Ieee Digital image processing projects using matlab

Ieee Digital image processing projects using matlab

Ieee Digital image processing projects using matlab

Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing. It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal distortion during processing. Since images are defined over two dimensions (perhaps more) digital image processing may be modeled in the form of multidimensional systems.

 

Ieee Digital image processing projects using matlab

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Ieee Neural network image processing based matlab projects

Ieee Neural network image processing based matlab projects

Ieee Neural network image processing based matlab projects

Artificial neural networks (ANNs) or connectionist systems are computing systems inspired by the biological neural networks that constitute animal brains. Such systems learn (progressively improve performance) to do tasks by considering examples, generally without task-specific programming. Neural network image processing is perform by matlab softaware.

 

Ieee Neural network image processing based matlab projects

 

Why use neural networks?

Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an “expert” in the category of information it has been given to analyse. This expert can then be used to provide projections given new situations of interest and answer “what if” questions.
Other advantages include:

  1. Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.
  2. Self-Organisation: An ANN can create its own organisation or representation of the information it receives during learning time.
  3. Real Time Operation: ANN computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability.
  4. Fault Tolerance via Redundant Information Coding: Partial destruction of a network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage

 

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Ieee medical image processing projects using matlab

Ieee medical image processing projects using matlab

Ieee medical image processing projects using matlab

Biomedical image processing projects using matlab. Biomedical image processing is a way of process and visual representation of inter body parts for medical analysis.Biomedical image processing is similar in concept to biomedical signal processing in multiple dimensions.

Biomedical image processing is similar in concept to biomedical signal processing in multiple dimensions. It includes the analysis, enhancement and display of images captured via x-ray, ultrasound, MRI, nuclear medicine and optical imaging technologies.

Image reconstruction and modeling techniques allow instant processing of 2D signals to create 3D images. When the original CT scanner was invented in 1972, it literally took hours to acquire one slice of image data and more than 24 hours to reconstruct that data into a single image. Today, this acquisition and reconstruction occurs in less than a second.

Rather than simply eyeball an x-ray on a lightbox, image processing software helps to automatically identify and analyze what might not be apparent to the human eye. Computerized algorithms can provide temporal and spatial analysis to detect patterns and characteristics indicative of tumors and other ailments.

Depending on the imaging technique and what diagnosis is being considered, image processing and analysis can be used to determine the diameter, volume and vasculature of a tumor or organ; flow parameters of blood or other fluids and microscopic changes that have yet to raise any otherwise discernible flags.

 

Ieee medical image processing projects using matlab

 

Some of ieee medical image processing projects using matlab are:

 

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Ieee Image cryptography based matlab projects

Ieee Image cryptography based matlab projects

Ieee Image cryptography based matlab projects

Image cryptography based matlab projects. cryptography algorithm require a set of characters called key to encrypt and decrypt data and in image cryptography we encrypt image and decrypt image by key. image cryptography is performed by matlab which is high performance language for technical computing.

How encryption works

Data, often referred to as plaintext, is encrypted using an encryption algorithm and an encryption key. This process generates ciphertext that can only be viewed in its original form if decrypted with the correct key. Decryption is simply the inverse of encryption, following the same steps but reversing the order in which the keys are applied. Today’s encryption algorithms are divided into two categories: symmetric and asymmetric.

Symmetric-key ciphers use the same key, or secret, for encrypting and decrypting a message or file. The most widely used symmetric-key cipher is AES, which was created to protect government classified information. Symmetric-key encryption is much faster than asymmetric encryption, but the sender must exchange the key used to encrypt the data with the recipient before he or she can decrypt it. This requirement to securely distribute and manage large numbers of keys means most cryptographic processes use a symmetric algorithm to efficiently encrypt data, but use an asymmetric algorithm to exchange the secret key.

Asymmetric cryptography, also known as public-key cryptography, uses two different but mathematically linked keys, one public and one private. The public key can be shared with everyone, whereas the private key must be kept secret. RSA is the most widely used asymmetric algorithm, partly because both the public and the private keys can encrypt a message; the opposite key from the one used to encrypt a message is used to decrypt it. This attribute provides a method of assuring not only confidentiality, but also the integrity, authenticity and non-reputability of electronic communications and data at rest through the use of digital signatures.

 

Ieee Image cryptography based matlab projects

Some of Image cryptography based matlab projects are:

 

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Image Processing Projects Using Matlab for PH.D|M.Tech|B.Tech

Image Processing Projects Using Matlab for PH.D|M.Tech|B.Tech

Image Processing Projects Using Matlab for PH.D|M.Tech|B.Tech. In an Image processing we perform some Mathematical operation on image. input is an image, a series of an image, video etc and output of set of character or parameter related to image and for image processing we use Matlab software. MATLAB is a high-performance language for technical computing. It integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation. Typical uses include:

  • Math and computation
  • Algorithm development
  • Modeling, simulation, and prototyping
  • Data analysis, exploration, and visualization
  • Scientific and engineering graphics
  • Application development, including Graphical User Interface building

Image Processing Projects Using Matlab for PH.D|M.Tech|B.Tech

Image cryptography based matlab projects

Image cryptography based matlab projects. cryptography algorithm require a set of characters called key to encrypt and decrypt data and in image cryptography we encrypt image and decrypt image by key. image cryptography is performed by matlab which is high performance language for technical computing.

 

Image Processing Projects Using Matlab for PH.D|M.Tech|B.Tech

 

Biomedical image processing projects using matlab

Biomedical image processing projects using matlab. Biomedical image processing is a way of process and visual representation of inter body parts for medical analysis.Biomedical image processing is similar in concept to biomedical signal processing in multiple dimensions.

Image Processing Projects Using Matlab for PH.D|M.Tech|B.Tech

 

Neural network image processing based matlab projects

Artificial neural networks (ANNs) or connectionist systems are computing systems inspired by the biological neural networks that constitute animal brains. Such systems learn (progressively improve performance) to do tasks by considering examples, generally without task-specific programming. Neural network image processing is perform by matlab softaware.

Image Processing Projects Using Matlab for PH.D|M.Tech|B.Tech

Digital image processing projects using matlab

Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing. It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal distortion during processing. Since images are defined over two dimensions (perhaps more) digital image processing may be modeled in the form of multidimensional systems.

Image Processing Projects Using Matlab for PH.D|M.Tech|B.Tech

 

 

 

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An approach to the automatic detection of weld defects in radiography films using digital image processing

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Gene selection approach based on improved swarm intelligent optimisation algorithm for tumour classification

Gene selection approach based on improved swarm intelligent optimisation algorithm for tumour classification

Gene selection approach based on improved swarm intelligent optimisation algorithm for tumour classification

Abstract:

A number of different gene selection approaches based on gene expression profiles (GEP) have been developed for tumour classification. A gene selection approach selects the most informative genes from the whole gene space, which is an important process for tumour classification using GEP. This study presents an improved swarm intelligent optimisation algorithm to select genes for maintaining the diversity of the population. The most essential characteristic of the proposed approach is that it can automatically determine the number of the selected genes. On the basis of the gene selection, the authors construct a variety of the tumour classifiers, including the ensemble classifiers. Four gene datasets are used to evaluate the performance of the proposed approach. The experimental results confirm that the proposed classifiers for tumour classification are indeed effective.
Published in: IET Systems Biology ( Volume: 10, Issue: 3, 6 2016 )
Date of Publication: 19 May 2016
ISSN Information:
INSPEC Accession Number: 15987211
Publisher: IET

What we provide:

  • Complete Research Assistance

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  • MATLAB, Simulink, MATPOWER, GRIDLAB-D,OpenDSS, ETAP, GAMS

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Discriminative Low-Rank Gabor Filtering for Spectral–Spatial Hyperspectral Image Classification

Discriminative Low-Rank Gabor Filtering for Spectral–Spatial Hyperspectral Image Classification

Discriminative Low-Rank Gabor Filtering for Spectral–Spatial Hyperspectral Image Classification

Abstract:

Spectral-spatial classification of remotely sensed hyperspectral images has attracted a lot of attention in recent years. Although Gabor filtering has been used for feature extraction from hyperspectral images, its capacity to extract relevant information from both the spectral and the spatial domains of the image has not been fully explored yet. In this paper, we present a new discriminative low-rank Gabor filtering (DLRGF) method for spectral-spatial hyperspectral image classification. A main innovation of the proposed approach is that our implementation is accomplished by decomposing the standard 3-D spectral-spatial Gabor filter into eight subfilters, which correspond to different combinations of low-pass and bandpass single-rank filters. Then, we show that only one of the subfilters (i.e., the one that performs low-pass spatial filtering and bandpass spectral filtering) is actually appropriate to extract suitable features based on the characteristics of hyperspectral images. This allows us to perform spectral-spatial classification in a highly discriminative and computationally efficient way, by significantly decreasing the computational complexity (from cubic to linear order) compared with the 3-D spectral-spatial Gabor filter. In order to theoretically prove the discriminative ability of the selected subfilter, we derive an overall classification risk bound to evaluate the discriminating abilities of the features provided by the different subfilters. Our experimental results, conducted using different hyperspectral images, indicate that the proposed DLRGF method exhibits significant improvements in terms of classification accuracy and computational performance when compared with the 3-D spectral-spatial Gabor filter and other state-of-the-art spectral-spatial classification methods.
Published in: IEEE Transactions on Geoscience and Remote Sensing ( Volume: 55, Issue: 3, March 2017 )
Date of Publication: 02 December 2016
ISSN Information:
INSPEC Accession Number: 16707533
Publisher: IEEE

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Complete Research Assistance

Technology Involved:-

MATLAB, Simulink, MATPOWER, GRIDLAB-D,OpenDSS, ETAP, GAMS

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HEp-2 Cell Image Classification With Deep Convolutional Neural Networks

HEp-2 Cell Image Classification With Deep Convolutional Neural Networks

HEp-2 Cell Image Classification With Deep Convolutional Neural Networks

Abstract:

Efficient Human Epithelial-2 cell image classification can facilitate the diagnosis of many autoimmune diseases. This paper proposes an automatic framework for this classification task, by utilizing the deep convolutional neural networks (CNNs) which have recently attracted intensive attention in visual recognition. In addition to describing the proposed classification framework, this paper elaborates several interesting observations and findings obtained by our investigation. They include the important factors that impact network design and training, the role of rotation-based data augmentation for cell images, the effectiveness of cell image masks for classification, and the adaptability of the CNN-based classification system across different datasets. Extensive experimental study is conducted to verify the above findings and compares the proposed framework with the well-established image classification models in the literature. The results on benchmark datasets demonstrate that 1) the proposed framework can effectively outperform existing models by properly applying data augmentation, 2) our CNN-based framework has excellent adaptability across different datasets, which is highly desirable for cell image classification under varying laboratory settings. Our system is ranked high in the cell image classification competition hosted by ICPR 2014.
Published in: IEEE Journal of Biomedical and Health Informatics ( Volume: 21, Issue: 2, March 2017 )
Date of Publication: 08 February 2016
ISSN Information:
PubMed ID: 26887016
INSPEC Accession Number: 16721742
Publisher: IEEE

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MATLAB, Simulink, MATPOWER, GRIDLAB-D,OpenDSS, ETAP, GAMS

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Dimensionality Reduction and Classification of Hyperspectral Images Using Ensemble Discriminative Local Metric Learning

Dimensionality Reduction and Classification of Hyperspectral Images Using Ensemble Discriminative Local Metric Learning

Dimensionality Reduction and Classification of Hyperspectral Images Using Ensemble Discriminative Local Metric Learning

Abstract:

The high-dimensional data space of hyperspectral images (HSIs) often result in ill-conditioned formulations, which finally leads to many of the high-dimensional feature spaces being empty and the useful data existing primarily in a subspace. To avoid these problems, we use distance metric learning for dimensionality reduction. The goal of distance metric learning is to incorporate abundant discriminative information by reducing the dimensionality of the data. Considering that global metric learning is not appropriate for all training samples, this paper proposes an ensemble discriminative local metric learning (EDLML) algorithm for HSI analysis. The EDLML algorithm learns robust local metrics from both the training samples and the relative neighborhood of them and considers the different local discriminative distance metrics by dealing with the data region by region. It aims to learn a subspace to keep all the samples in the same class are as near as possible, while those from different classes are separated. The learned local metrics are then used to build an ensemble metric. Experiments on a number of different hyperspectral data sets confirm the effectiveness of the proposed EDLML algorithm compared with that of the other dimension reduction methods.
Published in: IEEE Transactions on Geoscience and Remote Sensing ( Volume: 55, Issue: 5, May 2017 )
Date of Publication: 17 January 2017
ISSN Information:
INSPEC Accession Number: 16756606
Publisher: IEEE

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Complete Research Assistance

Technology Involved:-

MATLAB, Simulink, MATPOWER, GRIDLAB-D,OpenDSS, ETAP, GAMS

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Remote Sensing Image Classification: A survey of support-vector-machine-based advanced techniques

Remote Sensing Image Classification: A survey of support-vector-machine-based advanced techniques

Remote Sensing Image Classification: A survey of support-vector-machine-based advanced techniques

Abstract:

Land-cover mapping in remote sensing (RS) applications renders rich information for decision support and environmental monitoring systems. The derivation of such information increasingly relies on robust classification methods for identifying the complex land-cover area of different categories. Numerous classification techniques have been designed for the analysis of RS imagery. In this context, support vector machines (SVMs) have recently received increasing interest. However, the need for a small-size training set remains a bottleneck to design efficient supervised classifiers, while an adequate number of unlabeled data is readily available in RS images and can be exploited as a supplementary source of information. To fully leverage these precious unlabeled data, a number of promising advanced SVM-based methods, such as active SVMs, semisupervised SVMs (S3VMs), and SVMs combined with other algorithms, have been developed to analyze satellite imagery. In this literature review, we have surveyed these learning techniques to explore RS images. Moreover, we have provided the empirical evidences of SVMs and three representative techniques. It is our hope that this review will provide guidelines to future researchers to enhance further algorithmic developments in RS applications.
Published in: IEEE Geoscience and Remote Sensing Magazine ( Volume: 5, Issue: 1, March 2017 )
Date of Publication: 20 March 2017
ISSN Information:
INSPEC Accession Number: 16759674
Publisher: IEEE

What we provide:

Complete Research Assistance

Technology Involved:-

MATLAB, Simulink, MATPOWER, GRIDLAB-D,OpenDSS, ETAP, GAMS

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