<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Deep-Image-Labeling on Selvam Subbiah</title><link>https://selvamsubbiah.com/tags/deep-image-labeling/</link><description>Recent content in Deep-Image-Labeling on Selvam Subbiah</description><generator>Hugo -- 0.154.3</generator><language>en-us</language><lastBuildDate>Sun, 05 Dec 2021 00:00:00 +0000</lastBuildDate><atom:link href="https://selvamsubbiah.com/tags/deep-image-labeling/index.xml" rel="self" type="application/rss+xml"/><item><title>TensorFlow.js - Semantic Image Segmentation in browser using DeepLab model</title><link>https://selvamsubbiah.com/semantic-image-segmentation-in-browser-using-deeplab/</link><pubDate>Sun, 05 Dec 2021 00:00:00 +0000</pubDate><guid>https://selvamsubbiah.com/semantic-image-segmentation-in-browser-using-deeplab/</guid><description>&lt;p&gt;In Computer Vision, one of the main challenges is to understand an image at a higher level like the way we humans do, i.e., to identify the objects, their location and their surroundings to make sense of the scene captured in the image. &lt;a href="https://en.wikipedia.org/wiki/Image_segmentation" target="_blank" rel="noopener noreferrer"&gt;Semantic Image Segmentation&lt;/a&gt;
is the process of identifying the objects present in the image and labelling each pixel by associating it to an identified object class. Semantic Image Segmentation can be applied to a wide variety of use cases such as human body segmentation, separating the background from foreground in an image, and understanding the surroundings for self-driving cars, just to name a few.&lt;/p&gt;</description></item></channel></rss>