IEEE 2014 DOTNET IMAGE PROCESSING PROJECTS Image classification using multiscale information fusion based on saliency driven nonlinear diffusion filtering
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
This document proposes a convolutional neural network (CNN) to automatically classify aerial and remote sensing images. The CNN has six layers - three convolutional layers to extract visual features from the images at different levels of abstraction, two fully-connected layers to integrate the extracted features, and a final softmax classifier layer to classify the images. The CNN is evaluated on two datasets and is shown to outperform state-of-the-art baselines in terms of classification accuracy, demonstrating its ability to learn spatial features directly from images without relying on handcrafted features or descriptors.
JPM1414 Progressive Image Denoising Through Hybrid Graph Laplacian Regulariz...chennaijp
JP INFOTECH is one of the leading Matlab projects provider in Chennai having experience faculties. We have list of image processing projects as our own and also we can make projects based on your own base paper concept also.
For more details:
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The document discusses several studies that use different neural network models to classify dementia stages from MRI and PET scan images. A DEMNET model uses CNN to detect Alzheimer's characteristics from MRI scans. A modified LeNet model uses min pooling and max pooling layers concatenated together to better retain spatial information. A capsule network technique classifies dementia groups by considering minor details unlike pooling layers in CNNs. A 3D-CNN and FSBi-LSTM framework extracts features from MRI and PET scans to improve diagnosis. A divNet architecture is proposed and tested for its effectiveness in terms of memory usage, parameters, runtime, and error rates for Alzheimer's prediction.
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Adaptive metric learning for saliency detectionSuresh Nagalla
This document proposes using generic metric learning (GML) and specific metric learning (SML) to more efficiently detect salient objects in images compared to existing approaches. It suggests learning two complementary Mahalanobis distance metrics - GML to model global training data distributions and SML to capture image-specific structures - rather than relying on Euclidean distance measures alone. This adaptive metric learning approach aims to generate keys for pixels and allow for easier pixel matching compared to previous methods.
This document discusses band ratioing, image differencing, and principal and canonical component analysis techniques in remote sensing. Band ratioing involves dividing pixel values in one band by another band to enhance spectral differences. Image differencing calculates differences between images after alignment. Principal component analysis transforms correlated spectral data into fewer uncorrelated bands retaining most information, while canonical component analysis aims to maximize separability of user-defined features. These techniques can help analyze multispectral and hyperspectral remote sensing data.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
This document proposes a convolutional neural network (CNN) to automatically classify aerial and remote sensing images. The CNN has six layers - three convolutional layers to extract visual features from the images at different levels of abstraction, two fully-connected layers to integrate the extracted features, and a final softmax classifier layer to classify the images. The CNN is evaluated on two datasets and is shown to outperform state-of-the-art baselines in terms of classification accuracy, demonstrating its ability to learn spatial features directly from images without relying on handcrafted features or descriptors.
JPM1414 Progressive Image Denoising Through Hybrid Graph Laplacian Regulariz...chennaijp
JP INFOTECH is one of the leading Matlab projects provider in Chennai having experience faculties. We have list of image processing projects as our own and also we can make projects based on your own base paper concept also.
For more details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/matlab-projects/
The document discusses several studies that use different neural network models to classify dementia stages from MRI and PET scan images. A DEMNET model uses CNN to detect Alzheimer's characteristics from MRI scans. A modified LeNet model uses min pooling and max pooling layers concatenated together to better retain spatial information. A capsule network technique classifies dementia groups by considering minor details unlike pooling layers in CNNs. A 3D-CNN and FSBi-LSTM framework extracts features from MRI and PET scans to improve diagnosis. A divNet architecture is proposed and tested for its effectiveness in terms of memory usage, parameters, runtime, and error rates for Alzheimer's prediction.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601
Adaptive metric learning for saliency detectionSuresh Nagalla
This document proposes using generic metric learning (GML) and specific metric learning (SML) to more efficiently detect salient objects in images compared to existing approaches. It suggests learning two complementary Mahalanobis distance metrics - GML to model global training data distributions and SML to capture image-specific structures - rather than relying on Euclidean distance measures alone. This adaptive metric learning approach aims to generate keys for pixels and allow for easier pixel matching compared to previous methods.
This document discusses band ratioing, image differencing, and principal and canonical component analysis techniques in remote sensing. Band ratioing involves dividing pixel values in one band by another band to enhance spectral differences. Image differencing calculates differences between images after alignment. Principal component analysis transforms correlated spectral data into fewer uncorrelated bands retaining most information, while canonical component analysis aims to maximize separability of user-defined features. These techniques can help analyze multispectral and hyperspectral remote sensing data.
Convolutional neural network (CNN / ConvNet's) is a part of Computer Vision. Machine Learning Algorithm. Image Classification, Image Detection, Digit Recognition, and many more. https://technoelearn.com .
This document describes a new technique for generating 3D numeric breast phantoms from MRI data for use in microwave imaging simulations. The technique uses semi-automated segmentation algorithms to translate MR images into voxel-based surface meshes representing breast tissue structures. These patient-specific models improve on previous manual methods. The models were validated using a custom multi-modal phantom to test the program's ability to accurately reconstruct complex breast geometries. The resulting phantoms provide realistic models needed to advance the investigation of microwave imaging for breast cancer detection.
This document is an assignment submission for a data communication course. It summarizes key topics in data communication including the components of a data communication system, different modes of data flow, and common network topologies. The modes of data flow are simplex, half-duplex, and full-duplex. The common network topologies discussed are mesh, star, bus, ring, and tree with their advantages and disadvantages.
Pixel Recursive Super Resolution.
Ryan Dahl, Mohammad Norouzi & Jonathon Shlens
Google Brain.
Abstract
We present a pixel recursive super resolution model that
synthesizes realistic details into images while enhancing
their resolution. A low resolution image may correspond
to multiple plausible high resolution images, thus modeling
the super resolution process with a pixel independent conditional
model often results in averaging different details–
hence blurry edges. By contrast, our model is able to represent
a multimodal conditional distribution by properly modeling
the statistical dependencies among the high resolution
image pixels, conditioned on a low resolution input. We
employ a PixelCNN architecture to define a strong prior
over natural images and jointly optimize this prior with a
deep conditioning convolutional network. Human evaluations
indicate that samples from our proposed model look
Vectors are based on geometric elements like points, lines, and shapes defined by mathematical expressions. They represent images using control points on the x and y axes. Properties like color and thickness don't increase file size.
Bitmaps map domains like pixels to binary values of 0 or 1, representing black and white images. Pixmaps store more colors using more bits per pixel.
Digital cameras and scanners capture images digitally which are then processed into formats like JPEGs of various sizes for web or full screen display. Factors like lens quality, focus, noise, and dynamic range impact image quality.
Image file formats use vector data, pixels, or mixtures to store and organize graphics. File size increases
Non negative matrix factorization ofr tuor classificationSahil Prajapati
The PPT aware about you the concept of Non Negative Matrix Factorization and how theses techniques can be used to treat cancer by the use of the coding such as a MATLAB,LABVIEW software to locate the tumor or the cancer part with the different approaches and tachniques.
Go through the PPT to know and how one can improvise my work for better results??
Please help me if one come up with other techniques.
Supervised and unsupervised classification techniques for satellite imagery i...gaup_geo
This document compares supervised and unsupervised classification techniques for satellite imagery analysis of land cover in the Porto Alegre region of Brazil. Supervised classification involved collecting over 500 training sites to create signatures for 8 land cover classes. Unsupervised classification used ISOcluster to generate 36 spectral classes which were grouped into the 8 informational classes. Both classifications underwent post-processing including majority filtering and polygon elimination to produce final 1-hectare minimum mapping unit vector maps. Accuracy assessments found the supervised classification to be more accurate at 76% compared to 48% for the unsupervised method.
Explainable deep learning with applications in Healthcare By Sunil Kumar Vupp...Analytics India Magazine
We started relying on the decisions made by deep learning models, however why it works and how it works are still big questions for most of us. We shall try to open that black box of deep learning which is essential to build trust for wide spread adoption. The speaker shall address the importance of feature visualization and localization in deep learning models esp. convolutional neural networks. He shares the results of applying methods such as activation map, deconvolution and Grad-CAM in healthcare.
Human brain is the most complex structure where identifying the tumor like diseases are extremely challenging because differentiating the components of a brain is complex. In this paper, pillar k-means algorithm is used for segmentation of brain tumor from magnetic resonance image (MRI).Generally, the brain tumor is detected by radiologist through analysis of MR images which takes longer time. The pillar k-means algorithm’s experimental results clarify the effectiveness of our approach to improve the segmentation quality, accuracy, and computational time. Classify, the tumor from the brain MR images using Bayesian classification.
This document provides an overview of the application of remote sensing and geographical information systems in civil engineering. It discusses key concepts such as image interpretation, data preprocessing, feature extraction, image classification, and accuracy assessment. The document aims to explain how remote sensing and GIS techniques can be used to extract useful information from imagery and geospatial data for civil engineering applications.
it is highly useful for geography students in the field of remote sensing and it is in very simple and explanatory for the purpose of simplification with relevant images in this ppt.
This document describes a method for pixel-level image fusion using principal component analysis (PCA). PCA is used to transform correlated image pixels into a set of uncorrelated principal components. The first principal component accounts for the most variance in the pixel values. To fuse images, the pixels of the input images are arranged into vectors and subtracted from their mean. PCA is applied to get the eigenvectors corresponding to the largest eigenvalues. The normalized eigenvectors are used to compute a fused image as a weighted sum of the input images. Performance is evaluated using metrics like standard deviation, entropy, cross-entropy, and fusion mutual information, with higher values of these metrics indicating better quality of the fused image.
This document outlines several papers on unsupervised and semi-supervised object detection. It describes approaches such as using unbiased teachers to generate pseudo-labels for semi-supervised learning. It also discusses contrastive learning methods to learn representations from unlabeled data as well as consistency-based active learning and self-training frameworks. The papers covered include approaches for domain adaptation, open world detection of unknown objects, and monocular 3D object detection through self-supervised reconstruction.
Hierarchical Self-Organizing Networks are used to reveal the topology and structure of datasets. Those structures create crisp partitions of the dataset producing branches or prototype vectors that represent groups of data with similar characteristics. However, when observations can be represented by several prototypes with similar accuracy, crisp partitions are forced to classify it in just one group, so crisp divisions usually lose information about the real dataset structure. To deal with this challenge we propose the Fuzzy Growing Hierarchical Self-Organizing Networks (FGHSON). FGHSON are adaptive networks which are able to reflect the underlying structure of the dataset, in a hierarchical fuzzy way. These networks grow by using three variables which govern the membership degree of data observations to its prototype vectors and the quality of the network representation. The resulting structure allows to represent heterogeneous groups and those that present similar membership degree to several clusters
EFFICIENT IMAGE RETRIEVAL USING REGION BASED IMAGE RETRIEVALsipij
1) The document describes an efficient region-based image retrieval system that uses discrete wavelet transform and k-means clustering. It segments images into regions, each characterized by features like size, mean, and covariance.
2) The system pre-processes images by resizing, converting to HSV color space, performing DWT, and using k-means clustering on DWT coefficients to generate regions. It extracts features for each region and stores them in a database.
3) For retrieval, it pre-processes the query image similarly and calculates similarities between the query regions and database regions based on their features, returning similar images.
Region-based image segmentation partitions an image into regions based on pixel properties like homogeneity and spatial proximity. The key region-based methods are thresholding, clustering, region growing, and split-and-merge. Region growing works by aggregating neighboring pixels with similar attributes into regions starting from seed pixels. Split-and-merge first over-segments an image and then refines the segmentation by splitting regions with high variance and merging similar adjacent regions. Region-based segmentation is used for tasks like object recognition, image compression, and medical imaging.
The document discusses a method for classifying brain tumor images using artificial neural networks. It involves three main steps: 1) preprocessing MRI images using morphological operations to remove noise, 2) extracting texture and statistical features using GLCM and GLRLM techniques, and 3) classifying images using a probabilistic neural network (PNN) and measuring accuracy. Features are extracted from 50 brain tumor images and 65 images are tested, achieving a classification accuracy of up to 98%.
This document summarizes work on implementing and accelerating a 3D front propagation segmentation algorithm to measure tumor volumes from medical images. Key points:
- The algorithm uses async/finish in Habanero C to spawn parallel tasks that trace contours on individual 2D slices for 3D image segmentation.
- Initial results show speedups of 1.38x on a dual core for a 128x128x11 image and 1.26x for a 512x512x29 image.
- Future work includes rewriting sequential distance calculation and contour tracing for more speedup, improving seed point detection, and excluding non-nodule regions from segmentation.
Segmentation of Color Image using Adaptive Thresholding and Masking with Wate...Habibur Rahman
The document proposes a modified watershed algorithm for image segmentation. It applies adaptive masking and thresholding to each color channel before combining the results. The modified algorithm is compared to FCM, RG, and HKM using metrics like PSNR, MSE, PSNRRGB, and CQM on 10 images. Results show the proposed method ensures accuracy and quality while being faster than other algorithms, making it suitable for real-time use. It performs better than the other algorithms according to visual and quantitative analysis.
PR-231: A Simple Framework for Contrastive Learning of Visual RepresentationsJinwon Lee
The document presents SimCLR, a framework for contrastive learning of visual representations using simple data augmentation. Key aspects of SimCLR include using random cropping and color distortions to generate positive sample pairs for the contrastive loss, a nonlinear projection head to learn representations, and large batch sizes. Evaluation shows SimCLR learns representations that outperform supervised pretraining on downstream tasks and achieves state-of-the-art results with only view augmentation and contrastive loss.
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.
Thesis on Image compression by Manish MystManish Myst
The document discusses using neural networks for image compression. It describes how previous neural network methods divided images into blocks and achieved limited compression. The proposed method applies edge detection, thresholding, and thinning to images first to reduce their size. It then uses a single-hidden layer feedforward neural network with an adaptive number of hidden neurons based on the image's distinct gray levels. The network is trained to compress the preprocessed image block and reconstruct the original image at the receiving end. This adaptive approach aims to achieve higher compression ratios than previous neural network methods.
Convolutional neural network (CNN / ConvNet's) is a part of Computer Vision. Machine Learning Algorithm. Image Classification, Image Detection, Digit Recognition, and many more. https://technoelearn.com .
This document describes a new technique for generating 3D numeric breast phantoms from MRI data for use in microwave imaging simulations. The technique uses semi-automated segmentation algorithms to translate MR images into voxel-based surface meshes representing breast tissue structures. These patient-specific models improve on previous manual methods. The models were validated using a custom multi-modal phantom to test the program's ability to accurately reconstruct complex breast geometries. The resulting phantoms provide realistic models needed to advance the investigation of microwave imaging for breast cancer detection.
This document is an assignment submission for a data communication course. It summarizes key topics in data communication including the components of a data communication system, different modes of data flow, and common network topologies. The modes of data flow are simplex, half-duplex, and full-duplex. The common network topologies discussed are mesh, star, bus, ring, and tree with their advantages and disadvantages.
Pixel Recursive Super Resolution.
Ryan Dahl, Mohammad Norouzi & Jonathon Shlens
Google Brain.
Abstract
We present a pixel recursive super resolution model that
synthesizes realistic details into images while enhancing
their resolution. A low resolution image may correspond
to multiple plausible high resolution images, thus modeling
the super resolution process with a pixel independent conditional
model often results in averaging different details–
hence blurry edges. By contrast, our model is able to represent
a multimodal conditional distribution by properly modeling
the statistical dependencies among the high resolution
image pixels, conditioned on a low resolution input. We
employ a PixelCNN architecture to define a strong prior
over natural images and jointly optimize this prior with a
deep conditioning convolutional network. Human evaluations
indicate that samples from our proposed model look
Vectors are based on geometric elements like points, lines, and shapes defined by mathematical expressions. They represent images using control points on the x and y axes. Properties like color and thickness don't increase file size.
Bitmaps map domains like pixels to binary values of 0 or 1, representing black and white images. Pixmaps store more colors using more bits per pixel.
Digital cameras and scanners capture images digitally which are then processed into formats like JPEGs of various sizes for web or full screen display. Factors like lens quality, focus, noise, and dynamic range impact image quality.
Image file formats use vector data, pixels, or mixtures to store and organize graphics. File size increases
Non negative matrix factorization ofr tuor classificationSahil Prajapati
The PPT aware about you the concept of Non Negative Matrix Factorization and how theses techniques can be used to treat cancer by the use of the coding such as a MATLAB,LABVIEW software to locate the tumor or the cancer part with the different approaches and tachniques.
Go through the PPT to know and how one can improvise my work for better results??
Please help me if one come up with other techniques.
Supervised and unsupervised classification techniques for satellite imagery i...gaup_geo
This document compares supervised and unsupervised classification techniques for satellite imagery analysis of land cover in the Porto Alegre region of Brazil. Supervised classification involved collecting over 500 training sites to create signatures for 8 land cover classes. Unsupervised classification used ISOcluster to generate 36 spectral classes which were grouped into the 8 informational classes. Both classifications underwent post-processing including majority filtering and polygon elimination to produce final 1-hectare minimum mapping unit vector maps. Accuracy assessments found the supervised classification to be more accurate at 76% compared to 48% for the unsupervised method.
Explainable deep learning with applications in Healthcare By Sunil Kumar Vupp...Analytics India Magazine
We started relying on the decisions made by deep learning models, however why it works and how it works are still big questions for most of us. We shall try to open that black box of deep learning which is essential to build trust for wide spread adoption. The speaker shall address the importance of feature visualization and localization in deep learning models esp. convolutional neural networks. He shares the results of applying methods such as activation map, deconvolution and Grad-CAM in healthcare.
Human brain is the most complex structure where identifying the tumor like diseases are extremely challenging because differentiating the components of a brain is complex. In this paper, pillar k-means algorithm is used for segmentation of brain tumor from magnetic resonance image (MRI).Generally, the brain tumor is detected by radiologist through analysis of MR images which takes longer time. The pillar k-means algorithm’s experimental results clarify the effectiveness of our approach to improve the segmentation quality, accuracy, and computational time. Classify, the tumor from the brain MR images using Bayesian classification.
This document provides an overview of the application of remote sensing and geographical information systems in civil engineering. It discusses key concepts such as image interpretation, data preprocessing, feature extraction, image classification, and accuracy assessment. The document aims to explain how remote sensing and GIS techniques can be used to extract useful information from imagery and geospatial data for civil engineering applications.
it is highly useful for geography students in the field of remote sensing and it is in very simple and explanatory for the purpose of simplification with relevant images in this ppt.
This document describes a method for pixel-level image fusion using principal component analysis (PCA). PCA is used to transform correlated image pixels into a set of uncorrelated principal components. The first principal component accounts for the most variance in the pixel values. To fuse images, the pixels of the input images are arranged into vectors and subtracted from their mean. PCA is applied to get the eigenvectors corresponding to the largest eigenvalues. The normalized eigenvectors are used to compute a fused image as a weighted sum of the input images. Performance is evaluated using metrics like standard deviation, entropy, cross-entropy, and fusion mutual information, with higher values of these metrics indicating better quality of the fused image.
This document outlines several papers on unsupervised and semi-supervised object detection. It describes approaches such as using unbiased teachers to generate pseudo-labels for semi-supervised learning. It also discusses contrastive learning methods to learn representations from unlabeled data as well as consistency-based active learning and self-training frameworks. The papers covered include approaches for domain adaptation, open world detection of unknown objects, and monocular 3D object detection through self-supervised reconstruction.
Hierarchical Self-Organizing Networks are used to reveal the topology and structure of datasets. Those structures create crisp partitions of the dataset producing branches or prototype vectors that represent groups of data with similar characteristics. However, when observations can be represented by several prototypes with similar accuracy, crisp partitions are forced to classify it in just one group, so crisp divisions usually lose information about the real dataset structure. To deal with this challenge we propose the Fuzzy Growing Hierarchical Self-Organizing Networks (FGHSON). FGHSON are adaptive networks which are able to reflect the underlying structure of the dataset, in a hierarchical fuzzy way. These networks grow by using three variables which govern the membership degree of data observations to its prototype vectors and the quality of the network representation. The resulting structure allows to represent heterogeneous groups and those that present similar membership degree to several clusters
EFFICIENT IMAGE RETRIEVAL USING REGION BASED IMAGE RETRIEVALsipij
1) The document describes an efficient region-based image retrieval system that uses discrete wavelet transform and k-means clustering. It segments images into regions, each characterized by features like size, mean, and covariance.
2) The system pre-processes images by resizing, converting to HSV color space, performing DWT, and using k-means clustering on DWT coefficients to generate regions. It extracts features for each region and stores them in a database.
3) For retrieval, it pre-processes the query image similarly and calculates similarities between the query regions and database regions based on their features, returning similar images.
Region-based image segmentation partitions an image into regions based on pixel properties like homogeneity and spatial proximity. The key region-based methods are thresholding, clustering, region growing, and split-and-merge. Region growing works by aggregating neighboring pixels with similar attributes into regions starting from seed pixels. Split-and-merge first over-segments an image and then refines the segmentation by splitting regions with high variance and merging similar adjacent regions. Region-based segmentation is used for tasks like object recognition, image compression, and medical imaging.
The document discusses a method for classifying brain tumor images using artificial neural networks. It involves three main steps: 1) preprocessing MRI images using morphological operations to remove noise, 2) extracting texture and statistical features using GLCM and GLRLM techniques, and 3) classifying images using a probabilistic neural network (PNN) and measuring accuracy. Features are extracted from 50 brain tumor images and 65 images are tested, achieving a classification accuracy of up to 98%.
This document summarizes work on implementing and accelerating a 3D front propagation segmentation algorithm to measure tumor volumes from medical images. Key points:
- The algorithm uses async/finish in Habanero C to spawn parallel tasks that trace contours on individual 2D slices for 3D image segmentation.
- Initial results show speedups of 1.38x on a dual core for a 128x128x11 image and 1.26x for a 512x512x29 image.
- Future work includes rewriting sequential distance calculation and contour tracing for more speedup, improving seed point detection, and excluding non-nodule regions from segmentation.
Segmentation of Color Image using Adaptive Thresholding and Masking with Wate...Habibur Rahman
The document proposes a modified watershed algorithm for image segmentation. It applies adaptive masking and thresholding to each color channel before combining the results. The modified algorithm is compared to FCM, RG, and HKM using metrics like PSNR, MSE, PSNRRGB, and CQM on 10 images. Results show the proposed method ensures accuracy and quality while being faster than other algorithms, making it suitable for real-time use. It performs better than the other algorithms according to visual and quantitative analysis.
PR-231: A Simple Framework for Contrastive Learning of Visual RepresentationsJinwon Lee
The document presents SimCLR, a framework for contrastive learning of visual representations using simple data augmentation. Key aspects of SimCLR include using random cropping and color distortions to generate positive sample pairs for the contrastive loss, a nonlinear projection head to learn representations, and large batch sizes. Evaluation shows SimCLR learns representations that outperform supervised pretraining on downstream tasks and achieves state-of-the-art results with only view augmentation and contrastive loss.
PR-231: A Simple Framework for Contrastive Learning of Visual Representations
Similar to IEEE 2014 DOTNET IMAGE PROCESSING PROJECTS Image classification using multiscale information fusion based on saliency driven nonlinear diffusion filtering
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.
Thesis on Image compression by Manish MystManish Myst
The document discusses using neural networks for image compression. It describes how previous neural network methods divided images into blocks and achieved limited compression. The proposed method applies edge detection, thresholding, and thinning to images first to reduce their size. It then uses a single-hidden layer feedforward neural network with an adaptive number of hidden neurons based on the image's distinct gray levels. The network is trained to compress the preprocessed image block and reconstruct the original image at the receiving end. This adaptive approach aims to achieve higher compression ratios than previous neural network methods.
CNN FEATURES ARE ALSO GREAT AT UNSUPERVISED CLASSIFICATION cscpconf
This paper aims at providing insight on the transferability of deep CNN features to
unsupervised problems. We study the impact of different pretrained CNN feature extractors on
the problem of image set clustering for object classification as well as fine-grained
classification. We propose a rather straightforward pipeline combining deep-feature extraction
using a CNN pretrained on ImageNet and a classic clustering algorithm to classify sets of
images. This approach is compared to state-of-the-art algorithms in image-clustering and
provides better results. These results strengthen the belief that supervised training of deep CNN
on large datasets, with a large variability of classes, extracts better features than most carefully
designed engineering approaches, even for unsupervised tasks. We also validate our approach
on a robotic application, consisting in sorting and storing objects smartly based on clustering
IRJET- Deep Convolutional Neural Network for Natural Image Matting using Init...IRJET Journal
This document describes a study that used a deep convolutional neural network (CNN) to perform natural image matting using initial alpha mattes. The researchers trained a CNN using alpha mattes generated from closed form matting and K-nearest neighbor (KNN) matting as inputs. Combining the results from these two existing matting methods, which take different local image structures as input, achieved more accurate foreground extraction than prior methods alone. The CNN was able to classify images with higher performance than existing algorithms by using convolutional layers instead of fully connected layers.
Integrated Hidden Markov Model and Kalman Filter for Online Object Trackingijsrd.com
Visual prior from generic real-world images study to represent that objects in a scene. The existing work presented online tracking algorithm to transfers visual prior learned offline for online object tracking. To learn complete dictionary to represent visual prior with collection of real world images. Prior knowledge of objects is generic and training image set does not contain any observation of target object. Transfer learned visual prior to construct object representation using Sparse coding and Multiscale max pooling. Linear classifier is learned online to distinguish target from background and also to identify target and background appearance variations over time. Tracking is carried out within Bayesian inference framework and learned classifier is used to construct observation model. Particle filter is used to estimate the tracking result sequentially however, unable to work efficiently in noisy scenes. Time sift variance were not appropriated to track target object with observer value to prior information of object structure. Proposal HMM based kalman filter to improve online target tracking in noisy sequential image frames. The covariance vector is measured to identify noisy scenes. Discrete time steps are evaluated for identifying target object with background separation. Experiment conducted on challenging sequences of scene. To evaluate the performance of object tracking algorithm in terms of tracking success rate, Centre location error, Number of scenes, Learning object sizes, and Latency for tracking.
The document summarizes a proposed system for currency recognition on mobile phones. The system has the following modules: 1) segmentation to isolate the currency from background noise, 2) feature extraction and building a visual vocabulary, 3) instance retrieval using inverted indexing and spatial reranking, 4) classification by vote counting spatially consistent features. The system was adapted for mobile by reducing complexity, such as using an inverted index, while maintaining accuracy. Performance is evaluated using metrics like accuracy and precision.
This paper proposes a new algorithm for single-image super-resolution that exploits image compressibility in the wavelet domain using compressed sensing theory. The algorithm incorporates the downsampling low-pass filter into the measurement matrix to decrease coherence between the wavelet basis and sampling basis, allowing use of wavelets. It then uses a greedy algorithm to solve for sparse wavelet coefficients representing the high-resolution image. Results show improved performance over existing super-resolution approaches without requiring training data.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
This document presents a method for image upscaling using a fuzzy ARTMAP neural network. It begins with an introduction to image upscaling and interpolation techniques. It then provides background on ARTMAP neural networks and fuzzy logic. The proposed method uses a linear interpolation algorithm trained with an ARTMAP network. Results show the method performs better than nearest neighbor interpolation in terms of peak signal-to-noise ratio, mean squared error, and structural similarity, though not as high as bicubic interpolation. Overall, the fuzzy ARTMAP network provides an effective way to perform image upscaling with fewer artifacts than traditional methods.
Issues in AI product development and practices in audio applicationsTaesu Kim
1) Deep neural networks are difficult to understand and analyze due to their complex architectures and large number of parameters. Understanding why neural networks make certain predictions is an important area of research.
2) Influence functions can be used to analyze the effect that individual training samples have on a neural network model's parameters and predictions. This helps explain model behavior and identify influential training points.
3) Identifying influential training samples allows experts to prioritize data points to check for label noise, which can improve model performance. Influence functions also enable crafting adversarial training examples that subtly change a model's predictions without appearing different to humans.
Implementation of Fuzzy Logic for the High-Resolution Remote Sensing Images w...IOSR Journals
This document describes an implementation of fuzzy logic for high-resolution remote sensing image classification with improved accuracy. It discusses using an object-based approach with fuzzy rules to classify urban land covers in a satellite image. The approach involves image segmentation using k-means clustering or ISODATA clustering. Features are then extracted from the image objects and fuzzy logic is applied to classify the objects based on membership functions. The method was tested on different sensor and resolution images in MATLAB and showed improved classification accuracy over other techniques, achieving lower entropy in results. Future work planned includes designing an unsupervised classification model combining k-means clustering and fuzzy-based object orientation.
Enhancement and Segmentation of Historical Recordscsandit
Document Analysis and Recognition (DAR) aims to extract automatically the information in the document and also addresses to human comprehension. The automatic processing of degraded
historical documents are applications of document image analysis field which is confronted with many difficulties due to the storage condition and the complexity of the script. The main interest
of enhancement of historical documents is to remove undesirable statistics that appear in the
background and highlight the foreground, so as to enable automatic recognition of documents
with high accuracy. This paper addresses pre-processing and segmentation of ancient scripts, as an initial step to automate the task of an epigraphist in reading and deciphering inscriptions.
Pre-processing involves, enhancement of degraded ancient document images which is achieved through four different Spatial filtering methods for smoothing or sharpening namely Median,
Gaussian blur, Mean and Bilateral filter, with different mask sizes. This is followed by
binarization of the enhanced image to highlight the foreground information, using Otsu
thresholding algorithm. In the second phase Segmentation is carried out using Drop Fall and
WaterReservoir approaches, to obtain sampled characters, which can be used in later stages of
OCR. The system showed good results when tested on the nearly 150 samples of varying
degraded epigraphic images and works well giving better enhanced output for, 4x4 mask size
for Median filter, 2x2 mask size for Gaussian blur, 4x4 mask size for Mean and Bilateral filter.
The system can effectively sample characters from enhanced images, giving a segmentation rate of 85%-90% for Drop Fall and 85%-90% for Water Reservoir techniques respectively
Binarization of Degraded Text documents and Palm Leaf ManuscriptsIRJET Journal
This document proposes a technique for binarizing degraded text documents and palm leaf manuscripts. It involves taking the average pixel value of the image as a threshold to distinguish foreground from background. The algorithm first computes the average value of the original image and uses it to set pixels above the threshold to black, removing background. It then computes the average of the remaining image, excluding black pixels, and uses that value as a new threshold to set remaining pixels above it to white, extracting the foreground. The technique is tested on old documents and manuscripts, showing improvement over existing methods based on metrics like peak signal-to-noise ratio. While effective for documents, it needs improvement for palm leaf manuscripts with non-uniform degradation.
Noise-robust classification with hypergraph neural networknooriasukmaningtyas
This paper presents a novel version of hypergraph neural network method. This method is utilized to solve the noisy label learning problem. First, we apply the PCA dimensional reduction technique to the feature matrices of the image datasets in order to reduce the “noise” and the redundant features in the feature matrices of the image datasets and to reduce the runtime constructing the hypergraph of the hypergraph neural network method. Then, the classic graph based semisupervised learning method, the classic hypergraph based semi-supervised learning method, the graph neural network, the hypergraph neural network, and our proposed hypergraph neural network are employed to solve the noisy label learning problem. The accuracies of these five methods are evaluated and compared. Experimental results show that the hypergraph neural network methods achieve the best performance when the noise level increases. Moreover, the hypergraph neural network methods are at least as good as the graph neural network.
CONTENT BASED VIDEO CATEGORIZATION USING RELATIONAL CLUSTERING WITH LOCAL SCA...ijcsit
This paper introduces a novel approach for efficient video categorization. It relies on two main
components. The first one is a new relational clustering technique that identifies video key frames by
learning cluster dependent Gaussian kernels. The proposed algorithm, called clustering and Local Scale
Learning algorithm (LSL) learns the underlying cluster dependent dissimilarity measure while finding
compact clusters in the given dataset. The learned measure is a Gaussian dissimilarity function defined
with respect to each cluster. We minimize one objective function to optimize the optimal partition and the
cluster dependent parameter. This optimization is done iteratively by dynamically updating the partition
and the local measure. The kernel learning task exploits the unlabeled data and reciprocally, the
categorization task takes advantages of the local learned kernel. The second component of the proposed
video categorization system consists in discovering the video categories in an unsupervised manner using
the proposed LSL. We illustrate the clustering performance of LSL on synthetic 2D datasets and on high
dimensional real data. Also, we assess the proposed video categorization system using a real video
collection and LSL algorithm.
JPM1406 Dual-Geometric Neighbor Embedding for Image Super Resolution With Sp...chennaijp
This document proposes a dual-geometric neighbor embedding (DGNE) approach for single image super resolution (SISR) that considers image patches as multiview data with spatial organization. DGNE explores multiview features and local spatial neighbors of patches to find a feature-spatial manifold embedding for images. It assumes patches from the same manifold will lie in a low-dimensional affine subspace, and uses tensor-simultaneous orthogonal matching pursuit to find sparse neighbors and realize joint sparse coding of feature-spatial image tensors. Experiments show it provides efficient and superior recovery compared to other methods.
Deep neural networks learn hierarchical representations of data through multiple layers of feature extraction. Lower layers identify low-level features like edges while higher layers integrate these into more complex patterns and objects. Deep learning models are trained on large labeled datasets by presenting examples, calculating errors, and adjusting weights to minimize errors over many iterations. Deep learning has achieved human-level performance on tasks like image recognition due to its ability to leverage large amounts of training data and learn representations automatically rather than relying on manually designed features.
SINGLE IMAGE SUPER RESOLUTION: A COMPARATIVE STUDYcsandit
The majority of applications requiring high resolution images to derive and analyze data
accurately and easily. Image super resolution is playing an effective role in those applications.
Image super resolution is the process of producing high resolution image from low resolution
image. In this paper, we study various image super resolution techniques with respect to the
quality of results and processing time. This comparative study introduces a comparison between
four algorithms of single image super-resolution. For fair comparison, the compared algorithms
are tested on the same dataset and same platform to show the major advantages of one over the
others.
A Review on Matching For Sketch TechniqueIOSR Journals
This document summarizes several techniques for sketch-based image retrieval. It discusses methods using SIFT features, HOG descriptors, color segmentation, and gradient orientation histograms. It also reviews applications of these techniques to domains like facial recognition, graffiti matching, and tattoo identification for law enforcement. The techniques aim to extract visual features from sketches that can be used to match and retrieve similar images from databases. While achieving good results, the methods have limitations regarding database size and specificity, and accuracy with complex textures and shapes. Overall, the review examines advances in using sketches as queries for image retrieval.
This document is an internship report submitted by Raghunandan J to Eckovation about a project on classifying handwritten digits using a convolutional neural network. It provides an introduction to convolutional neural networks and explains each layer of a CNN including the input, convolutional layer, pooling layer, and fully connected layer. It also gives examples of real-world applications that use artificial neural networks like Google Maps, Google Images, and voice assistants.
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IEEE 2014 DOTNET IMAGE PROCESSING PROJECTS Image classification using multiscale information fusion based on saliency driven nonlinear diffusion filtering
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Image Classification Using Multiscale Information Fusion Based
on Saliency Driven Nonlinear Diffusion Filtering
Abstract
In this paper, we propose saliency driven image multiscale nonlinear diffusion
filtering. The resulting scale space in general preserves or even enhances
semantically important structures such as edges, lines, or flow-like structures in
the foreground, and inhibits and smoothes clutter in the background. The image
is classified using multi scale information fusion based on the original image, the
image at the final scale at which the diffusion process converges, and the image at
a midscale. Our algorithm emphasizes the foreground features, which are
important for image classification. The background image regions, whether
considered as contexts of the foreground or noise to the foreground, can be
globally handled by fusing information from different scales. Experimental tests of
2. the effectiveness of the multi scale space for the image classification are
conducted on the following publicly available datasets: 1) the PASCAL
2005dataset; 2) the Oxford 102 flowers dataset; and 3) the Oxford 17flowers
dataset, with high classification rates.
Existing System:
In image classification, it is an important but difficult task to deal with the
background information. The background treated as noise; nevertheless, in some
cases the background provides a context, which may increase the performance of
image classification. Experimentally analyzed the influence of the background on
image classification. They demonstrated that although the background may have
correlations with the foreground objects, using both the background and
foreground features for learning and recognition yields less accurate results than
using the foreground features alone. Overall, the background information was not
relevant to image classification.
Proposed System
We propose to classify images using the saliency driven multi-scale image
representation. Images whose foregrounds are clearer than their backgrounds are
more likely to be correctly classified at a large scale, and images whose
backgrounds are clearer are more likely to be correctly classified at a small scale.
So, information from different scales can be used to acquire more accurate image
classification results.
3. Advantage
No other work which applies nonlinear diffusion filtering to image
classification..
First, the nonlinear diffusion-based multi scale space can preserve or
enhance semantically important image structures at large scales.
Second, our method can deal with the background information no
matter whether it is a context or noise, and then can be adapted to
backgrounds which change over time.
Third, our method can partly handle cases in which the saliency map
is incorrect, by including the original image at scale 0 in the set of
scaled images used for classification.
Modules:
Original Image
Scales tm
Scales TM
Multi scale Diffusion(Saliency)
Original Image
It contains original image with large background for saliency multi
scale detection.
4. Tm and TM:
Multi-scale fusion obtains more accurate results than those obtained using
the individual scales Tm or TM. This indicates that the three scales include
complementary information, and their fusion can improve the classification
results.
However, because the original image is included in the fusion, correct final
classification results are obtained.
5. Multi Scale Diffusion (Saliency):
Saliency maps, the foreground regions were correctly detected. Our
saliency driven nonlinear diffusion preserved their foreground regions and largely
smoothed the background regions. Therefore, at scales Tm and TM in which the
backgrounds were filtered out, the images were correctly classified. This produces
a correct classification by multi-scale fusion.
6. System Specification
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive: 1.44 Mb.
• Monitor : 14’ Colour Monitor.
• Mouse : Optical Mouse.
• Ram : 512 Mb.
Software Requirements:
• Operating system : Windows 7.
• Coding Language : ASP.Net with C#
• Data Base : SQL Server 2008.
7. Conclusion
In this paper, we have proposed saliency driven multi-scale nonlinear
diffusion filtering, by modifying the mathematical equations for nonlinear
diffusion filtering, and determining the diffusion parameters using the saliency
detection results. We have further applied this new method to image
classification. The saliency driven nonlinear multi-scale space preserves and even
enhances important image local structures, such as lines and edges, at large
scales. Multi-scale information has been fused using a weighted function of the
distances between images at different scales. The saliency driven multi-scale
representation can include information about the background in order to improve
image classification. Experiments have been conducted on widely used datasets,
namely the PASCAL2005 dataset, the Oxford 102 flowers dataset, and the Oxford
17 flowers dataset. The results have demonstrated that saliency driven multi-scale
information fusion improves the accuracy of image classification.