tejus-gupta

\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}

tejus-gupta

mkdir train_set
cd train_set
wget https://gist.githubusercontent.com/tejus-gupta/3d4564e624cad79691706a5c1303f4c6/raw/3cafe4877f981e3f3c481727d0a90db519a4e95b/download.py
python download.py
unzip -qq masks.zip
unzip -qq train_data.zip
cd ..
git clone https://github.com/tejus-gupta/Segmentation
cd Segmentation
git checkout modelD

tejus-gupta

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

tejus-gupta

import requests

def download_file_from_google_drive(id, destination):
    def get_confirm_token(response):
        for key, value in response.cookies.items():
            if key.startswith('download_warning'):
                return value

        return None

tejus-gupta

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

tejus-gupta

Semantic Segmentation

A Recurrent Neural Network-based Model for Semantic Segmentation
PyramidNet
PSPNet
Semantic Segmentation Models
Semantic Segmentation Models
FCN Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network
PointNet

tejus-gupta

📋 Project overview: Parallelizing Apollo

Status	Just starting 🌱 / In progress 🔨 / Ready to go 🚀
Team	Start with you and add others with @mentions
Description	What do you have in mind?
Deadline	When do you want to finish?

Timeline

• What are the key dates?

tejus-gupta

Automated Fiducial Localization

tejus-gupta

#include <pcl/visualization/cloud_viewer.h>

boost::shared_ptr<pcl::visualization::PCLVisualizer> cloudVis (pcl::PointCloud<pcl::PointXYZ>::ConstPtr cloud)
{
  boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer (new pcl::visualization::PCLVisualizer ("3D Viewer"));
  viewer->setBackgroundColor (0, 0, 0);
  viewer->addPointCloud<pcl::PointXYZ> (cloud, "sample cloud");
  viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "sample cloud");
  viewer->addCoordinateSystem (1.0);
  viewer->initCameraParameters ();

tejus-gupta

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