hyperspectral image is going to be detected
and classified. Hyperspectral remote sensing image classification is a challenging problem because of its
high dimensional inputs, many class outputs and limited availability of reference data. Therefore some
powerful techniques to improve the accuracy of classification are required. The objective of our project is
to reduce the dimensionality of the Hyperspectral image using Principal Component Analysis followed by
classification using Neural Network. The project is to be implemented using MATLAB.
Medical Image Processing � Detection of Cancer Brainijcnes
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SAR Image Classification by Multilayer Back Propagation Neural Network
1. IJMTST | International Journal for Modern Trends in Science and Technology
Volume 1, Issue 2, October 2015 36
SAR Image Classification by Multilayer Back
Propagation Neural Network
Syed Asif Ali K. Venkata Ramaiah
PG Scholar, Department of CSE Assistant Professor & Head, Department of CSE
Chebrolu Engineering College, Chebrolu Chebrolu Engineering College, Chebrolu
Abstract - A novel descriptive feature extraction method of
Discrete Fourier transform and neural network classifier for
classification of Synthetic Aperture Radar (SAR) images is
proposed. The classification process has the following stages (1)
Image Segmentation using statistical Region Merging (SRM) (2)
Polar transform and Feature extraction using Discrete Fourier
Transform (3) Neural Network classification using back
propagation. This is generally the first step in image analysis.
Segmentation subdivides an image into its constituent parts or
objects. The level to which this subdivision is carried depends on
the problem being solved. The image segmentation in this study
is performed using Statistical Region Merging proposed
Richard Nock and Frank Nielsen. The key idea of the Statistical
Region Merging model is to formulate image segmentation as an
inference problem. Here the merging procedure is based on the
theorem. Feature vectors as the input for the neural network.
Polar transform is applied to segmented SAR image. The
rotation problem under the Cartesian coordinates becomes the
translation problem under the polar coordinates.
Keywords: Back-propagation algorithm, feature extraction,
MSTAR database, SAR images, SRM segmentation.
I. INTRODUCTION
There has always been a need of protecting the messages
that are sensitive in nature. Such messages if exposed to some
intruder may pose a threat to nation’s security or company’s
critical decisions. Thus, such information must be secured at
any cost and to serve the purpose there has been a trend to
encrypt or hide the secret information. Cryptography (derived
from Greek work ‘kryptos’ meaning hidden and ‘graphein’
meaning to write) is used to encode the text to make
itunderstandable. Steganography (composed of Greek word
‘steganos’, meaning covered and ‘graphein’ meaning to
write) on the other hand, is used to hide the text behind some
other media. Cryptography may draw the suspicion of the
intruder towards the text that is in encoded format.
Steganography do not lures the eavesdropper as it hides the
message. Steganography can be classified based on the type of
media it uses to hide the text[1]. These are as
follows:Synthetic Aperture Radar (SAR) is a coherent radar
system that generates high resolution remote sensing
imagery. SAR imagery is used in finding comparatively small
mobile or immobile targets for military applications. The
need is to classify the targets using the SAR images. SAR
images containing objects that are small, influenced by
speckle still requires efficient classification technique to
correctly classify objects. The foci of this study are on
providing an advanced classification techniques for SAR
images.
II. SYSTEM DESIGN MODEL
The proposed method is used to classify vehicle astank or
armed personal carrier of SAR images. Areference database is
maintained with the SAR image,which are released by
MSTAR database. In proposed method, the SAR image
classification is done by employing feature extraction
algorithm to extract the stable, repeatable and distinctive
features of the SAR image and then by matching these
features with the features of reference images. Proposed
method introduces a SAR classification method with rotation
invariance. The rotation invariance feature is represented by
the absolute valueof Fourier coefficients of polar image of the
SAR. Then SAR image can be distinguished by feeding those
features into a multi-layered BP neural network. The block
diagram of proposed method is represented in Fig. 1.
Fig. 1: Block diagram of the proposed method
2. IJMTST | International Journal for Modern Trends in Science and Technology
Volume 1, Issue 2, October 2015 37
A. Image segmentation:
In this section, a method forsegmenting the object from
the SAR image is proposed.Image segmentation is a
technique for extractinginformation from an image. This is
generally the firststep in image analysis. Segmentation
subdivides animage into its constituent parts or objects. The
level towhich this subdivision is carried depends on
theproblem being solved. The image segmentation in
thisstudy is performed using Statistical Region
Mergingproposed Richard Nock and Frank Nielsen.
Fig. 2: (a) T72-Tank (b) BMP2-armored personnel carriers (c) BTR60- armored
personnel
The keyidea of the Statistical Region Merging model is
toformulate image segmentation as an inference
problem.Here the merging procedure is based on the
theorem‘The independent bounded difference inequality’. It
isthe reconstruction of regions on the observed image,based
on image on which the true regions we seek arestatistical
regions whose borders are defined from asimple axiom.
Second, the model shows the existenceof a particular blend of
statistics and algorithmic toprocess observed images
generated with this model, byregion merging, with two
statistical properties. Theregions in the query image are
merged using the SRMsegmentation procedure to obtain the
object regionfrom the image. The segmentation procedure
outputwas shown in (Fig. 2) which shows the region
mergedimage and the shows the segmented output.
B. Polar transform:
The segmented SAR image isconverted from Cartesian
coordinates to logarithmicpolar coordinates. The rotation
problem under theCartesian coordinates becomes the
translation problemunder the logarithmic polar coordinates.
The imagetransform from Cartesian to logarithmic polar
coordinates is introduced.
C. Feature extraction:
Feature vectors as the input for the neural network. Polar
transform is applied to segmented SAR image. The rotation
problem under the Cartesian coordinates becomes the
translation problem under the polar coordinates. Then the
Fourier transform applied to the output of the polar
transform. Then the feature vectors will be obtained as shown
in Fig. 3.
Fig. 3: Feature vector computation
D. Fourier transform:
The output of the transformation represents the image in
the Fourier or frequency domain, while the input image is the
spatial domain equivalent. The absolute value of Fourier
coefficients will not change after the image is rotated. The
absolute values of Fourier coefficients are used as feature
vectors, then the feature vectors are rotation invariant. The
Fourier coefficients can be used to reconstruct the image
under the logarithmic polar coordinates. The number of
Fourier coefficients maybe be infinite, but with increasing
frequency, the amplitude of the coefficients will be reduced
significantly. So the Fourier coefficients above a certain
frequency can be ignored. Here fifteen co efficient used as
feature vectors.
E. Neural network training and classification:
Multilayer back propagation neural network is taken as
the network architecture for the present application. After
choosing the network, the number of neurons in each layer
has to be decided. The number of neurons in the output layer
is fixed. the neural network input is Fourier co efficient. By
applying polar transform to the segmented image and then
applying Fourier transform to that, feature vectors are
obtained. The number of hidden layers in the network and the
number of neurons in each layer is chosen by trial and error
3. IJMTST | International Journal for Modern Trends in Science and Technology
Volume 1, Issue 2, October 2015 38
method based on the performance function until it reaches the
specified goal.
III. SIMULATION RESULTS
The proposed method is required to find the feature
vectors. The database contains 3 images. BTR60, BMP2, T72
are used as the reference for experimentation. In this study,
SAR ATR experiments were performed using the MSTAR
database to classify three targets as shown in Table 1. The
image data are composed of SAR images chips roughly
centered on three types of military vehicles: the T72, BTR60
and BMP2 (the T-72 is a tank and the other two vehicles
are armored personnel carriers) as shown in Fig. 2.The
segmented image and Neural Network Analysisas shown in
Fig. 5. Based on the features extracted from the SAR images,
fifteen parameters of Fourier coefficients values are given as
input to train the network as shown in Table 2. These input
values are compared with those image feed into the network.
Then the matching is performed. The network is trained with
the parameters corresponding to three types of SAR image, to
their respective targets. After training the performance
function reaches the goal for all the samples.
Fig. 4: a) Original Image b) segmented image
Fig. 5: a) Original Image b) segmented image c) Neural Network Analysis.
4. IJMTST | International Journal for Modern Trends in Science and Technology
Volume 1, Issue 2, October 2015 39
CONCLUSION
In this study, a new SAR image classification method has
been proposed. The image of the SAR is acquired using a
MSTAR database. Then the SAR image of the BTR60,
BMP2, T72 (armored personnel carriers & Tank) has been
subjected to the segmentation process. The Image is
segmented using the Statistical Region Merging (SRM)
method. After that the feature vectors are extracted using
Fourier descriptor and it given in to the neural network.
Neural network will trained in to the three types of SAR
Images. The DWT based SYM wavelet coefficients perform
the better quality in the image with more than 35dB in Peak
Signal to Noise Ratio. The classification rate of the proposed
algorithm is around 88%. The future study is to Automatic
target recognition of SAR images with reduced time.
REFERENCES
[1] JBennamoun, J. and G.J. Mamic, 2002. Object Recognition Fundamentals
and Case Studies. Springer, ISBN: 1-85233-398-7.
[2] Daisheng, L 2005. Pattern Recognition and Image Processing. Horwood
Series in Engineering Series. ISBN-1-898563-52-7. Minoru, F., O. Sigeru,
T. Fumiaki and K. Toshihisa, 1992. Rotation invariant neural pattern
recognition system with application to coinrecognition. IEEE T. Neur.
Network., 3: 272-279, DOI: 10.1109/72.125868.
[3] Neagoe and G. Strugaru, 2008. A concurrent neural network model for
pattern recognition in multi spectral satellite imagery. Proceeding of the
World Automation Congress, 2008 (WAC 2008), International Symposium
on Soft Computing in Industry (ISSCI'08), Sept. 28-Oct, 2, Hawaii, USA,
ISBN: 978-1-889335-38-4, IEEE Catalog No. 08EX2476.
[4] Neagoe and A. Ropot, 2009. A New Neural Approach for Pattern
Recognition in Space Imagery. In: Harbour Protection through Data
[5] Fusion Technologies, NATO Science for Peace and Security Series-C:
Environmental Security, Springer, pp: 283-289.
[6] Ruohong and Y. Ruliang, 2008. SAR target recognition based on MRF and
gabor wavelet feature extraction. IEEE International
[7] Geoscience and Remote Sensing Symposium (IGARSS 2008), July 2008, 2:
2-907-/2-910.
[8] Sandirasegaram, 2002. Automaic Target Recognition in SAR Imagery
using a MLP Neural Network. Technical Memorandum, Defence Research
and Development Canada (DRDC), Ottawa, TM, 2002-1.