Open source adobe lightroom like
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This document outlines the requirements and design for an open source Lightroom-like photo editing application. It discusses implementing a graphical user interface with adjustment tools for brightness, contrast, and saturation. It also covers using threads for parallel execution and calculating histograms to avoid blocking the user interface. Adjustments are made by modifying pixel color values using a color matrix rather than opening images directly.
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Contrast stretching aims to increase the dynamic range of an image by transforming gray levels using a piecewise linear function. The locations of points (r1, s1) and (r2, s2) control the shape of this function. Histogram equalization produces an output image with a near-uniformly distributed histogram by mapping input gray levels to output levels based on the cumulative distribution function. It enhances contrast but may introduce noise or wash out images with most pixels at one end of the gray scale. Histogram matching transforms an image to match the histogram of a specified image by applying histogram equalization followed by the inverse transformation.
DIP_Lecture5.pdf
This document discusses various image processing techniques for contrast enhancement and histogram manipulation, including contrast stretching, gray-level slicing, histogram equalization, and histogram matching. Contrast stretching aims to increase the dynamic range of an image by transforming gray levels using a piecewise linear function. Histogram equalization produces an output image with a near-uniformly distributed histogram for contrast enhancement. Histogram matching enables generating an image with a specified histogram shape by applying histogram equalization followed by the inverse of another image's equalization. Examples are provided to illustrate applying these techniques and their effects.
R Language Introduction
The document outlines various statistical and data analysis techniques that can be performed in R including importing data, data visualization, correlation and regression, and provides code examples for functions to conduct t-tests, ANOVA, PCA, clustering, time series analysis, and producing publication-quality output. It also reviews basic R syntax and functions for computing summary statistics, transforming data, and performing vector and matrix operations.
Q-Metrics in Theory And Practice
A generalized class of normalized distance functions called Q-Metrics is described in this presentation. The Q-Metrics approach relies on a unique functional, using a single bounded parameter (Lambda), which characterizes the conventional distance functions in a normalized per-unit metric space. In addition to this coverage property, a distinguishing and extremely attractive characteristic of the Q-Metric function is its low computational complexity. Q-Metrics satisfy the standard metric axioms. Novel networks for classification and regression tasks are defined and constructed using Q-Metrics. These new networks are shown to outperform conventional feed forward back propagation networks with the same size when tested on real data sets.
Q-Metrics in Theory and Practice
A generalized class of normalized distance functions called Q-Metrics is described in this presentation. The Q-Metrics approach relies on a unique functional, using a single bounded parameter Lambda, which characterizes the conventional distance functions in a normalized per-unit metric space. In addition to this coverage property, a distinguishing and extremely attractive characteristic of the Q-Metric function is its low computational complexity. Q-Metrics satisfy the standard metric axioms. Novel networks for classification and regression tasks are defined and constructed using Q-Metrics. These new networks are shown to outperform conventional feed forward back propagation networks with the same size when tested on real data sets.
Computer Vision - Image Filters
1. The document discusses various image filtering techniques, including correlation filtering, convolution, averaging filters, and Gaussian filters.
2. Gaussian filters are commonly used for smoothing images as they remove high-frequency components while maintaining edges. The scale parameter σ controls the amount of smoothing.
3. Median filters can reduce noise in images by selecting the median value in a local neighborhood, unlike mean filters which are susceptible to outliers.
Parallel Evaluation of Multi-Semi-Joins
Presentation given on VLDB 2016: 42nd International Conference on Very Large Data Bases.
Paper: http://dx.doi.org/10.14778/2977797.2977800
ArXiv: https://arxiv.org/abs/1605.05219
Poster: https://zenodo.org/record/61653 (doi 10.5281/zenodo.61653)
Gumbo Software: https://github.com/JonnyDaenen/Gumbo
Abstract
While services such as Amazon AWS make computing power abundantly available, adding more computing nodes can incur high costs in, for instance, pay-as-you-go plans while not always significantly improving the net running time (aka wall-clock time) of queries. In this work, we provide algorithms for parallel evaluation of SGF queries in MapReduce that optimize total time, while retaining low net time. Not only can SGF queries specify all semi-join reducers, but also more expressive queries involving disjunction and negation. Since SGF queries can be seen as Boolean combinations of (potentially nested) semi-joins, we introduce a novel multi-semi-join (MSJ) MapReduce operator that enables the evaluation of a set of semi-joins in one job. We use this operator to obtain parallel query plans for SGF queries that outvalue sequential plans w.r.t. net time and provide additional optimizations aimed at minimizing total time without severely affecting net time. Even though the latter optimizations are NP-hard, we present effective greedy algorithms. Our experiments, conducted using our own implementation Gumbo on top of Hadoop, confirm the usefulness of parallel query plans, and the effectiveness and scalability of our optimizations, all with a significant improvement over Pig and Hive.
Aptitude with handy-formulae
This document contains a table of contents for mathematics formulas covering topics such as numbers, HCF and LCM, percentages, profit and loss, ratio and proportion, partnership, and more. Some key formulas and concepts include:
- The formula for finding the greatest common factor (HCF) and least common multiple (LCM) of two numbers.
- Percentage formulas for expressing a value as a percentage, finding percentage increase or decrease, and compound interest.
- Formulas for finding profit, loss, selling price, and the effects of depreciation over time.
- Proportion concepts like using the product of extremes equals product of means.
- Formulas for finding time taken, speed, and distances
Image enhancement
Digital image enhancement involves modifying images to improve visual quality. It can be done in the spatial or frequency domain. Spatial domain enhancement works directly with pixel values. Key point processing techniques in the spatial domain include: digital negative, contrast stretching, thresholding, gray level slicing, bit plane slicing, and dynamic range compression. These techniques apply mathematical transforms to pixel values to darken or lighten regions of the image for better visualization.
Tao Fayan_Iso and Full_volume rendering
This document describes algorithms for iso-surface and full volume rendering. It discusses computing gradients at sample points using central differencing, shading using a standard illumination model, and implementing transfer functions for color (RGB) and opacity (alpha). Pseudocode is provided for the main algorithms, gradient computation, transfer functions, and scaling/rotation implementations. Key aspects covered include ray casting, sampling, interpolation, thresholding, and compositing color and opacity along rays.
Hprec7.1
This document discusses graphs of trigonometric functions sine, cosine, and tangent. It contains examples and problems involving graphing these functions using tables of values and graphing calculators. It also discusses key concepts like the domain and range of trig functions as well as asymptotes of the tangent function.
Foliumcheatsheet
Folium is a Python library for interactive maps that allows users to easily create and publish interactive web maps. Some key features of Folium include adding markers, polylines, polygons and other shapes to maps. It also supports adding popups and labels to features, customizing basemaps and tiles, integrating geospatial data formats, and using plugins to add additional functionality like search, drawing and heatmap tools. Folium works with common Python data structures and geospatial file formats and generates HTML and JavaScript to display interactive maps in the Jupyter notebook or standalone web pages.
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Open source adobe lightroom like
- 1. Open Source Adobe Lightroom like Pierre-Loic Chevillot
- 2. Requirements Richfeatured Graphical User Interface Adjustment Tools Brightness and Contrast Saturation Histogram Threads
- 4. Adjustment Library Create as a Dynamic Link Library Modularity Functions called on demand Adjustment tools Brightness Contrast Saturation
- 5. Brightness and Contrast float v = 0; Modification made by v = (float)(cValue + 100) / 100f; the class Color matrix float m = 0.5f * (1.0f - v) + Avoid to open the (float)bValue / 100f; image pixel per pixel to cm = new ColorMatrix(new apply the modification float[][]{ new float[]{v,0,0,0,0}, Contrast value are new float[]{0,v,0,0,0}, multiply to color new float[]{0,0,v,0,0}, component new float[]{0,0,0,1,0}, Brightness value is add new float[]{m,m,m,0,1} to the component });
- 6. Saturation float red = (float) 0.3086; float green = (float) 0.6094; Increase the intensity float blue = (float) 0.0820; of color float sat = (float) (satValue + Color component is weight 100) / 100f ; float redSaturation = (1 - sat) * Weight of the red + sat; float redSaturationComp = (1 - sat) * red; float greenSaturation = (1 - sat) * component green + sat; depends of the luminance float greenSaturationComp = (1 - sat) * green; float blueSaturation = (1 - sat) * blue + sat; Values given by float blueSaturationComp = (1 - sat) * blue; Adobe
- 7. Threads Parallel execution 3 threads Main application Histogram calculation Paint Handler Mutual exclusion Semaphore Delegate function Function pointer Permit access to GUI from a thread
- 8. Conclusion