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# Tejus Guptatejus-gupta

Created Oct 10, 2018
View figure.tex
 \begin{figure}[t] \begin{center} \includegraphics[width=1in,height=1in]{images/flute.png} \hspace{0.1cm} \includegraphics[width=1in,height=1in]{pictures/adv12.png} \hspace{0.1cm} \includegraphics[width=1in,height=1in]{images/carpenter_kit.png} \hspace{0.1cm} \includegraphics[width=1in,height=1in]{images/iron.png} \end{center}
Last active Oct 10, 2018
View setup.sh
Created Oct 9, 2018
Code for median filter based on 'A coarse-to-fine algorithm for fast median filtering of image data with a huge number of levels' by Alparone et al.
View median_filter_multilevel.jl
 using Images, TestImages, Statistics, BenchmarkTools, Test img = testimage("lena_gray") function median_filter_multilevel(img::Array{Gray{Normed{T,f}}, 2}, window_size::Tuple{Int64,Int64}) where {T,f} nlevels = 2^f course_nlevels = 2^(f>>1) bin_bits = f>>1
Created Oct 6, 2018
Last active Oct 4, 2018
Code for median filter based on 'A Fast Two-Dimensional Median Filtering Algorithm' by Huang, Yang and Tang.
View median_filter.jl
 using Images, TestImages, Statistics, BenchmarkTools, Test img = testimage("lena_gray") high_resolution_img = convert(Array{Gray{Normed{UInt16,16}}, 2}, img) function median_filter(img::Array{Gray{Normed{T,f}}, 2}, window_size::Tuple{Int64,Int64}) where {T,f} function update_median(median_val, n_pixels_below_median, hist, half_pixels) if n_pixels_below_median <= half_pixels for val in median_val:2^f
Last active Jul 2, 2020
Pytorch for computer vision
Created Mar 27, 2018

# 📋 Project overview: Parallelizing Apollo

Status Just starting 🌱 / In progress 🔨 / Ready to go 🚀
Description What do you have in mind?
Deadline When do you want to finish?

# Timeline

• What are the key dates?
Created Mar 24, 2018
View literature-review.md

# Automated Fiducial Localization

Last active Mar 24, 2018
View vis_example.cpp
 #include boost::shared_ptr cloudVis (pcl::PointCloud::ConstPtr cloud) { boost::shared_ptr viewer (new pcl::visualization::PCLVisualizer ("3D Viewer")); viewer->setBackgroundColor (0, 0, 0); viewer->addPointCloud (cloud, "sample cloud"); viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "sample cloud"); viewer->addCoordinateSystem (1.0); viewer->initCameraParameters ();
Last active Sep 21, 2017
ImageSegmentation Blog
View blog.md

# Introduction

Over the last three months, we've been working on ImageSegmentation.jl - a collection of image segmentation algorithms written in Julia. Under the mentorship of Tim Holy, we have implemented several popular image segmentation algorithms and designed a consistent interface for using these algorithms. This blog post describes image segmentation, why it's useful and how to use the tools in this package.

Image Segmentation is the process of partitioning the image into regions that have similar attributes. Image segmentation has various applications e.g, medical image segmentation, image compression and is used as a preprocessing step in higher level vision tasks like object detection and optical flow.

Image segmentation is not a mathematically well-defined problem: for example, the only lossless representation of the input image would be to say that each pixel is its own segment. Yet this does not correspond to our own intuitive notion that