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This document provides goals and instructions for a course project on image restoration. The goals are to learn how to restore blurred images using the VEGA program and to evaluate restoration quality using measures like PSNR, BSNR and ISNR. Students are asked to choose a blurred image, restore it using different filters in VEGA, compare the results, optimize parameters, and write a report on the PSNR measurements. Extra credit involves designing Matlab functions to calculate BSNR and ISNR and including these in the evaluation report. Restored images and the report are to be saved in a designated project subfolder.
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This project involves comparing the efficiency of spatial domain linear filters for image processing. Functions will be written for calculating error metrics between images, adding Gaussian noise to images, and applying 3x3 linear filters to images. A script will utilize these functions to measure error between clean, noisy, and filtered versions of test images. Two linear filters will be applied to images with 0.2 and 0.4 standard deviation noise added. Results will be analyzed to determine the optimal linear filter and noise level for each test image. Reports will justify conclusions and include image files and a table of results.
Project 1
This document outlines the goals and steps for a course project in image processing and computer vision. The project involves writing Matlab functions to analyze image statistics, create histograms, perform histogram equalization, and linear contrast correction. A script will then use these functions to analyze two images by measuring their statistics, enhancing contrast through histogram equalization and linear correction, measuring the enhanced images' statistics and plotting histograms. The results will be saved in a project subfolder.
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This document outlines the goals of a course project on order statistic filters and computer vision. The project involves:
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2) Adding Gaussian noise to images from a previous project and filtering the noisy images using the designed function.
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Project 6
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Project 4
This project aims to detect and filter impulse noise from images using differential rank impulse detection followed by median filtering. Specifically, the student must design a Matlab function that takes an image, rank interval length r, and threshold s as inputs to detect noisy pixels and apply median filtering only to those pixels. The function should handle border effects using mirroring. The student will then add random impulse noise to a test image, filter it using their function with different parameter values, and report the best filtering result based on RMSE/PSNR metrics in a brief technical report.
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This document outlines the goals of a course project on order statistic filters and computer vision. The project involves:
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Fogler elements of chemical reaction engineering 3rd
Elementos de ingeniería de las reacciones químicas de Scott Fogler 3° edición en inglés
Cracking sin secretos
The document discusses the history of chocolate, from its origins in Mesoamerica to its spread throughout Europe. Cacao beans were first used as currency and medicine by the Olmecs and Mayans before Spanish conquistadors brought chocolate to Europe in the 16th century. It then grew in popularity among the elite classes and eventually became available to the masses as the Industrial Revolution made production more efficient.
A critical review of academic institutions and quality in higher education
ACCREDITATION, INNOVATION, RESEARCH, EXCELLENCE, AND BEST RANKING IN UNIVERSITIES
PRENTISS TUCKER :: The lost key
THE LOST KEY
AN EXPLANATION AND APPLICATION
of the
MASONIC SYMBOLS
by
PRENTISS TUCKER, B.A.
Unſpeakable Preſs
333 Via Nefanda, Lelag, Leng
2005
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Fogler elements of chemical reaction engineering 3rd
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A critical review of academic institutions and quality in higher education
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1 of 6
LAB 5: IMAGE FILTERING
ECE180: Introduction to Signal Processing
OVERVIEW
You have recently learned about the convolution sum that serves as the basis of the FIR filter difference equation. The filter
coefficient sequence {𝑏𝑘} – equivalent to the filter’s impulse response ℎ[𝑛] – may be viewed as a one-dimensional moving
window that slides over the input signal 𝑥[𝑛] to compute the output signal 𝑦[𝑛] at each time step. Extending the moving
window concept to a 2-D array that slides over an image pixel array provides a useful and popular way to filter an image.
In this lab project you will implement two types of moving-window image filters, one based on convolution and the other
based on the median value of the pixel grayscale values spanned by the window. You will also gain experience with the
built-in image convolution filter imfilter.
OUTLINE
1. Develop and test a 33 median filter
2. Develop and test a 33 convolution filter
3. Evaluate the median and convolution filters to reduce noise while preserving edges
4. Study the behavior of various 33 convolution filter kernels for smoothing, edge detection, and sharpening
5. Learn how to use imfilter to convolution-filter color images, and study the various mechanisms offered by
imfilter to deal with boundary effects
PREPARATION – TO BE COMPLETED BEFORE LAB
Study these tutorial videos:
1. Nested “for” loops -- http://youtu.be/q2xfz8mOuSI?t=1m8s (review this part)
2. Functions -- http://youtu.be/0zTmMIh6I8A (review as needed)
Ensure that you have added the ECE180 DFS folders to your MATLAB path, especially the “images” and “matlab” subfolders.
Follow along with the tutorial video http://youtu.be/MEqUd0dJNBA, if necessary.
LAB ACTIVITIES
1. Develop and test a 33 median filter function:
1.1. Implement the following algorithm as the function med3x3:
TIP: First implement and debug the algorithm as a script and then convert it to a function as a final step. Use any
of the smaller grayscale images from the ECE180 “images” folder as you develop the function, or use the test
image X described in the Step 1.2.
(a) Create the function template and save it to an .m file with the same name as the function,
(b) Accept a grayscale image x as the function input,
http://youtu.be/q2xfz8mOuSI?t=1m8s
http://youtu.be/0zTmMIh6I8A
http://youtu.be/MEqUd0dJNBA
2 of 6
(c) Copy x to the output image y and then initialize y(:) to zero; this technique creates y as the same size and
data type as x,
(d) Determine the number of image rows and columns (see size),
(e) Loop over all pixels in image x (subject to boundary limits):
Extract a 33 neighborhood (subarray) about the current pixel,
Flatten the 2-D array to a 1-D array,
Sort the 1-D array values (see sort),
Assign the middle value of the sorted array to the current output pixel, and
(f) Return the median-filtered image y.
1.2. Enter load lab_5_verify to load the
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IMAGE DE-NOISING USING DEEP NEURAL NETWORK
Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level representations of input data which has been introduced to many practical and challenging learning problems successfully. The primary goal of deep learning is to use large data to help solving a given task
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The objective of image classification is to classify each pixel into only one class (crisp or hard classification) or to associate the pixel with many classes (fuzzy or soft classification). The classification techniques may be categorized either on the basis of training process (supervised and unsupervised) or on the basis of theoretical model (parametric and non-parametric).
Unsupervised classification is where the groupings of pixels with common characteristics are based on the software analysis of an image without the user providing sample classes. The computer uses techniques to determine which pixels are related and groups them into classes. The user can specify which algorism the software will use and the desired number of output classes but otherwise does not aid in the classification process. However, the user must have knowledge of the area being classified when the groupings of pixels with common characteristics produced by the computer have to be related to actual features on the ground (such as waterbodies, developed areas, forests, etc.).
Supervised classification is based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image. Input classes are selected based on the knowledge of the user. The user also sets the bounds for how similar other pixels must be to group them together. These bounds are often set based on the spectral characteristics of the input classes (AOI), plus or minus a certain increment (often based on “brightness” or strength of reflection in specific spectral bands). The user also designates the number of classes that the image is classified into.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Analytical study of feature extraction techniques in opinion mining
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Decision making both on individual and organizational level is always accompanied by the search of other’s opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum discussions, blogs, micro-blogs, Twitter etc provide a rich anthology of sentiments. This user generated content can serve as a benefaction to market if the semantic orientations are deliberated. Opinion mining and sentiment analysis are the formalization for studying and construing opinions and sentiments. The digital ecosystem has itself paved way for use of huge volume of opinionated data recorded. This paper is an attempt to review and evaluate the various techniques used for opinion and sentiment analysis.
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Project 9
This document outlines the goals of a course project on color image processing and BM3D filtering. It provides 4 noisy test images - 2 with color Gaussian noise, 1 with color impulse noise, and 1 with monochromatic noise. It instructs the student to apply rank-order filtering to suppress Gaussian noise and median filtering to suppress impulse noise. It also instructs the student to process color noise by filtering individual color channels and compare to filtering the luminosity channel only. Bonus credit is offered for using custom Matlab code or applying the BM3D filter to individual channels. The student is asked to prepare a report on PSNR measurements and submit results.
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Localization and recognition are tasks of computer vision that involve identifying the spatial coordinates of an object (localization) and determining what type of object it is (recognition). Cross-correlation is a classical tool that can be used to solve localization and recognition problems by measuring the similarity between two signals or images. It works by calculating the dot product of one signal or image while sliding it over another, with a larger value indicating a more similar match. The Fourier transform of the cross-correlation is equal to the product of the Fourier transforms of the two inputs. This property allows for an efficient computation of cross-correlation using inverse Fourier transforms. If an image is contained within another, their cross-correlation will have a strong maximum at the coordinates where
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Digital color images are characterized by 3 or 4 dimensional vectors representing pixels in a color space model like RGB or CMYK. RGB uses additive color mixing of red, green and blue light, while CMYK uses subtractive color mixing of cyan, magenta, yellow and black inks. Color images can be processed by manipulating individual color channels separately or by extracting and processing luminosity from the RGB channels. Transforming between RGB and luminosity (YUV) space allows separate processing and recombination of luminosity and chromatic information. YUV space is also used in JPEG compression by applying higher compression rates to the chromatic channels U and V.
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This document discusses methods for restoring blurred images, including modeling image degradation using convolution with a point spread function in the spatial and frequency domains. Common point spread functions like Gaussian and motion blur are described. Methods for solving the deconvolution problem to restore blurred images are presented, including inverse filtering, Wiener filtering, regularization filtering, and evaluating the quality of restored images using metrics like PSNR, BSNR, and ISNR.
Lecture 12
BM3D is a denoising algorithm that uses block matching and collaborative filtering in 3D transform domains. It finds similar 2D image fragments, stacks them into 3D groups, applies a 3D transformation, shrinks the transform coefficients to attenuate noise, and inverse transforms to estimate denoised fragments. BM3D improves on previous methods by exploiting inter-fragment correlation both within and between similar image blocks grouped together. It provides multiple estimates for each pixel by processing overlapping blocks, then fuses the estimates to produce the final denoised image.
Lecture 10
Frequency domain filtering involves modifying the Fourier transform of an image by multiplying it with a filter function. Periodic noise appears as unusually high magnitudes in the spectral coefficients corresponding to the noise frequency. This noise can be reduced or removed in the frequency domain by correcting these coefficients using techniques like thresholding or median filtering in local neighborhoods. Filtering in blocks instead of the whole image can better handle non-uniform quasi-periodic noise.
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it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
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Project 8
- 1. CS-467 Image processing and Computer Vision Course Project 8 Goals: 1) to learn how to restore blurred images Use VEGA program 1. Choose an image ( , )f x y from a collection of blurred images (see classdataIMAGESBLUR EXAMPLES) degraded by Gaussian, motion, rectangular, and defocus blur. 2. Restore all 4 versions of this blurred image in VEGA using an image restoration tool. First, use a Restoration“Blur Recognition” tool and then, after a type of blur and its parameters are recognized, use Restoration “Image Restoration” tool to restore an image. Use Wiener filter, Tikhonov regularization, and Inverse Filter. Compare their efficiency. Try to find a better parameter for the recognized type of blur, if you are not satisfied with the restoration results. Estimate the restoration quality using PSNR as a measure. 3. Prepare a brief report based on the measured PSNRs. Bonus (50 % extra credit). Design Matlab functions for measuring BSNR and ISNR (see slide 27 of the Lecture-11 presentation) and apply them along with PSNR to evaluate your results. In BSNR, M (noise variance) shall be used as one of input parameters), but in your experiments you may set 1σ = , since images in the “blurry” collection are not noisy. Include BSNR and ISNR in your report and make your conclusions based on all of PSNR, BSNR, and ISNR. Put your resulting images and the report in the subfolder Project 8 (you need to create it) located in the designated folder (named by your last name) in the folder sfs01classesCS 467 001Class Data (The folder sfs01classes is mapped from all the lab computers, so you can easily find it through File Explorer (Computer) in Windows 7. A shortcut to the Classes folder is also available on the desktop of the lab computers.