Today, the world around us is being captured by imaging devices ranging from cell phones and action cameras to microscopes and telescopes. With ever-increasing generation of images, image processing and automatic image analysis are used in a wide range of individual, academic and industry applications.
We are excited to announce Introduction to Image Processing, a free interactive course from Wolfram U, which makes cutting-edge image processing simple with graphical and visual examples that demonstrate how image operations work. It includes 14 video lessons, each lasting 20 minutes or fewer, and 5 short quizzes, as well as a certificate for finishing all course materials. Topics range from how to control brightness and contrast or crop and resize images, to advanced topics including segmentation, image enhancement, feature detection and using machine learning to perform modern image processing—no machine learning knowledge necessary!
December 23, 2019 — Jon McLoone, Director, Technical Communication & Strategy
For many of us, programming represents leisure time just as much as work. Here at Wolfram, we have an incredibly creative group with a wide variety of hobbies, on the screen and off—including textile arts like cross-stitch. So when my colleague Jay suggested that I create a cross-stitch program using the Wolfram Language, I replied with “Challenge accepted!” Jay was looking for a simple way to generate a cross-stitch pattern from a photograph—or really any image—with the colors corresponding to the DMC thread ID numbers. We both knew that the image-processing capabilities of the Wolfram Language would make this an easy task, but incorporating the DMC thread catalog seemed a more interesting challenge. Armed with both computer and (virtual) thread, I set out on my quest to create the perfect cross-stitch pattern generator.
May 2, 2019 — Tuseeta Banerjee, Research Scientist, Machine Learning
If you haven’t used machine learning, deep learning and neural networks yourself, you’ve almost certainly heard of them. You may be familiar with their commercial use in self-driving cars, image recognition, automatic text completion, text translation and other complex data analysis, but you can also train your own neural nets to accomplish tasks like identifying objects in images, generating sequences of text or segmenting pixels of an image. With the Wolfram Language, you can get started with machine learning and neural nets faster than you think. Since deep learning and neural networks are everywhere, let’s go ahead and explore what exactly they are and how you can start using them.
January 12, 2018 — Jesse Dohmann, Technical Documentation Writer, Document & Media Systems
With the images from the Juno mission being made available to the public, I thought it might be fun to try my hand at some image processing with them. Though my background is not in image processing, the Wolfram Language has some really nice tools that lessen the learning curve, so you can focus on what you want to do vs. how to do it.
Microscopes were invented almost four hundred years ago. But today, there’s a revolution in microscopy (as in so many other fields) associated with computation. We’ve been working hard to make the Wolfram Language a definitive platform for the emerging field of computational microscopy.
It all starts with getting an image of some kind—whether from a light or x-ray microscope, transmission electron microscope (TEM), confocal laser scanning microscope (CLSM), two-photon excitation or a scanning electron microscope (SEM), as well as many more. You can then proceed to enhance images, reconstruct objects and perform measurements, detection, recognition and classification. At last month’s Microscopy & Microanalysis conference, we showed various examples of this pipeline, starting with a Zeiss microscope and a ToupTek digital camera.
March 10, 2017 — Jeffrey Bryant, Research Programmer, Wolfram|Alpha Scientific Content
In Mathematica 10, we introduced support for anatomical structures in EntityValue, which included, among many other things, a “Graphics3D” property that returns a 3D model of the anatomical structure in question. We also styled the models and aligned them with the concepts in the Unified Medical Language System (UMLS).
February 23, 2017 — Michael Trott, Chief Scientist, Wolfram|Alpha Scientific Content
And How Many Animals, Animal Heads, Human Faces, Aliens and Ghosts in Their 2D Projections?
In my recent Wolfram Community post, “How many animals can one find in a random image?,” I looked into the pareidolia phenomenon from the viewpoints of pixel clusters in random (2D) black-and-white images. Here are some of the shapes I found, extracted, rotated, smoothed and colored from the connected black pixel clusters of a single 800×800 image of randomly chosen, uncorrelated black-and-white pixels.
January 31, 2017 — Michael Gammon, Blog Administrator, Document and Media Systems
If aliens actually visited Earth, world leaders would bring in a scientist to develop a process for understanding their language. So when director Denis Villeneuve began working on the science fiction movie Arrival, he and his team turned to real-life computer scientists Stephen and Christopher Wolfram to bring authentic science to the big screen. Christopher specifically was tasked with analyzing and writing code for a fictional nonlinear visual language. On January 31, he demonstrated the development process he went through in a livecoding event you can watch on YouTube.
December 2, 2016 — Etienne Bernard, Lead Architect, Machine Learning
Two years ago, we introduced the first high-level machine learning functions of the Wolfram Language, Classify and Predict. Since then, we have been creating a set of automatic machine learning functionalities (ClusterClassify, DimensionReduction, etc.). Today, I am happy to present a new function called FeatureExtraction that deals with another important machine learning task: extracting features from data. Unlike Classify and Predict, which follow the supervised learning paradigm, FeatureExtraction belongs to the unsupervised learning paradigm, meaning that the data to learn from is given as a set of unlabeled examples (i.e. without an input -> output relation). The main goal of FeatureExtraction is to transform these examples into numeric vectors (often called feature vectors). For example, let’s apply FeatureExtraction to a simple dataset:
August 2, 2016 — Zach Littrell, Technical Content Writer, Technical Communications and Strategy Group
Happy National Coloring Book Day! When my coworkers suggested that I write a blog post celebrating this colorful occasion, I was, frankly, tickled pink by the idea. Coloring is a fun, therapeutic activity for anyone of any age who can color inside the lines—or occasionally just a little outside, if they’re more like me. And as the newest member of the Wolfram Blog team, I wanted to see in what fun ways I could add a little color to the Wolfram Blog.
While looking through Wolfram|Alpha’s massive collection of popular curves, from Pokémon to ALF to Stephen Wolfram, I realized that all of the images built into the Wolfram Knowledgebase would be great for coloring. So, I figured, why not make my own Wolfram coloring book in Mathematica? Carpe colores!
Each of the popular curves in the Knowledgebase can be accessed as an Entity in the Wolfram Language and comes with a wide variety of properties, including their parametric equations. But there’s no need to plot them yourself—they also conveniently come with an "Image" property already included: