- Extraction of Objects In Images and Videos Using 5 Lines of Code - Mar 25, 2021.
PixelLib is a library created for easy integration of image and video segmentation in real life applications. Learn to use PixelLib to extract objects In images and videos with minimal code.
- 10 Amazing Machine Learning Projects of 2020 - Mar 15, 2021.
So much progress in AI and machine learning happened in 2020, especially in the areas of AI-generating creativity and low-to-no-code frameworks. Check out these trending and popular machine learning projects released last year, and let them inspire your work throughout 2021.
- Microsoft Uses Transformer Networks to Answer Questions About Images With Minimum Training - Jan 18, 2021.
Unified VLP can understand concepts about scenic images by using pretrained models.
- Change the Background of Any Video with 5 Lines of Code - Dec 7, 2020.
Learn to blur, color, grayscale and create a virtual background for a video with PixelLib.
- Change the Background of Any Image with 5 Lines of Code - Nov 9, 2020.
Blur, color, grayscale and change the background of any image with a picture using PixelLib.
- Auto Rotate Images Using Deep Learning - Jul 14, 2020.
Follow these 5 simple steps to auto rotate images and get the right angle in human photos using computer vision.
- Easy Image Dataset Augmentation with TensorFlow - Feb 13, 2020.
What can we do when we don't have a substantial amount of varied training data? This is a quick intro to using data augmentation in TensorFlow to perform in-memory image transformations during model training to help overcome this data impediment.
- How to Convert a Picture to Numbers - Jan 6, 2020.
Reducing images to numbers makes them amenable to computation. Let's take a look at the why and the how using Python and Numpy.
- How to Convert an RGB Image to Grayscale - Dec 18, 2019.
This post is about working with a mixture of color and grayscale images and needing to transform them into a uniform format - all grayscale. We'll be working in Python using the Pillow, Numpy, and Matplotlib packages.
- Monash University: Lecturers / Senior Lecturers – Digital Health, Image Analytics [Melbourne, Australia] - Jul 19, 2019.
Monash is seeking 2 x Lecturer / Senior Lecturer within the space of Digital Health - Image Analytics. Digital Health is a fascinating cross-university, cross-faculty, multidisciplinary space with enormous practical real-world and societal benefits that draws from and contributes to a multiplicity of research areas.
- Computer Vision for Beginners: Part 1 - Jul 17, 2019.
Image processing is performing some operations on images to get an intended manipulation. Think about what we do when we start a new data analysis. We do some data preprocessing and feature engineering. It’s the same with image processing.
- End-to-End Machine Learning: Making videos from images - May 23, 2019.
Video is a natural way for us to understand three dimensional and time varying information. Read this short post on how to achieve the creation of videos from still images.
- Preprocessing for Deep Learning: From covariance matrix to image whitening - Oct 10, 2018.
The goal of this post/notebook is to go from the basics of data preprocessing to modern techniques used in deep learning. My point is that we can use code (Python/Numpy etc.) to better understand abstract mathematical notions!
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- Basic Image Data Analysis Using Python – Part 4 - Oct 5, 2018.
Accessing the internal component of digital images using Python packages helps the user understand its properties, as well as its nature.
- Basic Image Data Analysis Using Python – Part 3 - Sep 28, 2018.
Accessing the internal component of digital images using Python packages becomes more convenient to help understand its properties, as well as nature.
- Basic Image Processing in Python, Part 2 - Jul 17, 2018.
We explain how to easily access and manipulate the internal components of digital images using Python and give examples from satellite image processing.
- Basic Image Data Analysis Using Numpy and OpenCV – Part 1 - Jul 10, 2018.
Accessing the internal component of digital images using Python packages becomes more convenient to understand its properties as well as nature.