<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Image Processing on felladog</title><link>https://utsavdarlami.github.io/blogs/categories/image-processing/</link><description>Recent content in Image Processing on felladog</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Sun, 03 Mar 2024 20:55:59 +0545</lastBuildDate><atom:link href="https://utsavdarlami.github.io/blogs/categories/image-processing/index.xml" rel="self" type="application/rss+xml"/><item><title>image binarization and pixel count</title><link>https://utsavdarlami.github.io/blogs/posts/20230829193605-image_binarization_and_pixel_count/</link><pubDate>Tue, 29 Aug 2023 19:36:00 +0545</pubDate><guid>https://utsavdarlami.github.io/blogs/posts/20230829193605-image_binarization_and_pixel_count/</guid><description>References : https://haesleinhuepf.github.io/BioImageAnalysisNotebooks/intro.html Questions : what is thresholding and binarization how can we calculate the relative area of certain object in the image. One of the critical aspects of image analysis involves quantifying pixel data, a process that&amp;rsquo;s integral to various scientific and medical applications. In this blog, we&amp;rsquo;ll try to explore the process of converting microscopic images into binary representations for pixel counting.
Steps involved Conversion to Gray Images First, raw microscopic images are converted to grayscale images.</description></item><item><title>edge filters</title><link>https://utsavdarlami.github.io/blogs/notes/2021-05-31--03-54-38z--edge_filters/</link><pubDate>Mon, 31 May 2021 09:39:00 +0545</pubDate><guid>https://utsavdarlami.github.io/blogs/notes/2021-05-31--03-54-38z--edge_filters/</guid><description>References :
Done Reading Reading https://nptel.ac.in/courses/106/106/106106224/ [IIT Madras Week 2 part 1] Cornell CV lecture on Edge https://szeliski.org/Book/ [Book Chapter 3] https://www.wikiwand.com/en/Sobel%5Foperator Joseph Redmon Lectures https://evergreenllc2020.medium.com/fundamentals-of-image-gradients-and-edge-detection-b093662ade1b To read: Questions :
Criteria for a good edge detector Good detection find all real edges, ignoring noise or other artifacts Good Localization detect edges as close as possible to true edges Single Response return one point only for each true edge point from skimage.</description></item><item><title>image filtering</title><link>https://utsavdarlami.github.io/blogs/notes/2021-05-28--12-02-59z--image_convolution/</link><pubDate>Fri, 28 May 2021 17:47:00 +0545</pubDate><guid>https://utsavdarlami.github.io/blogs/notes/2021-05-28--12-02-59z--image_convolution/</guid><description>References : Done Reading Reading https://vincmazet.github.io/ftip/convolution.html https://nptel.ac.in/courses/106/106/106106224/ [IIT Madras] Cornell CV Lecture on Filter https://szeliski.org/Book/ [Book Chapter 3] Non-local means denoising for preserving textures To read: https://www.cs.tut.fi/~foi/papers/ICIP2019_Ymir.pdf [BM3D] https://stackoverflow.com/questions/40596026/what-does-entropy-mean-in-this-context Questions : Modify image pixels based on some function of a local neighbourhood of each pixel.
Why filter a image To sharpen, smooth, intensify or enhance a imag Linear filters Output pixel’s value is determined as a weighted sum of input pixel values within a small neighborhood N,</description></item></channel></rss>