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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…

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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.     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…

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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…

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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…

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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.…

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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…

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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…

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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…

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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…

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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,…

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