Here K means clustering method is being used to compress the images. The input is the number of color required in the output image which is same as the number of clusters. The output is the compressed image having prementioned number of colors
This document provides an overview of digital image processing and image compression techniques. It defines what a digital image is, discusses the advantages and disadvantages of digital images over analog images. It describes the fundamental steps in digital image processing as well as types of data redundancy that can be exploited for image compression, including coding, interpixel, and psychovisual redundancy. Common image compression models and lossless compression techniques like Lempel-Ziv-Welch coding are also summarized.
Operations in Digital Image Processing + Convolution by ExampleAhmed Gad
Digital image processing operations can be either point or group.
This presentation explains both operations (point and group) and shows how convolution works by a numerical example.
Ahmed Fawzy Gad
ahmed.fawzy@ci.menofia.edu.eg
Information Technology Department
Faculty of Computers and Information (FCI)
Menoufia University
Egypt
Find me on:
AFCIT
http://www.afcit.xyz
YouTube
https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw
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https://www.linkedin.com/in/ahmedfgad/
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https://www.researchgate.net/profile/Ahmed_Gad13
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https://www.academia.edu/
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https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en
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The document discusses various image enhancement techniques in the spatial domain. It covers basic gray level transformations like negatives, log transformations, and power law transformations. It also discusses histogram processing and enhancement using arithmetic operations. Furthermore, it explains smoothing and sharpening spatial filters, and how to combine different spatial enhancement methods. The document provides examples and background on these fundamental image enhancement concepts.
This document provides information about a digital image processing lecture given by Dr. Moe Moe Myint from Technological University in Kyaukse, Myanmar. It includes the lecture schedule and contact information for Dr. Myint. The document also provides an overview of Chapter 2 which discusses elements of visual perception, light and the electromagnetic spectrum, image sensing and acquisition, image sampling and quantization, and basic relationships between pixels. It provides examples of different types of digital images including intensity, RGB, binary, and index images. It also discusses the effects of spatial and intensity level resolution on images.
IMAGE COMPRESSION AND DECOMPRESSION SYSTEMVishesh Banga
Image compression is the application of Data compression on digital images. In effect, the objective is to reduce redundancy of the image data in order to be able to store or transmit data in an efficient form.
This document proposes a method called learnable image encryption that allows deep learning to be performed on encrypted images while protecting privacy. It works by applying weak block-wise encryption to images before training deep learning models. The models can still learn meaningful patterns from the encrypted images. This approach could help shopping malls analyze customer behavior from security camera footage or allow companies to develop AI systems using encrypted data without compromising privacy. The method achieves comparable accuracy to training on plain images while keeping the encrypted images unintelligible to humans. Code and details on the block-wise encryption and decryption algorithms are available online.
This document provides an overview of digital image processing and image compression techniques. It defines what a digital image is, discusses the advantages and disadvantages of digital images over analog images. It describes the fundamental steps in digital image processing as well as types of data redundancy that can be exploited for image compression, including coding, interpixel, and psychovisual redundancy. Common image compression models and lossless compression techniques like Lempel-Ziv-Welch coding are also summarized.
Operations in Digital Image Processing + Convolution by ExampleAhmed Gad
Digital image processing operations can be either point or group.
This presentation explains both operations (point and group) and shows how convolution works by a numerical example.
Ahmed Fawzy Gad
ahmed.fawzy@ci.menofia.edu.eg
Information Technology Department
Faculty of Computers and Information (FCI)
Menoufia University
Egypt
Find me on:
AFCIT
http://www.afcit.xyz
YouTube
https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw
Google Plus
https://plus.google.com/u/0/+AhmedGadIT
SlideShare
https://www.slideshare.net/AhmedGadFCIT
LinkedIn
https://www.linkedin.com/in/ahmedfgad/
ResearchGate
https://www.researchgate.net/profile/Ahmed_Gad13
Academia
https://www.academia.edu/
Google Scholar
https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en
Mendelay
https://www.mendeley.com/profiles/ahmed-gad12/
ORCID
https://orcid.org/0000-0003-1978-8574
StackOverFlow
http://stackoverflow.com/users/5426539/ahmed-gad
Twitter
https://twitter.com/ahmedfgad
Facebook
https://www.facebook.com/ahmed.f.gadd
Pinterest
https://www.pinterest.com/ahmedfgad/
The document discusses various image enhancement techniques in the spatial domain. It covers basic gray level transformations like negatives, log transformations, and power law transformations. It also discusses histogram processing and enhancement using arithmetic operations. Furthermore, it explains smoothing and sharpening spatial filters, and how to combine different spatial enhancement methods. The document provides examples and background on these fundamental image enhancement concepts.
This document provides information about a digital image processing lecture given by Dr. Moe Moe Myint from Technological University in Kyaukse, Myanmar. It includes the lecture schedule and contact information for Dr. Myint. The document also provides an overview of Chapter 2 which discusses elements of visual perception, light and the electromagnetic spectrum, image sensing and acquisition, image sampling and quantization, and basic relationships between pixels. It provides examples of different types of digital images including intensity, RGB, binary, and index images. It also discusses the effects of spatial and intensity level resolution on images.
IMAGE COMPRESSION AND DECOMPRESSION SYSTEMVishesh Banga
Image compression is the application of Data compression on digital images. In effect, the objective is to reduce redundancy of the image data in order to be able to store or transmit data in an efficient form.
This document proposes a method called learnable image encryption that allows deep learning to be performed on encrypted images while protecting privacy. It works by applying weak block-wise encryption to images before training deep learning models. The models can still learn meaningful patterns from the encrypted images. This approach could help shopping malls analyze customer behavior from security camera footage or allow companies to develop AI systems using encrypted data without compromising privacy. The method achieves comparable accuracy to training on plain images while keeping the encrypted images unintelligible to humans. Code and details on the block-wise encryption and decryption algorithms are available online.
This document discusses digital image compression. It notes that compression is needed due to the huge amounts of digital data. The goals of compression are to reduce data size by removing redundant data and transforming the data prior to storage and transmission. Compression can be lossy or lossless. There are three main types of redundancy in digital images - coding, interpixel, and psychovisual - that compression aims to reduce. Channel encoding can also be used to add controlled redundancy to protect the source encoded data when transmitted over noisy channels. Common compression methods exploit these different types of redundancies.
JPEG compression is a lossy compression technique that exploits human visual perception. It works by:
1) Splitting images into blocks and applying the discrete cosine transform (DCT) to each block to de-correlate pixel values.
2) Quantizing the resulting DCT coefficients, discarding less visible high-frequency data.
3) Entropy coding the quantized DCT coefficients using techniques like run-length encoding and Huffman coding to further compress the data.
Image compression involves reducing the size of image files to reduce storage space and transmission time. There are three main types of redundancy in images: coding redundancy, spatial redundancy between neighboring pixels, and irrelevant information. Common compression methods remove these redundancies, such as Huffman coding, arithmetic coding, LZW coding, and run length coding. Popular image file formats include JPEG for photos, PNG for web images, and TIFF, GIF, and DICOM for other uses.
Line Detection using Hough transform .pptxshubham loni
This document discusses line detection through the Hough transform. It begins with an introduction to the Hough transform and how it can be used to extract features like lines from an image. It then provides details on the process, which involves edge detection using the Canny edge detector followed by the Hough transform. The Canny edge detector uses Gaussian and Sobel operators for noise reduction and edge detection. The Hough transform maps edge points to a parameter space where lines are represented as peaks, allowing line detection. Examples and applications are provided, such as building edge extraction, lane detection, and extracting shapes.
This document discusses image steganography using the least significant bit (LSB) method. It defines steganography as concealing a file within another file. For image steganography, a secret message is hidden in an image by altering the LSB of pixel values. The algorithm embeds each bit of the message sequentially by toggling the LSB of pixels. To extract the message, the LSB values of pixels are read out in order. While simple, LSB steganography can be detected easily and hides messages in a small number of pixels. Applications include copyright protection and secure data transfers, while disadvantages are low security and reduced image quality.
The document provides information about MPEG compression standards. It discusses the history of MPEG and how it was established in 1988 as a joint effort between ISO and IEC to set standards for audio and video compression. It describes several MPEG standards including MPEG-1, MPEG-2, MPEG-4, MPEG-7, and MPEG-21. MPEG-4 is discussed in more detail, explaining that it offers greater efficiency than MPEG-2, allows encoding of mixed data types, and enables interaction of audio-visual scenes at the receiver end. The document contains diagrams and tables to illustrate key points about the different MPEG standards and compression techniques.
At the end of this lesson, you should be able to;
describe spatial resolution
describe intensity resolution
identify the effect of aliasing
describe image interpolation
describe relationships among the pixels
image compression using matlab project reportkgaurav113
The document discusses JPEG image compression and its implementation in MATLAB. It describes the steps taken to encode and decode grayscale images using the JPEG baseline standard in MATLAB. These include dividing images into 8x8 blocks, applying the discrete cosine transform, quantizing the results, and entropy encoding the data. Encoding compression ratios and processing times are compared between classic and fast DCT approaches. The project also examines how quantization coefficients affect the restored image quality.
The document discusses content-based image retrieval (CBIR) systems. It describes how CBIR systems use feature extraction to search large image databases based on visual content. The key components of CBIR systems are feature extraction, indexing, and system design. Feature extraction involves extracting information about images' colors, textures, shapes, and spatial locations. Effective features and indexing techniques are needed to make CBIR scalable for large image collections. Performance is evaluated based on how well systems return relevant images.
This document discusses various image compression standards and techniques. It begins with an introduction to image compression, noting that it reduces file sizes for storage or transmission while attempting to maintain image quality. It then outlines several international compression standards for binary images, photos, and video, including JPEG, MPEG, and H.261. The document focuses on JPEG, describing how it uses discrete cosine transform and quantization for lossy compression. It also discusses hierarchical and progressive modes for JPEG. In closing, the document presents challenges and results for motion segmentation and iris image segmentation.
This document discusses image enhancement and restoration techniques in digital image processing. It describes various arithmetic and logical operations that can be performed on images, including addition, averaging, subtraction, multiplication/division, AND, and OR. These operations allow images to be combined, adjusted for brightness, and manipulated to enhance features or remove artifacts. Pixel value ranges must be normalized back to 0-255 after arithmetic operations.
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.
This presentation describes briefly about the image enhancement in spatial domain, basic gray level transformation, histogram processing, enhancement using arithmetic/ logical operation, basics of spatial filtering and local enhancements.
This document discusses different techniques for image segmentation. It begins by defining image segmentation as dividing an image into regions based on similarity and differences between adjacent regions. The main approaches discussed are discontinuity-based segmentation, which looks for sudden changes in pixel intensity (edges), and similarity-based segmentation, which groups similar pixels into regions. The document then examines various methods for detecting edges, linking edges, thresholding, and region-based segmentation using techniques like region growing and splitting/merging.
The document discusses image compression techniques. It explains that the goal of image compression is to reduce irrelevant and redundant image data to store and transmit images more efficiently. There are three main types of redundancy reduced in image compression: coding, interpixel, and psychovisual. Lossless compression preserves all image data using techniques like Huffman coding, run-length coding, arithmetic coding, and Lempel-Ziv coding. Lossy compression allows for some quality loss and higher compression ratios.
This document discusses various methods of data compression. It begins by defining compression as reducing the size of data while retaining its meaning. There are two main types of compression: lossless and lossy. Lossless compression allows for perfect reconstruction of the original data by removing redundant data. Common lossless methods include run-length encoding and Huffman coding. Lossy compression is used for images and video, and results in some loss of information. Popular lossy schemes are JPEG, MPEG, and MP3. The document then proceeds to describe several specific compression algorithms and their encoding and decoding processes.
Spatial filtering involves applying filters or kernels to images to enhance or modify pixel values based on neighboring pixel values. Linear spatial filtering involves taking a weighted sum of pixel values within the filter window. Common filters include averaging filters for noise reduction, median filters to reduce impulse noise while preserving edges, and sharpening filters like Laplacian filters and unsharp masking to enhance details.
This slide gives you the basic understanding of digital image compression.
Please Note: This is a class teaching PPT, more and detail topics were covered in the classroom.
This document discusses techniques for super resolution image reconstruction from multiple low resolution images. There are three main approaches: interpolation-based, example-learning-based, and multi-image based. Multi-image based super resolution attempts to reconstruct the original high resolution image by using information from a set of observed low resolution images. Key steps involved in multi-image super resolution include image registration to determine displacements between images, modeling the imaging process, and using techniques like the Irani and Peleg algorithm to estimate the blurring function and reconstruct the high resolution image.
This document presents a new approach called Pillar-K-means for image segmentation. Pillar-K-means applies a clustering algorithm called K-means to group pixels in images, but first optimizes the K-means process using an algorithm called Pillar. This is done to improve precision and reduce computation time for image segmentation. The document describes K-means clustering and its issues, then introduces Pillar-K-means which optimizes K-means initialization to enhance segmentation accuracy and speed. Experiments show Pillar-K-means improved over standard K-means.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
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
This document discusses digital image compression. It notes that compression is needed due to the huge amounts of digital data. The goals of compression are to reduce data size by removing redundant data and transforming the data prior to storage and transmission. Compression can be lossy or lossless. There are three main types of redundancy in digital images - coding, interpixel, and psychovisual - that compression aims to reduce. Channel encoding can also be used to add controlled redundancy to protect the source encoded data when transmitted over noisy channels. Common compression methods exploit these different types of redundancies.
JPEG compression is a lossy compression technique that exploits human visual perception. It works by:
1) Splitting images into blocks and applying the discrete cosine transform (DCT) to each block to de-correlate pixel values.
2) Quantizing the resulting DCT coefficients, discarding less visible high-frequency data.
3) Entropy coding the quantized DCT coefficients using techniques like run-length encoding and Huffman coding to further compress the data.
Image compression involves reducing the size of image files to reduce storage space and transmission time. There are three main types of redundancy in images: coding redundancy, spatial redundancy between neighboring pixels, and irrelevant information. Common compression methods remove these redundancies, such as Huffman coding, arithmetic coding, LZW coding, and run length coding. Popular image file formats include JPEG for photos, PNG for web images, and TIFF, GIF, and DICOM for other uses.
Line Detection using Hough transform .pptxshubham loni
This document discusses line detection through the Hough transform. It begins with an introduction to the Hough transform and how it can be used to extract features like lines from an image. It then provides details on the process, which involves edge detection using the Canny edge detector followed by the Hough transform. The Canny edge detector uses Gaussian and Sobel operators for noise reduction and edge detection. The Hough transform maps edge points to a parameter space where lines are represented as peaks, allowing line detection. Examples and applications are provided, such as building edge extraction, lane detection, and extracting shapes.
This document discusses image steganography using the least significant bit (LSB) method. It defines steganography as concealing a file within another file. For image steganography, a secret message is hidden in an image by altering the LSB of pixel values. The algorithm embeds each bit of the message sequentially by toggling the LSB of pixels. To extract the message, the LSB values of pixels are read out in order. While simple, LSB steganography can be detected easily and hides messages in a small number of pixels. Applications include copyright protection and secure data transfers, while disadvantages are low security and reduced image quality.
The document provides information about MPEG compression standards. It discusses the history of MPEG and how it was established in 1988 as a joint effort between ISO and IEC to set standards for audio and video compression. It describes several MPEG standards including MPEG-1, MPEG-2, MPEG-4, MPEG-7, and MPEG-21. MPEG-4 is discussed in more detail, explaining that it offers greater efficiency than MPEG-2, allows encoding of mixed data types, and enables interaction of audio-visual scenes at the receiver end. The document contains diagrams and tables to illustrate key points about the different MPEG standards and compression techniques.
At the end of this lesson, you should be able to;
describe spatial resolution
describe intensity resolution
identify the effect of aliasing
describe image interpolation
describe relationships among the pixels
image compression using matlab project reportkgaurav113
The document discusses JPEG image compression and its implementation in MATLAB. It describes the steps taken to encode and decode grayscale images using the JPEG baseline standard in MATLAB. These include dividing images into 8x8 blocks, applying the discrete cosine transform, quantizing the results, and entropy encoding the data. Encoding compression ratios and processing times are compared between classic and fast DCT approaches. The project also examines how quantization coefficients affect the restored image quality.
The document discusses content-based image retrieval (CBIR) systems. It describes how CBIR systems use feature extraction to search large image databases based on visual content. The key components of CBIR systems are feature extraction, indexing, and system design. Feature extraction involves extracting information about images' colors, textures, shapes, and spatial locations. Effective features and indexing techniques are needed to make CBIR scalable for large image collections. Performance is evaluated based on how well systems return relevant images.
This document discusses various image compression standards and techniques. It begins with an introduction to image compression, noting that it reduces file sizes for storage or transmission while attempting to maintain image quality. It then outlines several international compression standards for binary images, photos, and video, including JPEG, MPEG, and H.261. The document focuses on JPEG, describing how it uses discrete cosine transform and quantization for lossy compression. It also discusses hierarchical and progressive modes for JPEG. In closing, the document presents challenges and results for motion segmentation and iris image segmentation.
This document discusses image enhancement and restoration techniques in digital image processing. It describes various arithmetic and logical operations that can be performed on images, including addition, averaging, subtraction, multiplication/division, AND, and OR. These operations allow images to be combined, adjusted for brightness, and manipulated to enhance features or remove artifacts. Pixel value ranges must be normalized back to 0-255 after arithmetic operations.
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.
This presentation describes briefly about the image enhancement in spatial domain, basic gray level transformation, histogram processing, enhancement using arithmetic/ logical operation, basics of spatial filtering and local enhancements.
This document discusses different techniques for image segmentation. It begins by defining image segmentation as dividing an image into regions based on similarity and differences between adjacent regions. The main approaches discussed are discontinuity-based segmentation, which looks for sudden changes in pixel intensity (edges), and similarity-based segmentation, which groups similar pixels into regions. The document then examines various methods for detecting edges, linking edges, thresholding, and region-based segmentation using techniques like region growing and splitting/merging.
The document discusses image compression techniques. It explains that the goal of image compression is to reduce irrelevant and redundant image data to store and transmit images more efficiently. There are three main types of redundancy reduced in image compression: coding, interpixel, and psychovisual. Lossless compression preserves all image data using techniques like Huffman coding, run-length coding, arithmetic coding, and Lempel-Ziv coding. Lossy compression allows for some quality loss and higher compression ratios.
This document discusses various methods of data compression. It begins by defining compression as reducing the size of data while retaining its meaning. There are two main types of compression: lossless and lossy. Lossless compression allows for perfect reconstruction of the original data by removing redundant data. Common lossless methods include run-length encoding and Huffman coding. Lossy compression is used for images and video, and results in some loss of information. Popular lossy schemes are JPEG, MPEG, and MP3. The document then proceeds to describe several specific compression algorithms and their encoding and decoding processes.
Spatial filtering involves applying filters or kernels to images to enhance or modify pixel values based on neighboring pixel values. Linear spatial filtering involves taking a weighted sum of pixel values within the filter window. Common filters include averaging filters for noise reduction, median filters to reduce impulse noise while preserving edges, and sharpening filters like Laplacian filters and unsharp masking to enhance details.
This slide gives you the basic understanding of digital image compression.
Please Note: This is a class teaching PPT, more and detail topics were covered in the classroom.
This document discusses techniques for super resolution image reconstruction from multiple low resolution images. There are three main approaches: interpolation-based, example-learning-based, and multi-image based. Multi-image based super resolution attempts to reconstruct the original high resolution image by using information from a set of observed low resolution images. Key steps involved in multi-image super resolution include image registration to determine displacements between images, modeling the imaging process, and using techniques like the Irani and Peleg algorithm to estimate the blurring function and reconstruct the high resolution image.
This document presents a new approach called Pillar-K-means for image segmentation. Pillar-K-means applies a clustering algorithm called K-means to group pixels in images, but first optimizes the K-means process using an algorithm called Pillar. This is done to improve precision and reduce computation time for image segmentation. The document describes K-means clustering and its issues, then introduces Pillar-K-means which optimizes K-means initialization to enhance segmentation accuracy and speed. Experiments show Pillar-K-means improved over standard K-means.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
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
An Approach for Image Deblurring: Based on Sparse Representation and Regulari...IRJET Journal
This document proposes an approach for image deblurring based on sparse representation and a regularized filter. The approach splits the blurred input image into patches, estimates sparse coefficients for each patch using dictionary learning, updates the dictionary, and estimates the deblur kernel. The deblur kernel is applied using Wiener deconvolution and further processed with a regularized filter to recover the original image. The approach was tested on MATLAB and evaluation metrics like RMSE, PSNR, and SSIM along with visual analysis showed it performed better deblurring compared to existing methods.
Deep Local Parametric Filters for Image EnhancementSean Moran
The document proposes a new DeepLPF method for image enhancement using learnable parametric filters. DeepLPF estimates parameters for elliptical, graduated, and polynomial filters using a CNN to reproduce local image retouching practices. It introduces a novel architecture that regresses spatially localized filter parameters and a plug-and-play neural block with a filter fusion mechanism. Evaluation on benchmark datasets shows DeepLPF achieves state-of-the-art performance for tasks like classical image retouching and low-light enhancement, with an efficient model containing only a few neural weights.
Master defense presentation 2019 04_18_rev2Hyun Wong Choi
The document analyzes household electricity consumption data using K-means clustering and compares the results using silhouette scores on both the full and a reduced 1/8th dataset. Testing on the full and reduced datasets found that K=7 clusters produced the optimal silhouette score of 0.799 and 0.810 respectively, indicating the clustering results were similar even with a smaller dataset. The analysis demonstrates that K-means clustering can effectively analyze household electricity consumption data and produce consistent results even when the dataset is reduced in size.
An Approach for Image Deblurring: Based on Sparse Representation and Regulari...IRJET Journal
This document presents an approach for image deblurring based on sparse representation and a regularized filter. The approach involves splitting the blurred input image into patches, estimating sparse coefficients for each patch, learning dictionaries from the coefficients, and merging the patches. The merged patches are subtracted from the blurred image to obtain the deblur kernel. Wiener deconvolution with the kernel is then applied and followed by a regularized filter to recover the original image without blurring. The approach was tested on MATLAB and evaluation metrics like RMSE, PSNR, and SSIM showed it performed better than existing methods, recovering images with more details and contrast.
IRJET- Finding Dominant Color in the Artistic Painting using Data Mining ...IRJET Journal
This document discusses finding the dominant color in an artistic painting using data mining techniques. It proposes using k-means clustering via the OpenCV library in Python to cluster pixels in the image by color and determine the dominant color cluster. The document provides background on k-means clustering and other clustering algorithms. It then describes applying a faster k-means algorithm to the image pixels to efficiently identify the dominant color in 2-3 times fewer iterations than standard k-means. The proposed system architecture involves preprocessing the image, extracting pixel vectors, clustering the pixels into color groups using fast k-means, and identifying the dominant color cluster.
WEB IMAGE RETRIEVAL USING CLUSTERING APPROACHEScscpconf
Image retrieval system is an active area to propose a new approach to retrieve images from the
large image database. In this concerned, we proposed an algorithm to represent images using
divisive based and partitioned based clustering approaches. The HSV color component and Haar wavelet transform is used to extract image features. These features are taken to segment an image to obtain objects. For segmenting an image, we used modified k-means clustering algorithm to group similar pixel together into K groups with cluster centers. To modify Kmeans, we proposed a divisive based clustering algorithm to determine the number of cluster and get back with number of cluster to k-means to obtain significant object groups. In addition, we also discussed the similarity distance measure using threshold value and object uniqueness to quantify the results.
This document summarizes an internship report on image analysis of SEM images. It discusses various image processing and analysis techniques used for SEM images, including:
- Converting RGB images to grayscale and binary images
- Segmentation techniques like thresholding, clustering, watershed segmentation, and quick shift segmentation
- Introduction to graphs and Markov chain Monte Carlo methods like the Swendsen Wang method
Efficient Variable Size Template Matching Using Fast Normalized Cross Correla...Gurbinder Gill
In this presentation we propose the parallel implementation of template matching using Full Search using NCC as a measure using the concept of pre-computed sum-tables referred to as FNCC for high resolution images on NVIDIA’s Graphics Processing Units (GP-GPU’s)
Object Shape Representation by Kernel Density Feature Points Estimator cscpconf
This paper introduces an object shape representation using Kernel Density Feature Points
Estimator (KDFPE). In this method we obtain the density of feature points within defined rings
around the centroid of the image. The Kernel Density Feature Points Estimator is then applied to
the vector of the image. KDFPE is invariant to translation, scale and rotation. This method of
image representation shows improved retrieval rate when compared to Density Histogram
Feature Points (DHFP) method. Analytic analysis is done to justify our method and we compared our results with object shape representation by the Density Histogram of Feature Points (DHFP) to prove its robustness.
This document summarizes using Octave to build a linear regression model to predict profit based on population size:
- The model takes population data and minimizes error to draw a line relating predictions of profit to population
- The cost function is used in gradient descent to find the local minimum cost and optimal values for theta0 and theta1
- The model can then predict profits of around $4,500 for a population of 35,000 and $45,000 for a population of 70,000
Quality and size assessment of quantized images using K-Means++ clusteringjournalBEEI
In this paper, an amended K-Means algorithm called K-Means++ is implemented for color quantization. K-Means++ is an improvement to the K-Means algorithm in order to surmount the random selection of the initial centroids. The main advantage of K-Means++ is the centroids chosen are distributed over the data such that it reduces the sum of squared errors (SSE). K-Means++ algorithm is used to analyze the color distribution of an image and create the color palette for transforming to a better quantized image compared to the standard K-Means algorithm. The tests were conducted on several popular true color images with different numbers of K value: 32, 64, 128, and 256. The results show that K-Means++ clustering algorithm yields higher PSNR values and lower file size compared to K-Means algorithm; 2.58% and 1.05%. It is envisaged that this clustering algorithm will benefit in many applications such as document clustering, market segmentation, image compression and image segmentation because it produces accurate and stable results.
- The document analyzes electricity consumption at home using the K-means clustering algorithm on a dataset and with 1/8 of the original dataset.
- With the full dataset, the optimal number of clusters was found to be 7, with a silhouette score of 0.799. Using 1/8 of the data, the optimal number of clusters was also 7, with a similar silhouette score of 0.810.
- The analysis shows that even with a smaller dataset, the K-means clustering produced similar results to those from the original larger dataset, suggesting the approach could help analyze large datasets more efficiently.
The document discusses the preprocessing stages for classifying and extracting features from brinjal leaf images. The stages include:
1) Reading RGB images of leaf samples
2) Performing histogram equalization and resizing images to increase quality
3) Using k-means clustering to segment diseased portions of leaves from healthy portions for analysis
4) Converting images to HSI color space and extracting texture features using GLCM to classify images using SVM.
Final edited master defense-hyun_wong choi_2019_05_23_rev21Hyun Wong Choi
This document outlines a study analyzing household electricity consumption using K-means clustering. It begins with an introduction to electricity consumption measurement and existing optimization techniques. It then discusses the related work in machine learning algorithms and clustering methods. The document proposes using K-means clustering on household electricity consumption data from the UC Irvine Machine Learning Repository. It evaluates the optimal number of clusters using silhouette scores and Calinski-Harabasz indexes. The analysis finds that K=7 clusters is optimal for both the full and reduced 1/8 datasets. The document concludes the approach provides meaningful predictions for analyzing household electricity usage.
master defense hyun-wong choi_2019_05_14_rev19Hyun Wong Choi
1) The document proposes using K-means clustering to analyze electricity consumption data from homes. It uses a dataset containing over 2 million measurements of electricity usage gathered from a home in France between 2006-2010.
2) The approach uses silhouette scores and Calinski-Harabasz indices to determine the optimal number of clusters is 7 for both the full and reduced 1/8th datasets. Graphs of silhouette scores against number of clusters support this.
3) The analysis finds that reducing the dataset to 1/8th its original size does not change the optimal number of clusters, showing K-means clustering produces consistent results even with smaller amounts of data.
master defense hyun-wong choi_2019_05_14_rev19Hyun Wong Choi
The document discusses using a K-means clustering algorithm to analyze electricity consumption data from households. It outlines introducing electricity consumption data, related work on clustering algorithms, and the proposed approach of using K-means clustering and different indices like Silhouette score and Calinski-Harabasz index to analyze the data. The document evaluates using K-means clustering on the full dataset and a reduced 1/8th dataset, finding an optimal cluster number of 7 both times. It concludes the approach provides efficient predictions and the analysis could help optimize electricity consumption and costs.
1) The document proposes using K-means clustering to analyze electricity consumption data from homes. It uses a dataset containing over 2 million measurements of electricity usage gathered from a home in France between 2006-2010.
2) The approach uses silhouette scores and Calinski-Harabasz indices to determine the optimal number of clusters is 7 for both the full and reduced 1/8th datasets. K-means clustering is then applied with 7 clusters and produces meaningful results for analyzing electricity consumption patterns.
3) The analysis finds that K-means clustering of the electricity consumption data, whether the full dataset or a reduced 1/8th version, consistently identifies 7 as the optimal number of clusters. This suggests K-means
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3. Clustering
•Group of collection of points into clusters
• Patterns are extracted from variables without
analysing any variable – unsupervised learning
•The points in each cluster are closer to one
another and far from points in other clusters
4. K-Means Clustering
• Unsupervised learning algorithm.
• Grouping of different data points which are like each other.
• Forming dissimilar groups and each group containing similar data points
• To partition data into distinct K clusters. K is defined by user.
•Works on predefined distinct K clusters in which each data point belongs to a
particular cluster.
5. Cost Function
•The goal is to minimize within-cluster dissimilarity.
•The Cost function(J) is:
J= 𝑖=1
𝑁
𝑘=1
𝐾
𝑟𝑖𝑘 𝑥(𝑖)
− 𝜇𝑘
2
Where= 𝑥(𝑖)
are data points
𝜇𝑘 is center of cluster k.
𝑟𝑖𝑘 = 1 if 𝑥(𝑖)
belongs to cluster k and 0 if it doesn’t belong to cluster k.
k = 1,.,…,K where K is the number of clusters provided
N is the number of total data points
•J represents sum of distances between each data 𝑥(𝑖)
and cluster center 𝜇𝑘.
• Cost function J is minimized for optimal clustering.
•After each iteration 𝜇𝑘 is obtained by the formula
𝜇𝑘 =
𝑖=1
𝑁
𝑟𝑖𝑘𝑥𝑖
𝑖=1
𝑁
𝑟𝑖𝑘
6. K- Means Algorithm
Step1- Randomly initialize the K data points as initial centroids for K clusters
Step2- Until the cluster centers are changed or for max iteration
◦ Allocate each data point to centroid whose data point is nearest
◦ Replace the cluster centres with the mean of the element in their
clusters
end
9. Image Compression
•An image is made up of small intensity dots called pixels.
•Each pixel contains three values which are the values of intensities of Red, Blue, Green colors
respectively for that pixel
•Reducing the size that an image takes while storing and transmitting
• Reducing the number of colors occurred in image to the most frequent colors appearing in it
• Essentially forming the different clusters of frequent occurring colors present in the image by
using pixel values
10. Original and Compressed Image-Parrot
𝑡𝑛= time taken for K- means algorithm to run for n iterations
Fig1b. 𝑡10= 1min 42sec
Fig1a. Original Image
11. Original and Compressed Image-Parrot
Fig1c. 𝑡10= 6min 32sec Fig1d. 𝑡10= 12min 42sec Fig.1e 𝑡10= 50min 40sec
13. Original and Compressed Image-Scenery
Fig2e. 𝑡10= 63 min 30sec
Fig2d. 𝑡10= 15 min 30sec
Fig2c. 𝑡10= 8 min 15sec
14. Uses of Image Compression
•Lesser data for storing the compressed image compared to original image,
reducing the cost of storage and transmission
• K-Means is utilized to compress visual contents in vast nexus of social
messaging app for its faster transmission and less storage utilization
•Used for archival purpose and for medical imaging, technical drawings
•Widely used in remote sensing via satellite, television broadcasting, for
capturing and transmitting satellite images
15. Results
Actual Size of Image of
Parrot
Number of clusters(K)
Specified while
Compressing the image
Reduced Size of the
Image of Parrot
1,87,236 bytes
100 52,032 bytes
20 54,888 bytes
15 54,888 bytes
12 54,351 bytes
2 43,616 bytes
Table1. Results of K-means clustering applied on parrot.jpg
16. Results
Actual Size of Image of
Scenery
Number of clusters(K)
Specified while
Compressing the image
Reduced Size of the
Image of Scenery
5,50,287 bytes
50 1,01,404 bytes
10 1,01,813 bytes
5 95,616 bytes
2 83,729 bytes
Table2. Results of K-means clustering applied on scenery.jpg
17. Drawbacks of K-means
• Gets sluggish as the size of data(image) increases.
• Time taken by algorithm increases as the number of cluster (K) increases.
• Results may represent a suboptimal local minimum.
• Works only for linear or almost linear boundaries
18. References
•Xing Wan (2019), “Application of K-means Algorithm in Image Compression”, IOP Conference
Series: Materials Science and Engineering, 563 052042,
•B. Reddaiah “A Study on Image Compression and its Applications”, International Journal of
Computer Applications, Volume 177 – No. 38, February 2020
•Hartigan, J. A., Wong, M. A. (1979). Algorithm as 136: a k-means clustering algorithm. Journal of
the Royal Statistical Society, 28(1), 100-10
•https://www.simplilearn.com/tutorials/machine-learning-tutorial/k-means-clustering-algorithm
•Van der Geer, J., Hanraads, J.A.J., Lupton, R.A. (2010) The art of writing a scientific article. J. Sci.
Commun., 163: 51–59.